Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
Elements of limited time intervals, contributing to particular results or situations.
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
Social and economic factors that characterize the individual or group within the social structure.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
Disappearance of a neoplasm or neoplastic state without the intervention of therapy.
An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)
Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
Inhaling and exhaling the smoke of burning TOBACCO.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.
An infant during the first month after birth.
A country spanning from central Asia to the Pacific Ocean.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
Educational attainment or level of education of individuals.
A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).
Country located in EUROPE. It is bordered by the NORTH SEA, BELGIUM, and GERMANY. Constituent areas are Aruba, Curacao, Sint Maarten, formerly included in the NETHERLANDS ANTILLES.
The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.
Individuals whose ancestral origins are in the continent of Europe.
A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.
The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
The worsening of a disease over time. This concept is most often used for chronic and incurable diseases where the stage of the disease is an important determinant of therapy and prognosis.
A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.
The proportion of survivors in a group, e.g., of patients, studied and followed over a period, or the proportion of persons in a specified group alive at the beginning of a time interval who survive to the end of the interval. It is often studied using life table methods.
Individuals whose ancestral origins are in the southeastern and eastern areas of the Asian continent.
Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.
Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
The number of males and females in a given population. The distribution may refer to how many men or women or what proportion of either in the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
A nonparametric method of compiling LIFE TABLES or survival tables. It combines calculated probabilities of survival and estimates to allow for observations occurring beyond a measurement threshold, which are assumed to occur randomly. Time intervals are defined as ending each time an event occurs and are therefore unequal. (From Last, A Dictionary of Epidemiology, 1995)
The frequency of different ages or age groups in a given population. The distribution may refer to either how many or what proportion of the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
Persons living in the United States having origins in any of the black groups of Africa.
Statistical interpretation and description of a population with reference to distribution, composition, or structure.
Behaviors associated with the ingesting of alcoholic beverages, including social drinking.
A generic concept reflecting concern with the modification and enhancement of life attributes, e.g., physical, political, moral and social environment; the overall condition of a human life.
The technique that deals with the measurement of the size, weight, and proportions of the human or other primate body.
The inhabitants of a city or town, including metropolitan areas and suburban areas.
A subclass of DIABETES MELLITUS that is not INSULIN-responsive or dependent (NIDDM). It is characterized initially by INSULIN RESISTANCE and HYPERINSULINEMIA; and eventually by GLUCOSE INTOLERANCE; HYPERGLYCEMIA; and overt diabetes. Type II diabetes mellitus is no longer considered a disease exclusively found in adults. Patients seldom develop KETOSIS but often exhibit OBESITY.
The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.
The mass or quantity of heaviness of an individual. It is expressed by units of pounds or kilograms.
The gradual irreversible changes in structure and function of an organism that occur as a result of the passage of time.
Diseases which have one or more of the following characteristics: they are permanent, leave residual disability, are caused by nonreversible pathological alteration, require special training of the patient for rehabilitation, or may be expected to require a long period of supervision, observation, or care. (Dictionary of Health Services Management, 2d ed)
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
Regular course of eating and drinking adopted by a person or animal.
A stratum of people with similar position and prestige; includes social stratification. Social class is measured by criteria such as education, occupation, and income.
Pathological conditions involving the CARDIOVASCULAR SYSTEM including the HEART; the BLOOD VESSELS; or the PERICARDIUM.
A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
Stress wherein emotional factors predominate.
Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.
The inhabitants of rural areas or of small towns classified as rural.
The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.
A plasma protein that circulates in increased amounts during inflammation and after tissue damage.
The age of the conceptus, beginning from the time of FERTILIZATION. In clinical obstetrics, the gestational age is often estimated as the time from the last day of the last MENSTRUATION which is about 2 weeks before OVULATION and fertilization.
A return to earlier, especially to infantile, patterns of thought or behavior, or stage of functioning, e.g., feelings of helplessness and dependency in a patient with a serious physical illness. (From APA, Thesaurus of Psychological Index Terms, 1994).
A group of pathological conditions characterized by sudden, non-convulsive loss of neurological function due to BRAIN ISCHEMIA or INTRACRANIAL HEMORRHAGES. Stroke is classified by the type of tissue NECROSIS, such as the anatomic location, vasculature involved, etiology, age of the affected individual, and hemorrhagic vs. non-hemorrhagic nature. (From Adams et al., Principles of Neurology, 6th ed, pp777-810)
The capital is Seoul. The country, established September 9, 1948, is located on the southern part of the Korean Peninsula. Its northern border is shared with the Democratic People's Republic of Korea.
A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Tumors or cancer of the human BREAST.
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
Pathological processes of CORONARY ARTERIES that may derive from a congenital abnormality, atherosclerotic, or non-atherosclerotic cause.
Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)
The confinement of a patient in a hospital.
The return of a sign, symptom, or disease after a remission.
The study of chance processes or the relative frequency characterizing a chance process.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
The mass or quantity of heaviness of an individual at BIRTH. It is expressed by units of pounds or kilograms.
Knowledge, attitudes, and associated behaviors which pertain to health-related topics such as PATHOLOGIC PROCESSES or diseases, their prevention, and treatment. This term refers to non-health workers and health workers (HEALTH PERSONNEL).
Persons living in the United States of Mexican (MEXICAN AMERICANS), Puerto Rican, Cuban, Central or South American, or other Spanish culture or origin. The concept does not include Brazilian Americans or Portuguese Americans.
Glucose in blood.
Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).
Pathologic processes that affect patients after a surgical procedure. They may or may not be related to the disease for which the surgery was done, and they may or may not be direct results of the surgery.
The end-stage of CHRONIC RENAL INSUFFICIENCY. It is characterized by the severe irreversible kidney damage (as measured by the level of PROTEINURIA) and the reduction in GLOMERULAR FILTRATION RATE to less than 15 ml per min (Kidney Foundation: Kidney Disease Outcome Quality Initiative, 2002). These patients generally require HEMODIALYSIS or KIDNEY TRANSPLANTATION.
Therapy for the insufficient cleansing of the BLOOD by the kidneys based on dialysis and including hemodialysis, PERITONEAL DIALYSIS, and HEMODIAFILTRATION.
A heterogeneous group of disorders characterized by HYPERGLYCEMIA and GLUCOSE INTOLERANCE.
The performance of the basic activities of self care, such as dressing, ambulation, or eating.
A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.
Diseases caused by factors involved in one's employment.
The state of being engaged in an activity or service for wages or salary.
Method for obtaining information through verbal responses, written or oral, from subjects.
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology.
Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.
Those characteristics that distinguish one SEX from the other. The primary sex characteristics are the OVARIES and TESTES and their related hormones. Secondary sex characteristics are those which are masculine or feminine but not directly related to reproduction.
Individuals whose ancestral origins are in the continent of Africa.
The measurement of the health status for a given population using a variety of indices, including morbidity, mortality, and available health resources.
The relative amounts of various components in the body, such as percentage of body fat.
Support systems that provide assistance and encouragement to individuals with physical or emotional disabilities in order that they may better cope. Informal social support is usually provided by friends, relatives, or peers, while formal assistance is provided by churches, groups, etc.
Behaviors expressed by individuals to protect, maintain or promote their health status. For example, proper diet, and appropriate exercise are activities perceived to influence health status. Life style is closely associated with health behavior and factors influencing life style are socioeconomic, educational, and cultural.
Research aimed at assessing the quality and effectiveness of health care as measured by the attainment of a specified end result or outcome. Measures include parameters such as improved health, lowered morbidity or mortality, and improvement of abnormal states (such as elevated blood pressure).
The exposure to potentially harmful chemical, physical, or biological agents in the environment or to environmental factors that may include ionizing radiation, pathogenic organisms, or toxic chemicals.
Tomography using x-ray transmission and a computer algorithm to reconstruct the image.
The distance from the sole to the crown of the head with body standing on a flat surface and fully extended.
Revenues or receipts accruing from business enterprise, labor, or invested capital.
Parliamentary democracy located between France on the northeast and Portugual on the west and bordered by the Atlantic Ocean and the Mediterranean Sea.
NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION).
Female parents, human or animal.
Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.
Physical activity which is usually regular and done with the intention of improving or maintaining PHYSICAL FITNESS or HEALTH. Contrast with PHYSICAL EXERTION which is concerned largely with the physiologic and metabolic response to energy expenditure.
The period of confinement of a patient to a hospital or other health facility.
Individuals enrolled in a school or formal educational program.
A status with BODY WEIGHT that is above certain standard of acceptable or desirable weight. In the scale of BODY MASS INDEX, overweight is defined as having a BMI of 25.0-29.9 kg/m2. Overweight may or may not be due to increases in body fat (ADIPOSE TISSUE), hence overweight does not equal "over fat".
Divisions of the year according to some regularly recurrent phenomena usually astronomical or climatic. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
Tests designed to assess neurological function associated with certain behaviors. They are used in diagnosing brain dysfunction or damage and central nervous system disorders or injury.
A state of harmony between internal needs and external demands and the processes used in achieving this condition. (From APA Thesaurus of Psychological Index Terms, 8th ed)
Disturbances in mental processes related to learning, thinking, reasoning, and judgment.
The number of offspring a female has borne. It is contrasted with GRAVIDITY, which refers to the number of pregnancies, regardless of outcome.
A situation in which the level of living of an individual, family, or group is below the standard of the community. It is often related to a specific income level.
New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.
An imbalance between myocardial functional requirements and the capacity of the CORONARY VESSELS to supply sufficient blood flow. It is a form of MYOCARDIAL ISCHEMIA (insufficient blood supply to the heart muscle) caused by a decreased capacity of the coronary vessels.
The physical activity of a human or an animal as a behavioral phenomenon.
The status of health in urban populations.
All deaths reported in a given population.
Ongoing scrutiny of a population (general population, study population, target population, etc.), generally using methods distinguished by their practicability, uniformity, and frequently their rapidity, rather than by complete accuracy.
Disorders related to substance abuse.
A person's view of himself.
The age of the mother in PREGNANCY.
Cholesterol which is contained in or bound to high-density lipoproteins (HDL), including CHOLESTEROL ESTERS and free cholesterol.
Size and composition of the family.
Determination of the degree of a physical, mental, or emotional handicap. The diagnosis is applied to legal qualification for benefits and income under disability insurance and to eligibility for Social Security and workmen's compensation benefits.
A spontaneous diminution or abatement of a disease over time, without formal treatment.
Persons functioning as natural, adoptive, or substitute parents. The heading includes the concept of parenthood as well as preparation for becoming a parent.
Disease having a short and relatively severe course.
Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.
Conversations with an individual or individuals held in order to obtain information about their background and other personal biographical data, their attitudes and opinions, etc. It includes school admission or job interviews.
A demographic parameter indicating a person's status with respect to marriage, divorce, widowhood, singleness, etc.
The volume of water filtered out of plasma through glomerular capillary walls into Bowman's capsules per unit of time. It is considered to be equivalent to INULIN clearance.
A statistical means of summarizing information from a series of measurements on one individual. It is frequently used in clinical pharmacology where the AUC from serum levels can be interpreted as the total uptake of whatever has been administered. As a plot of the concentration of a drug against time, after a single dose of medicine, producing a standard shape curve, it is a means of comparing the bioavailability of the same drug made by different companies. (From Winslade, Dictionary of Clinical Research, 1992)
Feeling or emotion of dread, apprehension, and impending disaster but not disabling as with ANXIETY DISORDERS.
Standardized procedures utilizing rating scales or interview schedules carried out by health personnel for evaluating the degree of mental illness.
The measurement around the body at the level of the ABDOMEN and just above the hip bone. The measurement is usually taken immediately after exhalation.
Minor hemoglobin components of human erythrocytes designated A1a, A1b, and A1c. Hemoglobin A1c is most important since its sugar moiety is glucose covalently bound to the terminal amino acid of the beta chain. Since normal glycohemoglobin concentrations exclude marked blood glucose fluctuations over the preceding three to four weeks, the concentration of glycosylated hemoglobin A is a more reliable index of the blood sugar average over a long period of time.
Crafts, trades, professions, or other means of earning a living.
Place or physical location of work or employment.
Factors which produce cessation of all vital bodily functions. They can be analyzed from an epidemiologic viewpoint.
Any observable response or action of an adolescent.
Public attitudes toward health, disease, and the medical care system.
The largest country in North America, comprising 10 provinces and three territories. Its capital is Ottawa.
The seeking and acceptance by patients of health service.
A cluster of metabolic risk factors for CARDIOVASCULAR DISEASES and TYPE 2 DIABETES MELLITUS. The major components of metabolic syndrome X include excess ABDOMINAL FAT; atherogenic DYSLIPIDEMIA; HYPERTENSION; HYPERGLYCEMIA; INSULIN RESISTANCE; a proinflammatory state; and a prothrombotic (THROMBOSIS) state. (from AHA/NHLBI/ADA Conference Proceedings, Circulation 2004; 109:551-556)
Measurements of the height, weight, length, area, etc., of the human and animal body or its parts.
The degree to which individuals are inhibited or facilitated in their ability to gain entry to and to receive care and services from the health care system. Factors influencing this ability include geographic, architectural, transportational, and financial considerations, among others.
The use of statistical and mathematical methods to analyze biological observations and phenomena.
State of the body in relation to the consumption and utilization of nutrients.
Tumors or cancer of the LUNG.
A generic term for fats and lipoids, the alcohol-ether-soluble constituents of protoplasm, which are insoluble in water. They comprise the fats, fatty oils, essential oils, waxes, phospholipids, glycolipids, sulfolipids, aminolipids, chromolipids (lipochromes), and fatty acids. (Grant & Hackh's Chemical Dictionary, 5th ed)
Diminished effectiveness of INSULIN in lowering blood sugar levels: requiring the use of 200 units or more of insulin per day to prevent HYPERGLYCEMIA or KETOSIS.
Statistical measures of utilization and other aspects of the provision of health care services including hospitalization and ambulatory care.
Groups of individuals whose putative ancestry is from native continental populations based on similarities in physical appearance.
The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.
Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.
Results of conception and ensuing pregnancy, including LIVE BIRTH; STILLBIRTH; SPONTANEOUS ABORTION; INDUCED ABORTION. The outcome may follow natural or artificial insemination or any of the various ASSISTED REPRODUCTIVE TECHNIQUES, such as EMBRYO TRANSFER or FERTILIZATION IN VITRO.
Radiography of the vascular system of the heart muscle after injection of a contrast medium.

Surgery-related factors and local recurrence of Wilms tumor in National Wilms Tumor Study 4. (1/22207)

OBJECTIVE: To assess the prognostic factors for local recurrence in Wilms tumor. SUMMARY BACKGROUND DATA: Current therapy for Wilms tumor has evolved through four studies of the National Wilms Tumor Study Group. As adverse prognostic factors were identified, treatment of children with Wilms tumor has been tailored based on these factors. Two-year relapse-free survival of children in the fourth study (NWTS-4) exceeded 91%. Factors once of prognostic import for local recurrence may lose their significance as more effective therapeutic regimens are devised. METHODS: Children evaluated were drawn from the records of NWTS-4. A total of 2482 randomized or followed patients were identified. Local recurrence, defined as recurrence in the original tumor bed, retroperitoneum, or within the abdominal cavity or pelvis, occurred in 100 children. Using a nested case-control study design, 182 matched controls were selected. Factors were analyzed for their association with local failure. Relative risks and 95% confidence intervals were calculated, taking into account the matching. RESULTS: The largest relative risks for local recurrence were observed in patients with stage III disease, those with unfavorable histology (especially diffuse anaplasia), and those reported to have tumor spillage during surgery. Multiple regression analysis adjusting for the combined effects of histology, lymph node involvement, and age revealed that tumor spillage remained significant. The relative risk of local recurrence from spill was largest in children with stage II disease. The absence of lymph node biopsy was also associated with an increased relative risk of recurrence, which was largest in children with stage I disease. The survival of children after local recurrence is poor, with an average survival rate at 2 years after relapse of 43%. Survival was dependent on initial stage: those who received more therapy before relapse had a worse prognosis. CONCLUSIONS: This study has demonstrated that surgical rupture of the tumor must be prevented by the surgeon, because spills produce an increased risk of local relapse. Both local and diffuse spills produce this risk. Stage II children with local spill appear to require more aggressive therapy than that used in NWTS-4. The continued critical importance of lymph node sampling in conjunction with nephrectomy for Wilms tumor is also established. Absence of lymph node biopsy may result in understaging and inadequate treatment of the child and may produce an increased risk of local recurrence.  (+info)

Prolonged mating in prairie voles (Microtus ochrogaster) increases likelihood of ovulation and embryo number. (2/22207)

Prairie voles are induced ovulators that mate frequently in brief bouts over a period of approximately 24 h. We examined 1) impact of mating duration on ovulation and embryo number, 2) incidence of fertilization, 3) temporal pattern of embryo development, 4) embryo progression through the reproductive tract over time, and 5) embryo development in culture. Mating was videotaped to determine first copulation, and the ovaries were examined and the reproductive tracts flushed at 6, 8, 10, 12, 16, 20, and 24 h and 2, 3, and 4 days after first copulation. The number of mature follicles and fresh corpora lutea and the number and developmental stage of embryos were quantified. One, two-, and four-cell embryos were cultured in Whitten's medium. Mature follicles were present at the earliest time examined (6 h). Thirty-eight percent of females that had been paired for < 12 h after the first copulation ovulated, whereas all females paired >/= 12 h after the first copulation ovulated. Virtually all (> 99%) oocytes recovered from females paired for >/= 12 h after first copulation were fertilized. Pairing time after first copulation and mean copulation-bout duration were significant (p < 0.05) determinants of embryo number. Embryos entered the uterine horns and implanted on Days 3 and 4, respectively, after first copulation (Day 0). Embryos cultured in vitro underwent approximately one cell division per day, a rate similar to that in vivo. We conclude that prairie voles ovulate reliably after pairing for >/= 12 h, although some females showed exceptional sensitivity not predicted by the variables quantified. Prolonged mating for longer than 12 h increased the total embryos produced. This mechanism likely has adaptive significance for increasing offspring number.  (+info)

Geographic, demographic, and socioeconomic variations in the investigation and management of coronary heart disease in Scotland. (3/22207)

OBJECTIVE: To determine whether age, sex, level of deprivation, and area of residence affect the likelihood of investigation and treatment of patients with coronary heart disease. DESIGN, PATIENTS, AND INTERVENTIONS: Routine discharge data were used to identify patients admitted with acute myocardial infarction (AMI) between 1991 and 1993 inclusive. Record linkage provided the proportion undergoing angiography, percutaneous transluminal coronary angioplasty (PTCA), and coronary artery bypass grafting (CABG) over the following two years. Multiple logistic regression analysis was used to determine whether age, sex, deprivation, and area of residence were independently associated with progression to investigation and revascularisation. SETTING: Mainland Scotland 1991 to 1995 inclusive. MAIN OUTCOME MEASURES: Two year incidence of angiography, PTCA, and CABG. Results-36 838 patients were admitted with AMI. 4831 (13%) underwent angiography, 587 (2%) PTCA, and 1825 (5%) CABG. Women were significantly less likely to undergo angiography (p < 0.001) and CABG (p < 0.001) but more likely to undergo PTCA (p < 0.05). Older patients were less likely to undergo all three procedures (p < 0.001). Socioeconomic deprivation was associated with a reduced likelihood of both angiography and CABG (p < 0.001). There were significant geographic variations in all three modalities (p < 0.001). CONCLUSION: Variations in investigation and management were demonstrated by age, sex, geography, and socioeconomic deprivation. These are unlikely to be accounted for by differences in need; differences in clinical practice are, therefore, likely.  (+info)

Regional patterns of myocardial sympathetic denervation in dilated cardiomyopathy: an analysis using carbon-11 hydroxyephedrine and positron emission tomography. (4/22207)

OBJECTIVE: To assess presynaptic function of cardiac autonomic innervation in patients with advanced congestive heart failure using positron emission tomography (PET) and the recently developed radiolabelled catecholamine analogue carbon-11 hydroxyephedrine (HED) as a marker for neuronal catecholamine uptake function. DESIGN AND PATIENTS: 29 patients suffering from dilated cardiomyopathy with moderate to severe heart failure were compared with eight healthy controls. Perfusion scan was followed by HED dynamic PET imaging of cardiac sympathetic innervation. The scintigraphic results were compared with markers of disease severity and the degree of sympathetic dysfunction assessed by means of heart rate variability. RESULTS: In contrast to nearly normal perfusions, mean (SD) HED retention in dilated cardiomyopathy patients was abnormal in 64 (32)% of the left ventricle. Absolute myocardial HED retention was 10.7 (1.0)%/min in controls v 6.2 (1.6)%/min in dilated cardiomyopathy patients (p < 0.001). Moreover, significant regional reduction of HED retention was demonstrated in apical and inferoapical segments. HED retention was significantly correlated with New York Heart Association functional class (r = -0.55, p = 0. 002) and ejection fraction (r = 0.63, p < 0.001), but not, however, with plasma noradrenaline concentrations as well as parameters of heart rate variability. CONCLUSIONS: In this study, using PET in combination with HED in patients with dilated cardiomyopathy, not only global reduction but also regional abnormalities of cardiac sympathetic tracer uptake were demonstrated. The degree of abnormality was positively correlated to markers of severity of heart failure. The pathogenetic mechanisms leading to the regional differences of neuronal damage as well as the prognostic significance of these findings remain to be defined.  (+info)

QT dispersion in patients with chronic heart failure: beta blockers are associated with a reduction in QT dispersion. (5/22207)

OBJECTIVE: To compare QT dispersion in patients with impaired left ventricular systolic function and in matched control patients with normal left ventricular systolic function. DESIGN: A retrospective, case-control study with controls matched 4:1 for age, sex, previous myocardial infarction, and diuretic and beta blocker treatment. SETTING: A regional cardiology centre and a university teaching hospital. PATIENTS: 25 patients with impaired left ventricular systolic function and 100 patients with normal left ventricular systolic function. MAIN OUTCOME MEASURES: QT and QTc dispersion measured by three methods: the difference between maximum and minimum QT and QTc intervals, the standard deviation of QT and QTc intervals, and the "lead adjusted" QT and QTc dispersion. RESULTS: All measures of QT/QTc dispersion were closely interrelated (r values 0.86 to 0.99; all p < 0.001). All measures of QT and QTc dispersion were significantly increased in the patients with impaired left ventricular systolic function v controls (p < 0.001): 71.9 (6.5) (mean (SEM)) v 46.9 (1.7) ms for QT dispersion, and 83.6 (7.6) v 54.3 (2.1) ms(-1-2) for QTc dispersion. All six dispersion parameters were reduced in patients taking beta blockers (p < 0.05), regardless of whether left ventricular function was normal or impaired-by 9.4 (4.6) ms for QT dispersion (p < 0.05) and by 13.8 (6. 5) ms(-1-2) for QTc dispersion (p = 0.01). CONCLUSIONS: QT and QTc dispersion are increased in patients with systolic heart failure in comparison with matched controls, regardless of the method of measurement and independently of possible confounding factors. beta Blockers are associated with a reduction in both QT and QTc dispersion, raising the possibility that a reduction in dispersion of ventricular repolarisation may be an important antiarrhythmic mechanism of beta blockade.  (+info)

Early death during chemotherapy in patients with small-cell lung cancer: derivation of a prognostic index for toxic death and progression. (6/22207)

Based on an increased frequency of early death (death within the first treatment cycle) in our two latest randomized trials of combination chemotherapy in small-cell lung cancer (SCLC), we wanted to identify patients at risk of early non-toxic death (ENTD) and early toxic death (ETD). Data were stored in a database and logistic regression analyses were performed to identify predictive factors for early death. During the first cycle, 118 out of 937 patients (12.6%) died. In 38 patients (4%), the cause of death was sepsis. Significant risk factors were age, performance status (PS), lactate dehydrogenase (LDH) and treatment with epipodophyllotoxins and platinum in the first cycle (EP). Risk factors for ENTD were age, PS and LDH. Extensive stage had a hazard ratio of 1.9 (P = 0.07). Risk factors for ETD were EP, PS and LDH, whereas age and stage were not. For EP, the hazard ratio was as high as 6.7 (P = 0.0001). We introduced a simple prognostic algorithm including performance status, LDH and age. Using a prognostic algorithm to exclude poor-risk patients from trials, we could minimize early death, improve long-term survival and increase the survival differences between different regimens. We suggest that other groups evaluate our algorithm and exclude poor prognosis patients from trials of dose intensification.  (+info)

Microvascular function relates to insulin sensitivity and blood pressure in normal subjects. (7/22207)

BACKGROUND: A strong but presently unexplained inverse association between blood pressure and insulin sensitivity has been reported. Microvascular vasodilator capacity may be a common antecedent linking insulin sensitivity to blood pressure. To test this hypothesis, we studied 18 normotensive and glucose-tolerant subjects showing a wide range in insulin sensitivity as assessed with the hyperinsulinemic, euglycemic clamp technique. METHODS AND RESULTS: Blood pressure was measured by 24-hour ambulatory blood pressure monitoring. Videomicroscopy was used to measure skin capillary density and capillary recruitment after arterial occlusion. Skin blood flow responses after iontophoresis of acetylcholine and sodium nitroprusside were evaluated by laser Doppler flowmetry. Insulin sensitivity correlated with 24-hour systolic blood pressure (24-hour SBP; r=-0.50, P<0.05). Capillary recruitment and acetylcholine-mediated vasodilatation were strongly and positively related to insulin sensitivity (r=0.84, P<0.001; r=0.78, P<0.001, respectively), and capillary recruitment was inversely related to 24-hour SBP (r=-0.53, P<0.05). Waist-to-hip ratio showed strong associations with insulin sensitivity, blood pressure, and the measures of microvascular function but did not confound the associations between these variables. Subsequent regression analysis showed that the association between insulin sensitivity and blood pressure was not independent of the estimates of microvascular function, and part of the variation in both blood pressure (R2=38%) and insulin sensitivity (R2=71%) could be explained by microvascular function. CONCLUSIONS: Insulin sensitivity and blood pressure are associated well within the physiological range. Microvascular function strongly relates to both, consistent with a central role in linking these variables.  (+info)

Modeling breathing-zone concentrations of airborne contaminants generated during compressed air spray painting. (8/22207)

This paper presents a mathematical model to predict breathing-zone concentrations of airborne contaminants generated during compressed air spray painting in cross-flow ventilated booths. The model focuses on characterizing the generation and transport of overspray mist. It extends previous work on conventional spray guns to include exposures generated by HVLP guns. Dimensional analysis and scale model wind-tunnel studies are employed using non-volatile oils, instead of paint, to produce empirical equations for estimating exposure to total mass. Results indicate that a dimensionless breathing zone concentration is a nonlinear function of the ratio of momentum flux of air from the spray gun to the momentum flux of air passing through the projected area of the worker's body. The orientation of the spraying operation within the booth is also very significant. The exposure model requires an estimate of the contaminant generation rate, which is approximated by a simple impactor model. The results represent an initial step in the construction of more realistic models capable of predicting exposure as a mathematical function of the governing parameters.  (+info)

Learn Simple Regression Analysis in Public Health from 约翰霍普金斯大学. Biostatistics is the application of statistical reasoning to the life sciences, and its the key to unlocking the data gathered by researchers and the evidence presented in the ...
When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation
Weight of evidence: The bioconcentration factor (BCF) of the main components are available from EpiSuite calculation: L-alpha terpineol: EPI-Suite, BCFBAF v3.01. The estimated BCF is 67.8 L/kg wet-wt (log BCF from regression-based method = 1.83) D-alpha terpineol: EPI-Suite, BCFBAF v3.01. The estimated BCF is 67.8 L/kg wet-wt (log BCF from regression-based method = 1.83) Terpinolene: EPI-Suite, BCFBAF v3.01. The estimated BCF is 413.3 L/kg wet-wt (log BCF from regression-based method = 2.616). Gamma terpineol: EPI-Suite, BCFBAF v3.01. The estimated BCF is 89.3 L/kg wet-wt (log BCF from regression-based method = 1.95) Cineole: EPI-Suite, BCFBAF v3.01. The estimated BCF is 29.8 L/kg wet-wt (log BCF from regression-based method = 1.47) Isocineole: EPI-Suite, BCFBAF v3.01. The estimated BCF is 42.3 L/kg wet-wt (log BCF from regression-based method = 1.63) L-Limonene: EPI-Suite, BCFBAF v3.01. The estimated BCF is 360.5 L/kg wet-wt (log BCF from regression-based method = 2.557) D-Limonene: EPI-Suite, ...
Multiple Regression Analysis Excel Template multiple regression analysis excel template excel multiple regression ideas. multiple regression analysis excel template linear regression analysis in excel template. multiple regression analysis excel template linear regression analysis in excel ideas. Multiple Regression Analysis Excel Template multiple regression analysis excel template multiple regression analysis excel real statistics using excel template. Multiple Regression Analysis Excel Template ...
TY - JOUR. T1 - The price is right!? A meta-regression analysis on willingness to pay for local food. AU - Printezis, Iryna. AU - Grebitus, Carola. AU - Hirsch, Stefan. PY - 2019/5/1. Y1 - 2019/5/1. N2 - We study the literature on willingness to pay (WTP) for local food by applying meta-regression analysis to a set of 35 eligible research papers that provide 86 estimates on consumers WTP for the attribute local. An analysis of the distribution of WTP measures suggests the presence of publication selection bias that favors larger and statistically significant results. The analyzed literature provides evidence for statistically significant differences among consumers WTP for various types of product. Moreover, we find that the methodological approach (choice experiments vs. other approaches) and the analyzed country can have a significant influence on the generated WTP for local.. AB - We study the literature on willingness to pay (WTP) for local food by applying meta-regression analysis to a ...
Downloadable! The objective of this study is to estimate the impact of natural amenity on farmland values in the contiguous United States using a quantile regression approach and data from the 2006, 2007, and 2008 Agricultural Resource Management Surveys. The contribution of this study is three-fold. First, we explicitly include variables representing natural amenity and soil characteristics of farmland. Second, we employ a quantile regression approach to examine potentially heterogeneous impacts of natural amenity and soil characteristics at different quantiles of farmland values. Third, we utilized data from a nationwide survey of farm household to examine findings in studies using regional data are consistent at a national scale. Our quantile regression analysis offers some insightful results. Natural amenity is positively correlated with farmland values and its impact is often more pronounced at a higher price range of farmland.
View Notes - Ch6 regression explanation from MBA 642 at Bellevue. How to perform Simple Regression using Excel 1. Open up excel and verify that you have the data analysis option under the drop down
Nonlinear regression analysis - CurveFitter - download the latest version for Windows XP/Vista/7/8/10 (32-bit and 64-bit). CurveFitter performs statistical regression analysis to estimate the values of parameters. Get Nonlinear regression analysis - CurveFitter old versions and alternatives.
Regression Analysis The basic concept of Regression in Statistics is establishing a cause - effect relationship between two or more variables. The Cause is better referred to as the Independent Variable(s). And the effect is the Dependent Variable. When we regress the dependent variable on the in...
Prediction and forecasting has become very important in modern society. Regression analysis enables to predict easily based on given data. This paper focuses on regression analysis on sparse grids using the existing toolbox Sparse Grid ++ (SG++). The core workload of the regression analysis will be implemented on graphics cards using NVIDIAs Compute Unified Device Architecture (CUDA). Therefore, we give guidance how to get high performance when dealing with this particular problem using CUDA enabled graphics cards. We also focus on problems where the datasets are larger than the available device memory. Finally, we present test results for real-world and artificial datasets ...
Footnotes. a. This is the source of variance, Model, Residual, and Total. The Total variance is partitioned into the variance which can be explained by the independent variables (Model) and the variance which is not explained by the independent variables. Note that the Sums of Squares for the Model and Residual add up to the Total Variance, reflecting the fact that the Total Variance is partitioned into Model and Residual variance.. b. These are the Sum of Squares associated with the three sources of variance, Total, Model & Residual. These can be computed in many ways. Conceptually, these formulas can be expressed as: SSTotal. The total variability around the mean. Σ(Y - Ybar)2. SSResidual. The sum of squared errors in prediction. Σ(Y - Ypredicted)2. SSModel. The improvement in prediction by using the predicted value of Y over just using the mean of Y. Hence, this would be the squared differences between the predicted value of Y and the mean of Y, Σ(Ypredicted - Ybar)2. Another way to think ...
Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test
Al-Hassan, Y. M. (2010). Performance of a new Ridge Regression Estimator. Journal of the Association of Arab Universities for Basic and Applied Sciences, 9(2), pp. 43-50. Drapper, N.R. and Smith, H. (1981). Applied Regression Analysis, Second Edition, New York: John Wiley and Sons. El-Dereny, M. and Rashwan, N. (2011). Solving multicollinearity problem Using Ridge Regression Models. International Journal of Contemporary Mathematical. Sciences, 12, pp. 585 - 600. Fitrianto, A. and Yik, L. C. (2014). Performance of Ridge Regression Estimator Method on Small Sample size By Varying correlation coefficients: A simulation study. Journal of Mathematics and Statistics 10 (1), pp. 25 - 29. Hoerl, A. E. and R. W. Kennard. (1976). Ridge regression: iterative estimation of the biasing parameter. Communication in Statist Theory and Method. 5(1), pp. 77-88. Hoerl, A.E. and R.W. Kennard, Ridge Regression, 1980. Advances, Algorithms and Applications 1981: American Sciences Press. Hoerl, A.E., R.W. Kennard, and ...
Calculates the regression model analysis of the variance (ANOVA) values. Syntax SLR_ANOVA(X, Y, Intercept, Return_type) X is the...
Univariate regression, polynomial regression, orthogonal polynomials, nonlinear - References for Univariate Regression with worked examples
Learn about nonlinear regression analysis in R Programming with the concept of logistic regression, nonlinear regression models, generalized additive models and self-starting functions.
The unit starts with reviewing univariate regression analysis and then extends towards multivariate regression analysis. In the first part of the unit, after analysing simple regression model, inference in multiple regression models and problems of relaxing classical assumptions, i.e., heteroskedasticity and autocorrelation, will be studied. The second part of the unit will analyse nonlinear time series models to track volatility (ARCH, GARCH, ARCH-M, GARCH-M, EGARCH, TARCH, APARCH AND IGARCH models) and panel data analysis including extensions to panel unit root testing and panel cointegration testing.. ...
Quadratic regression models are often constructed based on certain conditions that must be verified for the model to fit the data well, and to be able to predict accurately. This site also presents useful information about the characteristics of the fitted quadratic function.
Download complete research project materials on A REGRESSION ANALYSIS ON THE IMPACT OF SMOKING, LEVEL OF EXERCISE, WEIGHT ON MEDICAL COST. (A CASE STUDY OF FEDERAL MEDICAL CENTER OWERRI) Project Materials Ms Word Documentation Only 50 Pages 1-5 chapters
The third chapter provides a descriptive analysis of the gender wage gap using quantile regression. Many studies have examined the gender wage gap in the United States but this is the first to provide systematic analysis of the gender wage gap using quantile regression over time. Using data from both the March Current Population Survey (CPS) and the Outgoing Rotation Group files of the CPS, I find a narrowing of the gender wage gap over time. Furthermore there is a great deal of heterogeneity across quantiles of the conditional wage distribution of wages by gender. Although the gender pay gap has declined dramatically in recent decades, not all women gained form this change equally ...
Some basic results in probability and statistics. basic regression analysis. Linear regression with one independent variable. Inferences in regression analysis. Aptness of model and remedial measures. Topics in regression analysis - I. General regression and correlation analysis. Matrix appreach to simple regression analysis. Multiple regression. Polymonial regression. Indicator variables. Topics in regression analysis - II. Search for best set of independent variables. Normal correlation models. Basic analysis of variance. Single - factor analysis of variance. Analysis of factor effects. Implementation of ANOVA model. Topics in analysis of variance - I. Multifactor analysis of variance. Two factor analysis of variance. Analysis of two - factor studies. To pics in analysis of variance - II. Multifactor studies. Experimental designs. Completely randomized designs. Analysis of covariance for completely randomized designs. Randomized block designs. Latin square designs.
Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4, pp. 1043-1171). Elsevier. Autor, D. H., Houseman, S. N., & Kerr, S. P. (2017). The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach. Journal of Labor Economics, 35(1), 149-190. Autor, D. H., Katz, L. F., & Kearney, M. S. (2006). The polarization of the US labor market. American economic review, 96(2), 189-194. Blundell, R., Crawford, C., & Jin, W. (2014). What can wages and employment tell us about the UKs productivity puzzle?. The Economic Journal, 124(576), 377-407. Borjas, G. J. (2003). The labor demand curve is downward sloping: Reexamining the impact of immigration on the labor market. The quarterly journal of economics, 118(4), 1335-1374. Buchinsky, M. (1994). Changes in the US wage structure 1963-1987: Application of quantile regression. Econometrica: Journal of the ...
Downloadable! Meta-regression models are increasingly utilized to integrate empirical results across studies while controlling for the potential threats of data-mining and publication bias. We propose extended meta-regression models and evaluate their performance in identifying genuine em- pirical effects by means of a comprehensive simulation study for various scenarios that are prevalent in empirical economics. We can show that the meta-regression models here pro- posed systematically outperform the prior gold standard of meta-regression analysis of re- gression coefficients. Most meta-regression models are robust to the presence of publication bias, but data-mining bias leads to seriously inflated type I errors and has to be addressed explicitly.
NLREG performs linear and nonlinear regression analysis and curve fitting. NLREG can handle linear, polynomial, exponential, logistic, periodic, and general nonlinear functions.
NLREG performs linear and nonlinear regression analysis and curve fitting. NLREG can handle linear, polynomial, exponential, logistic, periodic, and general nonlinear functions.
Aspects of nonlinear regression analysis are discussed and solution techniques by iteration demonstrated via an illustrative example. Analytical and computational details, as well as numerical results, are given.. ...
This paper introduces a specification testing procedure for quantile regression functions consistent in the direction of nonparametric alternatives. We consider test statistics based on a marked empirical process which does not require to estimate nonparametri This paper introduces a specification testing procedure for quantile regression functions consistent in the direction of nonparametric alternatives. We consider test statistics based on a marked empirical process which does not require to estimate nonparametrically the true model. In general, the tests are not distribution free, but critical values can be consistentIy approximated using a residual based bootstrap. A small Monte Cario experiment shows that the test works fairly well in practice. [+] [-] ...
TY - JOUR. T1 - Asymptotic theory in fixed effects panel data seemingly unrelated partially linear regression models. AU - You, Jinhong. AU - Zhou, Xian. PY - 2014/4. Y1 - 2014/4. N2 - This paper deals with statistical inference for the fixed effects panel data seemingly unrelated partially linear regression model. The model naturally extends the traditional fixed effects panel data regression model to allow for semiparametric effects. Multiple regression equations are permitted, and the model includes the aggregated partially linear model as a special case. A weighted profile least squares estimator for the parametric components is proposed and shown to be asymptotically more efficient than those neglecting the contemporaneous correlation. Furthermore, a weighted two-stage estimator for the nonparametric components is also devised and shown to be asymptotically more efficient than those based on individual regression equations. The asymptotic normality is established for estimators of both ...
Birth Weight and Systolic Blood Pressure in Adolescence and Adulthood: Meta-Regression Analysis of Sex- and Age-specific Results from 20 Nordic Studies ...
Probit Regression Analysis in Estimating the Effect of Learning Assisted by Cabri 3D on Students Mathematical Understanding Ability
As predicted, a multiple regression analysis showed that meta-cognitive beliefs and thought fusion beliefs predicted OCD behaviours and symptoms after controlling for worry. However, contrary to predictions, a moderated regression analysis revealed that worry did not moderate the relationship between meta-cognitive beliefs and OCD behaviours and symptoms. As predicted, an analysis demonstrated that worry significantly predicted meta-cognitive beliefs. Additionally, as predicted, a hierarchical multiple regression analysis demonstrated that worry significantly predicted OCD behaviour and symptoms whilst controlling for meta-cognitive beliefs. Finally, thought fusion beliefs predicted OCD behaviours and symptoms whilst controlling for worry. These results are discussed in relation to previous research and theory and suggestions for future directions are made.. ...
Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based inference. As these seldom yield satisfactory results when analyzing binary outcomes from small clusters, estimation within the Structural Equation Modeling (SEM) framework is proposed as an alternative. We compare the performance of R-packages for random-intercept probit regression relying on: the Laplace approximation, adaptive Gaussian quadrature (AGQ), penalized quasi-likelihood, an MCMC-implementation, and integrated nested Laplace approximation within the GLMM-framework, and a robust diagonally weighted least squares estimation within the SEM-framework. In terms of bias for the fixed and random effect estimators, SEM usually performs best for cluster size two, while AGQ prevails in terms of precision (mainly because of SEMs robust standard errors). As
I ran a binary logistic of Y on three different numerical variables A,B,C respectively. I am having an issue of separation of variables with all of them, meaning that there are values Ao,Bo, Co for each of A,B,C (different values for each, of course) so that for ## A,Ao, B,Bo, C,Co ## all the responses are successes (I guess this forces the slope to diverge to minus infinity for the slope of the curve to accommodate the abrupt change of 1 to 0). Then I increased the success levels to three: high, medium and low, to use an ordinal regression . But now I have a significant lack of fit, with p --,0 on the Chi-squared test. How does one interpret lack-of-fit issues with a Logistic Regression? I know that a lack of fit in a simple linear means that data is not linear but what does it mean for a Logistic? Does it mean the (log of) the data is not distributed like an S-curve ExpL/(1+ExpL) (##L ...
Interpret a correlation matrix. Know how to generate a regression equation. Understand average prediction error (residual difference).. Use a multiple regression model to predict a criterion* variable. Determine whether there is a relationship between the criterion* variable and the predictor** variables using in the regression model. Determine which predictor** variables make a significant contribution to the regression model. Interpret the coefficient of multiple determination. Interpret the partial regression coefficients (beta weights).. Understand how categorical predictor** variables can be included in the regression model. Understand regression models that include interaction terms. Recognize when multicollinearity is a problem and how it affects your regression model. Know when to use logistic regression to predict a criterion* variable. * Criterion variable is analogous with dependent variable, but is generally referred to as a criterion in correlational analyses. .** Predictor variable ... Meta-Regression Analysis in Economics and Business (Routledge Advances in Research Methods) (9780415670784) by T.D. Stanley; Hristos Doucouliagos and a great selection of similar New, Used and Collectible Books available now at great prices.
Stanley, T.D. 2013, Does economics add up? An introduction to meta-regression analysis, European journal of economics and economic policies: intervention, vol. 10, no. 2, pp. 207-220, doi: 10.4337/ejeep.2013.02.05. ...
Using Excel as your processing tool, work through three simple regression analyses. First run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the INTRINSIC.
See attached data file. Prepare a report using Excel as your processing tool to process 3 simple regression analyses. Create a graph with the trendline displayed for each of the 3 different regressions. First run a regression.
TY - JOUR. T1 - Health care and patient-reported outcomes. T2 - Results of the cross-national Diabetes Attitudes, Wishes and Needs (DAWN) study. AU - Rubin, Richard R.. AU - Peyrot, Mark. AU - Siminerio, Linda M.. PY - 2006. Y1 - 2006. N2 - OBJECTIVE - The purpose of this study was to assess the relationship of patients self-reported well-being, self-management, and diabetes control with factors related to the patients health care. RESEARCH DESIGN AND METHODS - This was a cross-sectional survey of national samples of patients with diabetes (n = 5,104) from the multinational study of Diabetes Attitudes, Wishes and Needs (DAWN). Patients from 13 countries in Asia, Australia, Europe, and North America reported their level of well-being, self-management, and diabetes control. Hierarchical multiple regression analysis (blocks are countries, respondent characteristics, and health care features) was used to examine predictors of diabetes-related distress and general well-being, adherence to lifestyle ...
Adapted from the work of Kahana and colleagues (e.g., Kahana, 1996), we present two measures of order of recall in neuropsychological free recall tests. These are the position on the study list of the first recalled item, and the degree of variability in the order in which items are reported at test (i.e., the temporal distance across the first four recalled items). We tested two hypotheses in separate experiments: (1) whether these measures predicted generalized cognitive ability, and (2) whether they predicted gray matter hippocampal volume. To test hypothesis 1, we conducted ordinal regression analyses on data from a group of 452 participants, aged 60 or above. Memory performance was measured with Reys AVLT and generalized cognitive ability was measured with the MMSE test. To test hypothesis 2, we conducted a linear regression analysis on data from a sample of 79 cognitively intact individuals aged 60 or over. Memory was measured with the BSRT and hippocampal volume was extracted from MRI ...
After presenting the essentials of probability and statistics, the book covers simple regression analysis, multiple regression analysis, and advanced topics including heteroskedasticity, autocorrelation, large sample properties, instrumental variables, measurement error, omitted variables, panel data, simultaneous equations, and binary/truncated dependent variables. Two optional chapters treat additional probability and statistics topics. Each chapter offers examples, prep problems (bringing students up to speed at the beginning of a chapter), review questions, and exercises. An accompanying website offers students easy access to Java simulations and data sets (available in EViews, Stata, and Excel files). After a single semester spent mastering the material presented in this book, students will be prepared to take any of the many elective courses that use econometric techniques ...
This section describes the dialog box tabs that are associated with the Polynomial Regression analysis. The Polynomial Regression analysis calls the REG procedure in SAS/STAT software. See the REG procedure documentation in the SAS/STAT Users Guide for details. ...
74 How to Use Microsoft Excel® for Regression Analysis This section of this chapter is here in recognition that what we are now asking requires much more than a quick calculation of a ratio or a square root. Cons high low . In this article, we will explain four types of revenue forecasting methods that financial analysts use to predict future revenues. True _____ is a unit less quantity R Square+ When two or more variables are correlated in a Multiple Regression Model , it ... Regression Analysis Q&A.txt; COIMBATORE INSTITUTE OF TECHNOLOGY; BLOCK CHAI 123 - Spring 2019. Econometrics , Chapter 2 , Simple Linear Regression Analysis , Shalabh, IIT Kanpur 2 and the conditional variance of y given Xx as Var y x(,) 2. In regression analysis, the quantity that gives the amount by which Y changes for a unit change in X is called the a. coefficient of determination b. slope of the regression line c. Y intercept of the regression line d. correlation coefficient 23. Computation 4. The direction in which ...
Resampling Techniques in Regression Analysis for Model Simplification, 978-3-659-14290-1, Resampling techniques are now-a-days widely used for model assessment and comparison. In the literature, many variable selection methods for regression modeling have been developed whose performance depends critically on the stopping rules. In this book, resampling application for variable selection on the basis of optimum choice of stopping rules for each data set and model simplification in various regression models are addressed. We propose a general approach of resampling techniques in regression analysis that allows us to choose the stopping criterions for each data set. Our selection method first choosing appropriate cutoff values/stopping criterions and results in selecting a good subset regression model. We focus on optimizing cutoff values or stopping criterions in automated model selection methods in regression analysis due to the interest in holding only authentic predictor variables in the
By Gabriel Vasconcelos Introduction Today we are going to talk about quantile regression. When we use the lm command in R we are fitting a linear regression using Ordinary Least Squares (OLS), which has the interpretation of a model for … Continue reading →
Quick start Probit model of y on r manual probit calculation continuous variable x1. Below you will find a step by r manual probit calculation step guide to using probit analysis with various methods. Jun 03, · Probit regression and probability calculation 01 Jun , Dear Statalist, I have to run a probit regression and afterwards I am being asked to calculate the probability of dependent variable = 1 for certain values of explanatory variables. The scalar r is the dependence parameter and will assumed abs(r) r.. 2 Distribution of dependent variable yijxi is a linear exponential family, f(y; ;˚) = exp ˆ y b() + c(y ˚) 3 Expected response and linear predictor are related by a monotonic transformation, g. Probit Download: Probit analysis programs. The formula to calculate the inverse Mills ratios for univariate probit models is taken from Greene (, p. Probit and Logit Models R Program and [HOST] Probit and Logit Models R Program and [HOST] Sign In. If you really want to reproduce it, you either ...
Evidence suggests that physical activity has a beneficial effect of elevated high-density lipoprotein cholesterol (HDL-C) on reducing coronary artery risk. However, previous studies show contrasting results for this association between different types of exercise training (i.e., aerobic, resistance, or combined aerobic and resistance training). The aim of this study was to determine which type of exercise training is more effective in increasing HDL-C levels. Forty obese men, age 18-29 yr, were randomized into 4 groups: an aerobic-training group (n = 10), a resistance-training group (n = 10), a combined-exercise-training group (n = 10), and a control group (n = 10). After a 12-wk exercise program, anthropometrics, blood biochemical variables, and physical-fitness components were compared with the data obtained at the baseline. Multiple-regression analysis was used to evaluate the association between different types of exercise training and changes in HDL-C while adjusting for potential ...
This class shows you how to perform simple regression analysis. It is useful in estimating adjustments, such as market timing. It does not predict a sale price for the subject property (multiple variable regression analysis does that). It requires human intervention in the selection of data, elimination of outliers, and simple common sense.. ...
Quantile regression have its advantage properties comparing to the OLS model regression which are full measurement of the effects of a covariate on response, robustness and Equivariance property. In this paper, I use a survey data in Belgium and apply a linear model to see the advantage properites of quantile regression. And I use a quantile regression model with the raw data to analyze the different cost of family on different numbers of children and apply a Wald test. The result shows that for most of the family types and living standard, from the lower quantile to the upper quantile the family cost on children increases along with the increasing number of children and the cost of each child is the same. And we found a common behavior that the cost of the second child is significantly more than the cost of the first child for a nonworking type of family and all living standard families, at the upper quantile (from 0.75 quantile to 0.9 quantile) of the conditional distribution.. ...
Abstract: Detecting genetic loci responsible for variation in quantitative traits is a problem of great importance to biologists. The location on a genetic map responsible for a quantitative trait is referred to as Quantitative Trait Loci, or QTL. This thesis uses a Bayesian Hierarchical Regression model which incorporates variability both within and between lines to detect the QTL. This method is applied to a simulated data set using the line information from Bay-0 × Shahdara population to find the activation probability of each genetic segment via the Gibbs sampler and Monte Carlo integration techniques. Using the activation probability, which indicates the influence of each segment within all the models, the QTL is detected. The results show that it is an effective way to detect QTL.. Bayesian hierarchical regression model to detect quantitative trait loci ...
was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.. © 2011 Optical Society of America. Full Article , PDF Article ...
Notes 5: Simple Linear Regression. 1. The Simple Linear Regression Model 2. Estimates and Plug-in Prediction 3. Confidence Intervals and Hypothesis Tests 4. Fits, residuals, and R-squared. 1. The Simple Linear Regression Model. price: thousands of dollars Slideshow 333739 by keira
PREFACE xiii. 1. INTRODUCTION 1. 1.1 Regression and Model Building 1. 1.2 Data Collection 5. 1.3 Uses of Regression 9. 1.4 Role of the Computer 10. 2. SIMPLE LINEAR REGRESSION 12. 2.1 Simple Linear Regression Model 12. 2.2 Least-Squares Estimation of the Parameters 13. 2.3 Hypothesis Testing on the Slope and Intercept 22. 2.4 Interval Estimation in Simple Linear Regression 29. 2.5 Prediction of New Observations 33. 2.6 Coeffi cient of Determination 35. 2.7 A Service Industry Application of Regression 37. 2.8 Using SAS and R for Simple Linear Regression 39. 2.9 Some Considerations in the Use of Regression 42. 2.10 Regression Through the Origin 45. 2.11 Estimation by Maximum Likelihood 51. 2.12 Case Where the Regressor x is Random 52. 3. MULTIPLE LINEAR REGRESSION 67. 3.1 Multiple Regression Models 67. 3.2 Estimation of the Model Parameters 70. 3.3 Hypothesis Testing in Multiple Linear Regression 84. 3.4 Confidence Intervals in Multiple Regression 97. 3.5 Prediction of New Observations 104. 3.6 A ...
View Notes - 204 14 simp lin reg from MATH 2040 at Utah Valley University. Chapter 14 Simple Linear Regression Hypotheses tests and Confidence Intervals In simple linear regression we assume there is
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TY - JOUR. T1 - Unconditional quantile regression analysis of UK inbound tourist expenditures. AU - Sharma, Abhijit. AU - Woodward, Richard. AU - Grillini, Stefano. N1 - NOTICE: this is the authors version of a work that was accepted for publication in Economics Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Economics Letters, 186, (2020) DOI: 10.1016/j.econlet.2019.108857 © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International PY - 2020/1. Y1 - 2020/1. N2 - Using International Passenger Survey (2017) data, this paper employs unconditional quantile regression (UQR) to analyse the determinants of tourist ...
The research aims to study the distribution of hourly wages for men and women in Portugal, adopting a quantile regression (QR) approach. Two databases are used for the estimation of the wage functions: the Quadros de Pessoal, Linked Employer-Employee Data (QP-LEED) and the Inquérito ao Emprego, Portuguese Labour Force Survey (IE-LFS). Three basic models are considered to explain the hourly wages for men and women: the first model, using each database separately, is estimated adopting education, tenure, potential experience, activity sector, and job as independent variables; the second, using data from QP-LEED, includes additional determinants related to firm (firm size and foreign social capital); and the third, using data from the IE-LFS, includes additional independent variables related to the workers family (marital status and children). The results indicate that: (i) Regardless of the database used, the quantile regression (QR) shows superiority over OLS approach; (ii) In general, the same ...
In this guide, we will learn how to build a Simple Linear Regression Model using Sci-kit Learn. Simple Linear Regression is a allgorithm
TY - JOUR. T1 - Constrained topological mapping for nonparametric regression analysis. AU - Cherkassky, Vladimir S. AU - Lari-Najafi, Hossein. PY - 1991. Y1 - 1991. N2 - The idea of using Kohonens self-organizing maps is applied to the problem of nonparametric regression analysis, that is, evaluation (approximation) of the unknown function of N-1 variables given a number of data points (possibly corrupted by random noise) in N-dimensional input space. Simple examples show that the original Kohonens algorithm performs poorly for regression problems of even low dimensionality, due to the fact that topologically correct ordering of units in N-dimensional space may violate the natural topological ordering of projections of those units onto (N-1)-dimensional subspace of independent variables. A modification of the original algorithm called the constrained topological mapping algorithm is proposed for regression analysis applications. Given a number of data points in N-dimensional input space, the ...
Regression analysis models are adopted by using SPSS program to predict the 28-day compressive strength as dependent variable and the accelerated compressive strength as independent variable. Three accelerated curing method was adopted, warm water (35ºC) and autogenous according to ASTM C C684-99 and the British method (55ºC) according to BS1881: Part 112:1983. The experimental concrete mix design was according to ACI 211.1. Twenty eight concrete mixes with slump rang (25-50) mm and (75-100)mm for rounded and crushed coarse aggregate with cement content (585, 512, 455, 410, 372 and 341)Kg/m3.. The experimental results showed that the accelerated strength were equal to about (0.356), (0.492) and (0.595) of the 28-day compressive strength for warm water, autogenous and British curing methods respectively. A statistical regression analysis using SPSS program is implemented for the experimental results of the 28-day compressive strength ranging from (16 to 55.2)Mpa and accelerated strength for ...
Results HCM patients exhibited marked exercise limitation compared with controls (peak oxygen consumption 23.28 ± 6.31 ml/kg per minute vs 37.70 ± 7.99 ml/kg per minute, p,0.0001). The left ventricular ejection fraction (LVEF) in HCM patients and controls was similar (62.76 ± 9.05% vs 62.48 ± 5.82%, p = 0.86). Longitudinal, radial and circumferential strain and strain rate were all significantly reduced in HCM patients compared with controls. There was no significant difference in left ventricular twist and torsion between HCM patients and controls; however, there was a significant delay in 25% of the untwist and late untwist rate in HCM patients compared with controls. Using multiple stepwise regression analysis, both systolic twist rate and longitudinal systolic strain were independent predictors of exercise capacity (r = 0.5, p = 0.001, r = 0.4, p = 0.002, respectively) ...
The considerable gap between urban and rural areas in China has been one of those social problems during the urbanization process. Since the early 2000s, an increasing number of theoretical and empirical studies have discussed the association between urbanization and urban-rural income gap (URIG) in China. However, a very limited consensus has been reached so far, which makes it challenging to support formulating well-informed policies. To identify factors contributing to different conclusions of the effects of urbanization on URIG in China, we conducted a systematic literature review of 29 empirical studies and stepwise meta-regression analysis from 94 direct effect-size estimates. Our findings reveal that while urbanization is associated with larger URIG when URIG is measured via urban-rural income/consumption, urbanization is associated with smaller URIG when URIG is measured with inequality index (e.g., Theil index and/or Gini coefficient). Additionally, financial development is correlated with
AIMS: To estimate the combined contribution of serum total cholesterol, blood pressure and cigarette smoking to coronary heart disease (CHD) risk after adjustment for regression dilution bias. METHODS AND RESULTS: Six thousand, five hundred and thirteen middle-aged British men without CHD were followed for major CHD events over 10 years. The population attributable risk fraction (PARF) was predicted for a range of risk factor thresholds before and after adjustment for regression dilution of serum total cholesterol and blood pressure. Defining low-risk individuals as being in the bottom tenth of the population distributions of serum total cholesterol (|5.2 mmol/l) and diastolic blood pressure (|70 mmHg) and a non-cigarette smoker, the PARF was 75%, increasing to 86% after adjustment for regression dilution. Regardless of the threshold criteria chosen, the PARF was substantially greater than 65% before adjustment for regression dilution and greater than 75% after adjustment. Exclusion of ex-smokers and
The own-wage elasticity of labor demand is a key parameter in empirical research and policy analysis. However, despite extensive research, estimates of labor demand elasticities are subject to considerable heterogeneity. In this paper, we explore various dimensions of this heterogeneity by means of a comprehensive meta-regression analysis, building on information from 151 different studies containing 1334 estimates in total. Our results show that heterogeneity in the estimates of the elasticity is natural to a considerable extent: the magnitude of the elasticity depends on the theoretical model applied and features of the workforce. Moreover, we find that labor demand has become more elastic over time, and is particularly elastic in countries with low levels of employment protection legislation. Furthermore, we find heterogeneity due to the empirical specification of the labor demand model, characteristics of the dataset and publication bias ...
This thesis consists of three essays that address open research issues in two econometric frameworks: nonparametric quantile regression framework and social networks, supported by empirical applications. Both econometric approaches are used to achieve a deeper understanding of the economic processes and interactions in comparison to the simple mean regression ...
Incorporates Mixed Effects Modeling Techniques For More Powerful And Efficient Methods This Book Presents Current And Effective Nonparametric Regression Techniques For Longitudinal Data Analysis And Systematically Investigates The Incorporation Of Mixed Effects Modeling Techniques Into Various Nonparametric Regression Models
This work is geared towards detecting and solving the problem of multicolinearity in regression analysis. As such, Variance Inflation Factor (VIF) and the Condition Index (CI) were used as measures of such detection. Ridge Regression (RR) and the Principal Component Regression (PCR) were the two other approaches used in modeling apart from the conventional simple linear regression. For the purpose of comparing the two methods, simulated data were used. Our task is to ascertain the effectiveness of each of the methods based on their respective mean square errors. From the result, we found that Ridge Regression (RR) method is better than principal component regression when multicollinearity exists among the predictors.
In the single predictor case of linear regression, the standardized slope has the same value as the correlation coefficient. The advantage of the linear regression is that the relationship can be described in such a way that you can predict (based on the relationship between the two variables) the score on the predicted variable given any particular value of the predictor variable. In particular one piece of information a linear regression gives you that a correlation does not is the intercept, the value on the predicted variable when the predictor is 0.. In short - they produce identical results computationally, but there are more elements which are capable of interpretation in the simple linear regression. If you are interested in simply characterizing the magnitude of the relationship between two variables, use correlation - if you are interested in predicting or explaining your results in terms of particular values you probably want regression.. ...
Abstract: I consider the estimation of linear regression models when the independent variables are measured with errors whose variances differ across observations, a situation that arises, for example, when the explanatory variables in a regression model are estimates of population parameters based on samples of varying sizes. Replacing the error variance that is assumed common to all observations in the standard errors-in-variables estimator by the mean measurement error variance yields a consistent estimator in the case of measurement error heteroskedasticity. However, another estimator, which I call the Heteroskedastic Errors in Variables Estimator (HEIV), is, under standard assumptions, asymptotically more efficient. Simulations show that the efficiency gains are likely to appreciable in practice. In addition, the HEIV estimator, which is equal to the ordinary least squares regression of the dependent variable on the best linear predictor of the true independent variables, is simple to ...
The purpose of this study was to examine the role of grit and intrinsic motivation. regarding students propensity to procrastinate. Three specific research questions were. constructed: How much of the variance in participants procrastination is explained solely. by their degree of grit? Does the degree of intrisic motivation contribute with additional. explanatory information for the regression between grit and procrastination? Is intrisic. motivation a mediator for the regression between grit and procrastination?. To test this, a hierarchical multiple regression analysis was constructed. To collect. data an electronic questionnaire was constructed. The sample consisted of 271 students who. all studied at Karlstad University. The data was collected through the learning platform. itslearning. Grit was measured with Swedish-Grit Scale. Intrinsic motivation was measured. with a modified version of Task Evaluation Questionnaire and the students propensity to. procrastinate was measured with ...
The article reviews the problems of determining the prevalence of hazardous chemicals in the atmosphere of various information-analytical system (IAS). The expediency of the introduction of the monitoring unit in the IAS, which provides data for building a more accurate picture of the distribution of concentrations. The problem of determining the prevalence of hazardous chemicals and developed her critical method based on regression analysis of monitoring data. The analysis of regression functions and determined the optimal function.
Video created by Johns Hopkins University for the course Statistical Reasoning for Public Health 2: Regression Methods. In this module, a unified structure for simple regression models will be presented, followed by detailed treatises and ...
quick and easy-to-remember way for Lean Six Sigma practitioners to get the most benefit from simple linear regression analysis is with a simple check-up method. The method borrows and adapts the familiar concept found in the 5S tool.
Japan Geoscience Union Meeting 2016,Classification and Regression Tree Analysis of the Relationship between the Yellow Dust Concentration and TOA Reflectance observed with GOSAT CAI Sensor
I have atherosclerosis data set matched for age and gender. My doctoral advisory members suggested me to perform conditional logistic regression instead of...
Forecasting, in time series is an important in planning and making assumptions about future events using different statistical methods, and depends on estimating the value of a variable at a future date. The study reviewed the missing views in the time series (a model without loss of observations and three models was assumed to be lost in the views of the dependent variable in different locations in the series) ,After a simple linear regression of the four models of the analysis show that the series without losing it show coherent and clear in their dealings and morally within the statistical acceptable levels, and the loss of view where what is its position within the series and it show obvious effect on the estimated value of any expected value is much greater than the value of truth The Akaike test was used to compare the models and the test results indicated the models superiority without loss. and has recommended the researcher on the need to use all the views in the dependent variable ...
Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates
TY - GEN. T1 - Quantile Regression Analysis of Exchange Rate Risk in Cross-Country Sector Porfolios. AU - Gulati, Anand Bir S.. N1 - Volume: Proceeding volume: PY - 2011. Y1 - 2011. KW - 511 Economics. KW - KOTA2011. M3 - Conference contribution. BT - IRMC Conference Proceedings 2011. T2 - International Risk Management Conference - New Dimensions in Risk Management. Y2 - 1 January 1800. ER - ...
It is true that some countries grow faster than the linear regression line would indicate, and some slower. But this simple regression analysis says government spending explains almost half of the growth rate.. If you believe my equation, it vindicates conservatives: bigger government stifles growth. Yet it also vindicates statists: a government would need to spend 70% of GDP to stop growth altogether, and a government that takes half of everything could still grow about 2% per year. That is not exactly a gang-busting rate, but a lot of countries would be happy with 2% right now.. If we take away the four Asian Tigers of Hong Kong, Korea, Singapore, and Taiwan, the other advanced economies spent between 35% (Switzerland) and 56% (France) of their GDPs in 2011. Per my equation, the growth rates should be between 1.4% and 3.5% per year.. Why should Average Joe Voter care whether GDP grows 1.4% or 3.5%, especially if he thinks hes getting some good things from government like roads, schools, ...
Home , Zeitschriften , Journal of Environmental Pathology, Toxicology and Oncology , Volumen 22, 2003 Ausgabe 2 , LETTER TO THE EDITOR: Lack of Efficacy of the Combination of Pamidronate and Vitamin D on Regression of Prostate Cancer in the Dunning Rat Model ...
Performance Evaluation: Simple Linear Regression Models Hongwei Zhang Statistics is the art of lying by means of figures. ---...
Results Peripheral ED was documented in 212 out of 633 RA patients (33.3%). A linear regression for multiple variables (stepwise method) performed including into the models variables showing significant association with LnRHI at the univariate regression analysis (systolic blood pressure, HDL cholesterol levels, triglycerides levels, smoking habit and ACPA positivity; Age and gender were forced) showed that only higher levels of triglycerides [B coefficient (95%IC) = -0,001 (-0,001-0,00); p,0.05] negativity for ACPA [B coefficient (95%IC) = -0,070 (-0,135-0,005); p,0.05] and smoking habit [B coefficient (95%IC) =0,01 (0,043-0,156); p,0.05] were independently related to lower values of LnRHI. No significant correlation between peripheral ED and RA activity (DAS-28, CDAI, SDAI, HAQ), burden of systemic inflammation (CRP, ESR) and type of immunosuppressive treatment (steroids, NSAIDs, DMARDs and bDMARDs) was found. At logistic regression analysis ACPA negativity [OR ((95%IC) = 1.57 (1.04-2.21); ...
So you have data, do you? Thats awesome because anyone that loves statistics loves data! And data begs to be analyzed. Most of the time, you should start with a graph and some type of linear regression. But once you have the equation what do you do? Thats where simple regression analysis comes in. Key […]. ...
This chapter studies the effect of increasing formality via tax reduction and simplification schemes on micro-firm performance. We develop a simple theoretical model that yields two intuitive results. First, low- and high-ability entrepreneurs are unlikely to be affected by a tax reduction and therefore, the reduction has an impact only on a segment of the microfirm population. Second, the benefits to such reduction, as measured by profits and revenues, are increasing in the entrepreneurs ability. Then, we estimate the effect of formality on the entire conditional distribution (quantiles) of revenues using the 1996 Brazilian SIMPLES program and a rich survey of formal and informal micro-firms. The econometric approach compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We develop an estimator that combines both quantile regression and the regression discontinuity design. The econometric results corroborate the positive ...
Background. The aim of this study was to identify clinical risk factors associated with the development of albuminuria and renal impairment in patients with type 2 diabetes (T2D). In addition, we evaluated if different equations to estimate renal function had an impact on interpretation of data. This was done in a nationwide population-based study using data from the Swedish National Diabetes Register. Methods. Three thousand and six hundred sixty-seven patients with T2D aged 30-74 years with no signs of renal dysfunction at baseline (no albuminuria and eGFR ,60 mL/min/1.73 m(2) according to MDRD) were followed up for 5 years (2002-2007). Renal outcomes, development of albuminuria and/or renal impairment [eGFR , 60 mL/min/1.73 m(2) by MDRD or eCrCl , 60 mL/min by Cockgroft-Gault (C-G)] were assessed at follow-up. Univariate regression analyses and stepwise regression models were used to identify significant clinical risk factors for renal outcomes. Results. Twenty percent of patients developed ...
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Simple linear regression is used to model the relationship between two continuous variables. Often, the objective is to predict the value of an output variable based on the value of an input variable.
Prepare online for ICS part 2, 12th class Statistics Chapter 14 online mcq test with answers pdf, ICS Part 2 Book 2 Statistics Chapter 14 Simple Linear Regression and Correlation
Reason: We can ex ppylicitly control for other factors that affect the dependent variable y. x is called independent, predictor, os explanatory variable. A more aggressive but, in our opinion, reasonable approach would be to first note that the three equations are jointly significant, so we are justified in making some interpretation. Be able to correctly interpret the conceptual and practical meaning of coeffi-cients in linear regression analysis 5. • Researchers often report the marginal effect, which is the change in y* for each unit change in x. Conduct and Interpret an Ordinal Regression What is Ordinal Regression? SOLUTIONS . Skills: Statistics, Statistical Analysis, SPSS Statistics, Mathematics, Analytics It is designed to be an overview rather than a comprehensive guide, aimed at covering the basic tools necessary for econometric analysis. Here, its . Data analysis and regression in Stata This handout shows how the weekly beer sales series might be analyzed with Stata (the software ...
Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Caos method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results ...
Linear regression is a widely used supervised learning algorithm for various applications. The advantage of using linear regression is its implementation simplicity. A Linear regression algorithm is widely used in the cases where there is need to predict numerical values using the historical data. Suppose we have 20 years of population data and we are interested in predicting the population for the next 5 years or we have product purchase data and we are interested to find the best selling price by changing the product related features, linear regression will be the right choice to tackle this kind of interesting problems.Even though we have a bunch of regression algorithms to predict numerical values. Such as : Polynomial Regression, Stepwise Lasso Regression andElasticNet Regression.. Linear regression mostly used method for solving linear regression kind of problems, because linear regression needs less computational power compared to other regression methods and its the best approach to ...
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Wikimedia Commons has media related to Regression analysis. "Regression analysis", Encyclopedia of Mathematics, EMS Press, 2001 ... Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for ... Applied Regression Analysis (3rd ed.). John Wiley. ISBN 978-0-471-17082-2. Fox, J. (1997). Applied Regression Analysis, Linear ... Analysis of Variance," pp. 541-554. Lindley, D.V. (1987). "Regression and correlation analysis," New Palgrave: A Dictionary of ...
... regression Isotonic regression Semiparametric regression Local regression Total least squares regression Deming regression ... The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis - use of ... Simple linear regression Trend estimation Ridge regression Polynomial regression Segmented regression Nonlinear regression ... Regression analysis Linear regression Least squares Linear least squares (mathematics) Non-linear least squares Least absolute ...
... is a version of regression analysis when responses or covariates include functional data. Functional ... Yao, Müller and Wang (2005). "Functional linear regression analysis for longitudinal data". The Annals of Statistics. 33 (6): ... Functional data analysis Functional principal component analysis Karhunen-Loève theorem Generalized functional linear model ... analogous to extending linear regression to polynomial regression. For a scalar response Y {\displaystyle Y} and a functional ...
Watson, G. S. (1964). "Smooth regression analysis". Sankhyā: The Indian Journal of Statistics, Series A. 26 (4): 359-372. JSTOR ... ISBN 0-387-94716-7. Scale-adaptive kernel regression (with Matlab software). Tutorial of Kernel regression using spreadsheet ( ... Stata: npregress, kernreg2 Kernel smoother Local regression Nadaraya, E. A. (1964). "On Estimating Regression". Theory of ... with Microsoft Excel). An online kernel regression demonstration Requires .NET 3.0 or later. Kernel regression with automatic ...
Principal component analysis Partial least squares regression Ridge regression Canonical correlation Deming regression Total ... In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component ... using ordinary least squares regression (linear regression) to get a vector of estimated regression coefficients (with ... The regression function is then assumed to be a linear combination of these feature elements. Thus, the underlying regression ...
Logistic regression Multinomial probit Greene, William H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education ... doi:10.1111/j.1467-9574.1988.tb01238.x. Menard, Scott (2002). Applied Logistic Regression Analysis. SAGE. p. 91. ISBN ... The formulation of binary logistic regression as a log-linear model can be directly extended to multi-way regression. That is, ... The article on logistic regression presents a number of equivalent formulations of simple logistic regression, and many of ...
Koopmans, T. C. (1936). Linear regression analysis of economic time series. DeErven F. Bohn, Haarlem, Netherlands. Kummell, C. ... The Deming regression is only slightly more difficult to compute than the simple linear regression. Most statistical software ... Deming regression becomes orthogonal regression: it minimizes the sum of squared perpendicular distances from the data points ... In statistics, Deming regression, named after W. Edwards Deming, is an errors-in-variables model which tries to find the line ...
... Analysis: Theory and Computing, World Scientific, pp. 1-2, ISBN 9789812834119, Regression analysis ... is ... Logistic regression and probit regression for binary data. Multinomial logistic regression and multinomial probit regression ... which is the domain of multivariate analysis. Linear regression was the first type of regression analysis to be studied ... Mathematics portal Analysis of variance Blinder-Oaxaca decomposition Censored regression model Cross-sectional regression Curve ...
Applied Regression Analysis (3rd ed.). John Wiley. p. 19. ISBN 0-471-17082-8. Riggs, D. S.; Guarnieri, J. A.; et al. (1978). " ... Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the ... Standard methods can fit a regression of y on w without bias. There is bias only if we then use the regression of y on w as an ... Recall that linear regression is not symmetric: the line of best fit for predicting y from x (the usual linear regression) is ...
In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and ... Poisson regression creates proportional hazards models, one class of survival analysis: see proportional hazards models for ... Cameron, A. C.; Trivedi, P. K. (1998). Regression analysis of count data. Cambridge University Press. ISBN 978-0-521-63201-0. ... Negative binomial regression is a popular generalization of Poisson regression because it loosens the highly restrictive ...
Applied Regression Analysis (3rd ed.). John Wiley. ISBN 0-471-17082-8. Cook and Weisberg (1982). Residuals and Influence in ... Partial regression plots are related to, but distinct from, partial residual plots. Partial regression plots are most commonly ... On the other hand, for the partial regression plot, the x-axis is not Xi. This limits its usefulness in determining the need ... Partial regression plots are also referred to as added variable plots, adjusted variable plots, and individual coefficient ...
... analysis is based on the differences in the magnitude of regression to the mean in a genetic trait ... also sometimes called DeFries-Fulker extremes analysis, is a type of multiple regression analysis designed for estimating the ... "DeFries-Fulker multiple regression analysis". Erik Willcutt website. University of Colorado Boulder. Retrieved 2018-06-20. ... Lazzeroni, Laura C.; Ray, Amrita (2012-12-20). "A Generalized Defries-Fulker Regression Framework for the Analysis of Twin Data ...
The factor regression model can be viewed as a combination of factor analysis model ( y n = A x n + c + e n {\displaystyle \ ... Within statistical factor analysis, the factor regression model, or hybrid factor model, is a special multivariate model with ... B {\displaystyle \mathbf {B} } is the (unknown) regression coefficients of the design factors. c {\displaystyle \mathbf {c} } ... Open source software to perform factor regression is available. Carvalho, Carlos M. (1 December 2008). "High-Dimensional Sparse ...
Hoerl, Arthur E. (1962). "Application of Ridge Analysis to Regression Problems". Chemical Engineering Progress. 58 (3): 54-59. ... Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent ... Ridge regression was developed as a possible solution to the imprecision of least square estimators when linear regression ... L2 regularization is used in many contexts aside from linear regression, such as classification with logistic regression or ...
Linear regression Regression analysis Andrews, D. W. K. (2005). "Cross-Section Regression with Common Shocks" (PDF). ... This type of cross-sectional analysis is in contrast to a time-series regression or longitudinal regression in which the ... Regression analysis, Cross-sectional analysis, All stub articles, Statistics stubs). ... doi:10.1111/j.1468-0262.2005.00629.x. Preprint Wooldridge, Jeffrey M. (2009). "Part 1: Regression Analysis with Cross Sectional ...
In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the ... Like other forms of regression analysis, logistic regression makes use of one or more predictor variables that may be either ... Linear regression and logistic regression have many similarities. For example, in simple linear regression, a set of K data ... To do so, they will want to examine the regression coefficients. In linear regression, the regression coefficients represent ...
"Leveraging field data for impact analysis and regression testing". ACM SIGSOFT Software Engineering Notes. 28 (5): 128-137. doi ... In order to avoid regressions being seen by the end-user after release, developers regularly run regression tests after changes ... These tests can include unit tests to catch local regressions as well as integration tests to catch remote regressions. ... "Configuration selection using code change impact analysis for regression testing". Proceedings of the International Conference ...
Kousser, J. Morgan (1973). "Ecological Regression and the Analysis of past Politics" (PDF). Journal of Interdisciplinary ... Ecological regression is a statistical technique which runs regression on aggregates, often used in political science and ... then running a linear regression of dependent variable D against independent variable C will give D = a + bC. If the regression ... Brown, Philip J.; Payne, Clive D. (1986). "Aggregate Data, Ecological Regression, and Voting Transitions". Journal of the ...
2-4. ISBN 0-8039-5710-6. Amemiya, T. (1973). "Regression Analysis When the Dependent Variable is Truncated Normal". ... Censored regression model Sampling bias Truncated distribution Breen, Richard (1996). Regression Models : Censored, Samples ... Breen, Richard (1996). "Sample-Selection Models and the Truncated Regression Model". Regression Models : Censored, Samples ... Truncated regression models are a class of models in which the sample has been truncated for certain ranges of the dependent ...
Non-homogeneous Gaussian regression (NGR) is a type of statistical regression analysis used in the atmospheric sciences as a ... Articles with short description, Short description matches Wikidata, Regression analysis). ... It achieves this by generalising the simple linear regression model to either: y t ∼ N ( α + β m t , σ = γ + δ s t ) {\ ... The original name 'spread regression' has now fallen from use, EMOS is used to refer generally to any method used for the ...
Regression analysis, Statistical genetics, Genetic linkage analysis, All stub articles, Genetics stubs). ... regression is a form of statistical regression originally proposed for linkage analysis of quantitative traits for sibling ... Although HE regression "...seems a rusty weapon in the genomics analysis armory of the GWAS era. This is because the HE ... Wang, Tao; Elston, Robert C. (July 2005). "Two-level Haseman-Elston regression for general pedigree data analysis". Genetic ...
In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory ... as in linear regression. Binary regression is usually analyzed as a special case of binomial regression, with a single outcome ... logistic regression) and the probit model (probit regression). Binary regression is principally applied either for prediction ( ... In economics, binary regressions are used to model binary choice. Binary regression models can be interpreted as latent ...
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function ... Segmented regression with confidence analysis may yield the result that the dependent or response variable (say Y) behaves ... The nonlinear regression statistics are computed and used as in linear regression statistics, but using J in place of X in the ... Articles with short description, Short description matches Wikidata, Regression analysis). ...
... or kinked regression) can also mean a type of segmented regression, which is a different type of analysis. Final Considerations ... Regression-Discontinuity Analysis at Research Methods Knowledge Base (Articles with short description, Short description ... Quasi-experiment Design of quasi-experiments Thistlethwaite, D.; Campbell, D. (1960). "Regression-Discontinuity Analysis: An ... In contrast to the sharp regression discontinuity design, a fuzzy regression discontinuity design (FRDD) does not require a ...
Draper, N. and Smith, H. (1981) Applied Regression Analysis, 2d Edition, New York: John Wiley & Sons, Inc. SAS Institute Inc. ( ... Freedman's paradox Logistic regression Least-angle regression Occam's razor Regression validation Lasso (statistics) Efroymson, ... The procedure is used primarily in regression analysis, though the basic approach is applicable in many forms of model ... doi:10.1093/biomet/81.3.425 Mark, Jonathan, & Goldberg, Michael A. (2001). Multiple regression analysis and mass assessment: A ...
Cleveland, William S.; Devlin, Susan J. (1988). "Locally-Weighted Regression: An Approach to Regression Analysis by Local ... Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and ... and Survival Analysis. Springer. ISBN 978-3-319-19425-7. Local Regression and Election Modeling Smoothing by Local Regression: ... This can make it difficult to transfer the results of an analysis to other people. In order to transfer the regression function ...
High-dimensional statistics Lasso (statistics) Regression analysis Model selection Efron, Bradley; Hastie, Trevor; Johnstone, ... In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, ... The basic steps of the Least-angle regression algorithm are: Start with all coefficients β {\displaystyle \beta } equal to zero ... "A simple explanation of the Lasso and Least Angle Regression". (Wikipedia articles that are too technical from April 2018, All ...
Capital asset pricing model Standard errors in regression analysis IHS EViews (2014). "Fama-MacBeth Two-Step Regression" (PDF ... The Fama-MacBeth regression is a method used to estimate parameters for asset pricing models such as the capital asset pricing ... This means Fama MacBeth regressions may be inappropriate to use in many corporate finance settings where project holding ... "EconTerms - Glossary of Economic Research "Fama-MacBeth Regression"". Archived from the original on 28 September 2007. ...
... is defined to be a meta-analysis that uses regression analysis to combine, compare, and synthesize research ... A meta-regression analysis aims to reconcile conflicting studies or corroborate consistent ones; a meta-regression analysis is ... Meta-analysis (and meta-regression) is often placed at the top of the evidence hierarchy provided that the analysis consists of ... A meta-analysis with some or all studies having more than two arms is called network meta-analysis, indirect meta-analysis, or ...
... is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares ... Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. One ... Beyond simple linear regression, there are several machine learning methods that can be extended to quantile regression. A ... "qrnn: Quantile Regression Neural Networks". R Project. 2018-06-26. "qgam: Smooth Additive Quantile Regression Models". R ...
His last book, Time Series Analysis by State Space Methods, was published by Oxford University Press in May 2012. His last ... Durbin, J.; Watson, G. S. (1950). "Testing for Serial Correlation in Least Squares Regression: I". Biometrika. Biometrika Trust ... Durbin, J. (2012). Time Series Analysis by State Space Methods. Oxford Statistical Science Series. Oxford University Press. ... known particularly for his work on time series analysis and serial correlation. The son of a greengrocer, Durbin was born in ...
In regression analysis and least squares problems, the standard error of parameter estimates is readily available, which can be ... "Modularization in Bayesian analysis, with emphasis on analysis of computer models". Bayesian Analysis. Institute of ... The targets of uncertainty propagation analysis can be: To evaluate low-order moments of the outputs, i.e. mean and variance. ... The probabilistic approach is considered as the most rigorous approach to uncertainty analysis in engineering design due to its ...
"Öffentlichkeit und Erfahrung" has been translated into English as Public Sphere and Experience: Toward an Analysis of the ... Plass, Ulrich (Winter 2009). "Dialectic of Regression: Theador W Adorno and Fritz Lang". Telos. 149: 142. "Berlinale 1965: ... Analysis of the Bourgeois and Proletarian Public, trans. Peter Labany, Jamie Owen Daniel, and Assenka Oksiloff (Verso Books, ...
Within regression approaches, linear, log-normal and logistic regression approaches have been applied, but have been criticised ... Various analyses have sought to model length of stay in different condition contexts. This has usually been done with ... A variation in the calculation of ALOS can be to consider only length of stay during the period under analysis. Length of stay ... Carter & Potts (2014) instead recommend use of negative binomial regression. Length of stay is commonly used as a quality ...
Partially adjusted regression analyses produced similar results, as did analyses restricted to domestic and non-domestic mass ... In the October 2018 PLOS One study, the Bayesian zero-inflated Poisson regression model that included state-level SMI rates as ... In 2018, the FBI Behavioral Analysis Unit released a survey of 63 active shooter cases between 2000 and 2013 that found that ... However, the researchers also used a Poisson regression model to test if the frequency of online media coverage density and ...
In the form of statistical analysis glass modeling can aid with accreditation of new data, experimental procedures, and ... linear regression can be applied using common polynomial functions up to the third degree. Below is an example equation of the ... Glass properties and glass behavior during production can be calculated through statistical analysis of glass databases such as ... C using statistical analysis". Glass Technol. 40 (5): 149-53. Priven A.I. (December 2004). "General Method for Calculating the ...
Over time, a more varied analysis of bodhisattva careers developed focused on one's motivation. This can be seen in the Tibetan ... non-regression), (4) Ekajātipratibaddha ("separated by only one lifetime from buddhahood"). Drewes notes that Mahāyāna sūtras ...
The existence of heteroscedasticity is a major concern in regression analysis and the analysis of variance, as it invalidates ... Thus, regression analysis using heteroscedastic data will still provide an unbiased estimate for the relationship between the ... The econometrician Robert Engle was awarded the 2003 Nobel Memorial Prize for Economics for his studies on regression analysis ... Fox, J. (1997). Applied Regression Analysis, Linear Models, and Related Methods. California: Sage Publications. p. 306. (Cited ...
doi:10.1111/j.1466-8238.2007.00358.x. Fawcett, Tom (2006); An introduction to ROC analysis, Pattern Recognition Letters, 27, ... Dodd, Lori E.; Pepe, Margaret S. (2003). "Partial AUC Estimation and Regression". Biometrics. 59 (3): 614-623. doi:10.1111/1541 ... especially for spatially explicit analyses. Some features of the AUC that draw criticism include the fact that 1) AUC ignores ... "A suite of tools for ROC analysis of spatial models". ISPRS International Journal of Geo-Information. 2 (3): 869-887. doi: ...
Automated testing List of unit testing frameworks List of tools for static code analysis Regression testing Software testing ... The product includes technology for Data-flow analysis, Unit test-case generation and execution, static analysis, and more. ... Jtest is an automated Java software testing and static analysis product developed by Parasoft. ... Static program analysis tools, Unit testing, Unit testing frameworks). ...
... of Adaptive Regression (with Yadolah Dodge, Springer, 2000), of Robust Statistical Methods with R (with Jan Picek, Chapman & ... Nonparametrics and Robustness in Modern Statistical Inference and Time Series Analysis: A Festschrift in honor of Professor ...
He is known for rigorous methodological work on latent variable models and is a proponent of integrative data analysis, a meta- ... doi:10.1207/s15328007sem1204_1 Bauer, D.J. & Curran, P.J. (2005). Probing interactions in fixed and multilevel regression: ... Patrick J. Curran Integrative Data Analysis and Big Data, National Cancer Institute University teaching awards Conference ... Bauer has published widely in factor analysis, multilevel modeling, latent growth curves, mixture models, latent class models, ...
Clark is the coauthor of: Applied Statistics: Analysis of Variance and Regression (with Olive Jean Dunn, 1974; 3rd ed. with ... with Clark, 2009) Practical Multivariate Analysis (with Abdelmomem Afifi and Susanne May, 5th ed., 2012) Clark became a Fellow ... Computer-Aided Multivariate Analysis (with Abdelmomem Afifi, 1984; 4th ed. with Susanne May, 2004) Processing Data: The Survey ... Reviews of Computer-Aided Multivariate Analysis: Fang, Kai-tai, zbMATH, Zbl 0888.62050{{citation}}: CS1 maint: untitled ...
Its analysis is based on approximately 40 million Medicare discharges for the most recent three-year time period available. ... Specifically, most ratings are determined from multivariate logistic regressions of medical outcomes at a given healthcare ... Rachel Brand (October 16, 2004). "Analysis gives Health Grades flunking marks". Rocky Mountain News. Archived from the original ...
Therefore, Welzel sees the current autocratization trend as regression to the mean, but expects that it too will reverse in ... A 2018 analysis by political scientists Yascha Mounk and Jordan Kyle links populism to democratic backsliding, showing that ... Diamond, Larry (15 September 2020). "Democratic regression in comparative perspective: scope, methods, and causes". ... democratic regression, and democratic deconsolidation. Skaaning, Svend-Erik (2020). "Waves of autocratization and ...
Edwards, J.R. (2002). "Alternatives to difference scores: Polynomial regression analysis and response surface methodology". In ... The polynomial regression equation commonly used in person-environment fit research is as follows: Z = β 0 + β 1 E + β 2 P + β ... Polynomial regression involves using measures of the person and environment along with relevant higher-order terms (e.g., the ... Studies using polynomial regression have found that the restrictive assumptions underlying difference scores are usually ...
... using polynomial regression) had been published by him three years earlier. Bill Cooper proposed logistic regression for the ... sentiment analysis, and online advertising. A possible architecture of a machine-learned search engine is shown in the ... Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict the score ... Then the learning-to-rank problem can be approximated by a regression problem - given a single query-document pair, predict its ...
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. ... "SARAMA: a bioinformatics tool for Structure validation , Protein structure analysis". omictools. Group, Professor Emil Alexov ...
Leith, D. J.; Zhang, Yunong; Leithead, W. E. (2005). "Time-series Gaussian Process Regression Based on Toeplitz Computation of ... This is a comparison of statistical analysis software that allows doing inference with Gaussian processes often using ... Roustant, Olivier; Ginsbourger, David; Deville, Yves (2012). "DiceKriging, DiceOptim: Two R Packages for the Analysis of ... Data Analysis. 153: 107081. arXiv:1906.07828. doi:10.1016/j.csda.2020.107081. ISSN 0167-9473. S2CID 195068888. Retrieved 1 ...
Support vector regression, support vector machine, linear discriminant analysis) Density-based Methods (Bayes nets, Markov ... Frequently used methods for description filtering include factor analysis (e.g. by PCA), singular value decomposition (e.g. as ... The list of applicable classifiers includes the following: Metric approaches (Cluster analysis, vector space model, Minkowski ... MMIR methods are, therefore, usually reused from other areas such as: Bioinformation analysis Biosignal processing Content- ...
Matlab code for computing R/S, DFA, periodogram regression and wavelet estimates of the Hurst exponent and their corresponding ... Kamenshchikov, S. (2014). "Transport Catastrophe Analysis as an Alternative to a Monofractal Description: Theory and ... Many physical phenomena that have a long time series suitable for analysis exhibit a Hurst exponent greater than 1/2. For ...
There are also two cross-test scores that each range from 10 to 40 points: Analysis in History/Social Studies and Analysis in ... Domigue, Ben; Briggs, Derek C. (2009). "Using Linear Regression and Propensity Score Matching to Estimate the Effect of ... In its analysis of the incident, the Princeton Review supported the idea of curving grades, but pointed out that the test was ... An early meta-analysis (from 1983) found similar results and noted "the size of the coaching effect estimated from the matched ...
The potential outcomes and regression analysis techniques handle such queries when data is collected using designed experiments ... research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis Data analysis ... Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to ... Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. ...
... of all breast cancer cases underwent spontaneous regression. Everson and Cole offered as explanation for spontaneous regression ... in a meta-analysis, investigated about 1000 cases Turner, in a qualitative research study, conducted interviews with 20 ... The spontaneous regression and remission from cancer was defined by Everson and Cole in their 1966 book as "the partial or ... For one, not all cases of spontaneous regression can be apprehended, either because the case was not well documented or the ...
According to an FTC analysis, the existing framework, consisting of the FTC Act, the Fair Credit Reporting Act, and the ... A wide variety of machine learning techniques have been used in IoT domain ranging from traditional methods such as regression ... The connectivity enables health practitioners to capture patient's data and applying complex algorithms in health data analysis ... data collection and analysis for research, and monitoring. The IoMT has been referenced as "Smart Healthcare", as the ...
... such as Principal Component Analysis (PCA) or Partial Least Squares regression (PLS), to estimate the oil acidity. The ... Free acidity is a defect of olive oil that is tasteless and odorless, thus can not be detected by sensory analysis. Since ... The main advantage of NIR spectroscopy is the possibility to carry out the analysis on raw olive oil samples, without any ... Many commercial spectrophotometers exist that can be used for analysis of different quality parameters in olive oil. ...
... but regression analysis allows us to account for variables that may explain differences. For example, suppose that we are ... Jurimetrics Office of Fair Housing and Equal Opportunity Regression analysis Simpson's paradox#UC Berkeley gender bias EEOC v. ... Then we may construct a multiple regression model for pay y {\displaystyle y} as: y = β 0 ⏟ Intercept + ∑ i = 1 p β i x i ⏟ ... After correction for the potentially confounding variables in a regression model, we should be able to tell if there is still ...
The 2012 documentary Room 237 featured film analysis by fans of Stanley Kubrick's 1980 film The Shining, connecting Kubrick's ... "Regression and Debasement of Science - on the Apollo Moon Landings". Archived from the original on April 17, 2015. Retrieved ...
Rosenbaum's study was innovative for using Rubin causal model matching, instead of relying on regression analysis, which makes ... Their analysis was that identity movements work when there is a critical mass of members: too few members, and people don't ...
... regression and survival analysis, neural networks in statistics. + PEREIRA, B. de B,;RAO Calyampudi Radhakrishna; Oliveira, F.B ...
GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial ... GeoDa - Exploratory Data Analysis and Spatial Regression. GeoDa is a free software package that conducts spatial data analysis ... GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local ... and basic spatial regression analysis for point and polygon data (tens of thousands of records) ...
The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners ... coverage of quantile count regression, and a new chapter on Bayesian methods. ... Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits ... "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, October. ...
Bug 45813 - [4.5 Regression] alias analysis problem with -mthumb Summary: [4.5 Regression] alias analysis problem with -mthumb ... GCC Bugzilla - Bug 45813 [4.5 Regression] alias analysis problem with -mthumb Last modified: 2012-07-02 10:46:12 UTC ... It looks like the alias analysis knows that bytes points to val but doesnt know that bytes+1 points to a part of val also, so ...
Effect of trans-fatty acid intake on blood lipids and lipoproteins: a systematic review and meta-regression analysis  ...
Joinpoint analysis. We used joinpoint regression analysis to assess the slope of CRC mortality trends in urban and rural China ... an age-period-cohort analysis and a joinpoint regression analysis. Chin J Cancer 2016;35(1):55. CrossRefexternal icon PubMed ... We used joinpoint regression analysis to estimate the slope of mortality trends. We then used the age-period-cohort (APC) model ... For this analysis, we substituted 2002 data for 2000 data. We fitted the joinpoint regression on a log scale because CRC ...
A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides ... Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly ... As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional ... The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can ...
We identied 69 studies and applied meta-analysis, meta-regression and dose-response analysis ... We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. ... area: P = 0.454). erefore, we took the 6 estimates into meta-analysis. e result of the pooled analysis showed ... inuencing factors by meta-regression analyses. And the robust outcomes of sensitivity analysis suggest that there ...
Regression analysis is used to model the relationship between a response variable and one or more predictor variables. Learn ... Regression Analysis. Regression analysis is used to model the relationship between a response variable and one or more ... Regression Analysis for Proportions. When the response variable is a proportion or a binary value (0 or 1), standard regression ... Zero-Inflated Count Regression (Version 19). The Zero Inflated Count Regression procedure is designed to fit a regression model ...
This paper estimates the impacts of secondary school on human capital, occupational choice, and fertility for young adults in Kenya. The probability of admission to .
... in a proportional hazards regression analysis was evaluated using Monte Carlo simulation techniques for data from a randomized ... The analytical effect of the number of events per variable (EPV) in a proportional hazards regression analysis was evaluated ... Below this value for EPV, the results of proportional hazards regression analyses should be interpreted with caution because ... Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of ...
Argentina constitutes an interesting case for the analysis of the labour market given that during the nineties it reached high ... Maurizio, Roxana and Monsalvo, Ana P., Unemployment Duration and Business Cycle in Argentina: A Quantile Regression Analysis ( ... Censored quantile regressions will be used in order to estimate in a more flexible and robust way the effect of covariates on ... Argentina constitutes an interesting case for the analysis of the labour market given that during the nineties it reached high ...
The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non- ... The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non- ...
Regression Analysis» at to see how a worthy paper should be produced. ... Regression AnalysisIn the field of statistics regression analysis refers to the techniques for modeling as well as analyzing ... The target is regression function and probability distribution. Used widely for prediction and forecasting, regression analysis ... this line is called the regression line". Methods of simple regression and linear regressions were clearly explained in the ...
The Neural Networks classification, the Bayesian Decision Making and the Nonparametric Regression approaches are used and ... Based on a special data analysis methodology developed for non-direct multivariate experiments, we present the expected ... On the Nonparametric Classification and Regression Methods for Multivariate EAS Data Analysis *Chilingarian, A. ... Based on a special data analysis methodology developed for non-direct multivariate experiments, we present the expected ...
Linear Regression Analysis using Python: ML Basics (हिंदी) ... Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression ... Section 5 - Regression Model. This section starts with simple linear regression and then covers multiple linear regression.. We ... Linear Regression Analysis using Python: ML Basics (हिंदी). हिंदी में सीखें Basics of Machine Learning - covers Simple Linear ...
... regression analysis is a statistical technique that is used in the analysis of structural equations. ... Regression Analysis Two-Stage Least Squares (2SLS) Regression Analysis. Two-Stage least squares (2SLS) regression analysis is a ... Click on the "analysis" menu and select the "regression" option.. *Select two-stage least squares (2SLS) regression analysis ... Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the ...
Analyzing Nonresponse in Longitudinal Surveys Using Bayesian Additive Regression Trees: A Nonparametric Event History Analysis ... The innovative approach of Bayesian additive regression trees (BART) is an elegant way to overcome these limitations because it ... We present a BART event history analysis that allows identifying predictors for different types of nonresponse to anticipate ... A cross-validation and comparison with logistic regression models with least absolute shrinkage and selection operator ...
Physical Activity and Pediatric Obesity: A Quantile Regression Analysis. Medicine and science in sports and exercise. 2017 Mar ... Physical Activity and Pediatric Obesity : A Quantile Regression Analysis. In: Medicine and science in sports and exercise. 2017 ... Physical Activity and Pediatric Obesity: A Quantile Regression Analysis. Jonathan A. Mitchell, Marsha Dowda, Russell R. Pate, ... Dive into the research topics of Physical Activity and Pediatric Obesity: A Quantile Regression Analysis. Together they form ...
Fourier analysis versus multiple linear regression to analyse pressure-flow data during artificial ventilation. R Peslin, C ... The 5% flow offset did not modify the results of Fourier analysis, but increased Rrs and Ers from linear regression by 15.8 ... Fourier analysis versus multiple linear regression to analyse pressure-flow data during artificial ventilation ... Fourier analysis versus multiple linear regression to analyse pressure-flow data during artificial ventilation ...
Statistical analyses. Segmented time series regression analyses were used. 1998 was selected a priori as the starting time ... Have e-cigarettes renormalised or displaced youth smoking? Results of a segmented regression analysis of repeated cross ... Have e-cigarettes renormalised or displaced youth smoking? Results of a segmented regression analysis of repeated cross ... from binary logistic regression analyses (the top lines represent 15 year olds, the bottom 13 year olds). ...
Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials ... In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant ... Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective ... Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials ...
LINEAR REGRESSION LINESOverviewLinear regression is a statistical tool used to predict future values from past values. ... Linear regression analysis is the statistical confirmation of these logical assumptions.. A Linear Regression trendline is ... A Linear Regression trendline shows where equilibrium exists. Linear Regression Channels show the range prices can be expected ... A popular method of using the Linear Regression trendline is to construct Linear Regression Channel lines. Developed by Gilbert ...
We applied regression analysis to time-series data on vehicles, population and traffic fatalities in the United Arab Emirates ( ... Using regression analysis and data from Qatar for the period 1990-2004 [22], a Smeed-type formula of the following form, which ... Regression analysis can be applied to estimate road fatalities in Qatar although adjustment of bias is one of the weaknesses. ... We applied regression analysis to time-series data on vehicles, population and traffic fatalities in the United Arab Emirates ( ...
... based subspace learning technique that simultaneously preserves the intra-class regression ... A common challenge when dealing with heterogenous tasks such as face expression analysis, face and object recognition is high ... A common challenge when dealing with heterogenous tasks such as face expression analysis, face and object recognition is high ... Our method leverages the multi-dimensional image labels that quantify the within class regression to learn the subspaces for ...
The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory ... The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter ... A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients ... In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures ...
Academic performance of children born preterm: a meta-analysis and meta-regression ... Academic performance of children born preterm: a meta-analysis and meta-regression ...
As was discussed earlier, regression analysis requires that your data meet specific assumptions. Otherwise, the regression ... Performing and Assessing Regression Analysis. *Project 5: Examine Relationships in Data: Regression Diagnostic Utilities: ... Now youve walked through a regression analysis and learned to interpret its outputs. Youll be pleased to know that there is ... Project 5: Examine Relationships in Data: Performing and Assessing Regression Analysis up Project 5: Finishing Up › ...
Often, researchers get confused between different methods of data analysis and how they can go about using them. To help you ... will take you through some common methods of statistical analysis. ... better, we have invited an experienced biostatistician to simplify statistical analysis for you. In a two-part series, Prof. Jo ... Røislien will talk about two types of regression analysis - univariate regression analysis and multivariable regression ...
  • Methods of simple regression and linear regressions were clearly explained in the works of Waner S who also brought up the Regression Calculator. (
  • Most least squares regression programs are designed to fit models that are linear in the coefficients. (
  • large sample properties did not hold for variance estimates from the proportional hazards model, and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis. (
  • A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. (
  • However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. (
  • It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. (
  • Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. (
  • Because of this endogeneity, significant correlation can exist between the unobserved factors contributing to both the endogenous independent variable and the dependent variable, which results in biased estimators (incorrect regression coefficients) ( 2 ). (
  • There was found a good correlation between the measured data and the model results with regression coefficients of 0.9. (
  • Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018. (
  • Multivariate logistic regression model was used to determine the association between stunting , underweight and wasting given that of the estimated effect of other determinants. (
  • Next, we conducted a sex-specific analysis for obesity and its associated factors using backward elimination multivariate logistic regression models. (
  • In a multivariate logistic regression analysis, the type of AMI was classified based on electrocardiography findings [‎odds ratio 5.18, 95% confidence interval: 1.69-15.91, P=0.004]‎ and was independently associated with a long prehospital delay time, indicating that patients with ST segment elevation MI would seek early medical care. (
  • The association between childhood sexual violence and several potential demographic and social risk factors was explored through bivariate and multivariate logistic regression. (
  • The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non-linear functional forms, heteroskedasticity, complex survey designs, and real-world statistical applications in nutrition science and policy. (
  • The data are preprocessed by subtracting off a linear regression fit, followed by normalization of all features to unit variance. (
  • The quadratic regression resulted in variance explanations of greater magnitude when compared to the linear model. (
  • In addition you also should understand that extreme values already have more weight with variance-based analysis methods (i.e. regression analysis, Anova, factor analysis, etc.) since since distances are computed as squares. (
  • The project team used the Analysis of Variance (ANOVA) tools in Minitab's Assistant menu to investigate the relationship between revenue and the roles of different sales team members. (
  • Below this value for EPV, the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid. (
  • Methods The authors conducted Cox proportional hazards regression analyses of lung cancer risk with cumulative, mean and maximum 'daily weighted average' (DWA) exposure among 5436 workers, using age-based risk sets. (
  • The user may include all predictor variables in the fit or ask the program to use a stepwise regression to select a subset containing only significant predictors. (
  • If the number of predictors is not excessive, it is possible to fit regression models involving all combinations of 1 predictor, 2 predictors, 3 predictors, etc, and sort the models according to a goodness-of fit statistic. (
  • Partial Least Squares is designed to construct a statistical model relating multiple independent variables X to multiple dependent variables Y. The procedure is most helpful when there are many predictors and the primary goal of the analysis is prediction of the response variables. (
  • The innovative approach of Bayesian additive regression trees (BART) is an elegant way to overcome these limitations because it does not specify a parametric form for the relationship between the outcome and its predictors. (
  • We present a BART event history analysis that allows identifying predictors for different types of nonresponse to anticipate response rates for upcoming survey waves. (
  • You will also be asked to interpret regression output to understand overall model performance and importance of different predictors, as well as make predictions using the appropriate regression model. (
  • The practice of choosing predictors for a regression model, called model building , is an area of real craft. (
  • We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation, and correlation. (
  • In Episode Two of this series, Prof. Røislien will talk about two types of regression analysis - univariate regression analysis and multivariable regression analysis. (
  • Topic: Know thy data (Episode 2) - Looking at univariate and multivariable regression analyses. (
  • Univariate meta-regression showed an association between retinopathy and average HbA1c reduction during the overall follow-up (slope=0.77, p=0.007), but no relationship for SBP or weight. (
  • Univariate and multivariate logistic regressions were performed to identify variables associated with postoperative neurological deficits and a DWI signal. (
  • Univariate and multiple linear regression analyses were used to answer the three research questions. (
  • Identify the business problem which can be solved using the linear regression technique of Machine Learning. (
  • 1.) D. Linear regression is all about the residuals. (
  • The module also introduces the notion of errors, residuals and R-square in a regression model. (
  • Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. (
  • The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals . (
  • In other words: regression analysis tries to find a line that will maximize prediction and minimize residuals. (
  • If you find yourself wondering if there is a correlation or relationship between variables, then regression analysis might be worth exploring. (
  • Additionally, the correlation between the dependent variables can create significant multicollinearity, which violates the assumptions of standard regression models and results in inefficient estimators. (
  • The "red" student who uses the computer for very long hours will lead to a positive correlation and positive regression rate, whereas the "black" ones alone in the data suggest an inexistent correlation. (
  • Correlation and regression analysis was performed. (
  • A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. (
  • Pearson correlation test and stepwise regression analysis were used to analyze. (
  • Multivariable logistic regression was undertaken to examine associations between exposure to Cannabis dust (classified as low, medium, and high) and health symptoms. (
  • Multivariable logistic regression models were used to identify socioeconomic, psychosocial, and treatment factors associated with =5% weight gain over 2-year follow-up. (
  • We used joinpoint regression analysis to estimate the slope of mortality trends. (
  • Undergoing elective surgery on the weekend was associated with a 1.96 times higher odds of 30-day mortality than weekday surgery (95% confidence interval, 1.36-2.84) in a propensity-matched analysis. (
  • According to a recent large cohort analysis presented at the American College of Chest Physicians 2022 Annual Meeting, an association was found between exposure to probiotics in the ICU and a measurable increase in bacteremia and bacteremia-related mortality. (
  • Charvat H, Belot A. mexhaz: an R package for fitting flexible hazard-based regression models for overall and excess mortality with a random effect. (
  • GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. (
  • It's designed to offer new insights from data analysis by exploring and modeling spatial patterns. (
  • GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation. (
  • The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. (
  • The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods. (
  • Regression Analysis of Count Data ," Cambridge Books , Cambridge University Press, number 9781107014169. (
  • This repository includes data for snap analyses of the 2018 Midterm Elections using unofficial election returns data. (
  • As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. (
  • Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. (
  • Based on a special data analysis methodology developed for non-direct multivariate experiments, we present the expected accuracies of the KASCADE experiment on the elemental composition and primary energy estimation. (
  • In this short course we will provide an introduction to linear regression and how to utilize it in R. We will cover the theory of linear regression as well as demonstrating how to use R to make and interpret test statistics and data plots for a built in R data set. (
  • We will have time at the end of the course to answer any specific questions about linear regressions for participants' data sets. (
  • Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. (
  • In this section, you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. (
  • Quantile regression was used to analyze the data. (
  • The course provides learners with exposure to essential tools including exploratory data analysis, as well as regression methods that can be used to investigate the impact of marketing activity on aggregate data (e.g., sales) and on individual-level choice data (e.g., brand choices). (
  • In this module, you will be asked to determine the appropriate type of regression for different types of marketing data and will perform regression analysis to assess the impact of marketing actions on outcomes of interest, such as sales, traffic, and brand choices. (
  • We applied regression analysis to time-series data on vehicles, population and traffic fatalities in the United Arab Emirates (UAE), Jordan and Qatar. (
  • Design Interrupted time-series analysis of repeated cross-sectional time-series data. (
  • In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. (
  • A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). (
  • The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. (
  • As was discussed earlier, regression analysis requires that your data meet specific assumptions. (
  • Often, researchers get confused between different methods of data analysis and how they can go about using them. (
  • The selected model fits the data well except for very few discrepant or outlying data values, which may have greatly influenced the choice of the regression line. (
  • Regression analysis is useful in statistics as it allows us to identify trends in the data. (
  • In my example of regression analysis basics, I will be using Excel and Tableau to briefly walk you through how easy it is to perform linear regression on a sample data set that has recorded the average snowfall from 2004 to 2017, as well as how you can use linear regression to predict the average snowfall for future years. (
  • Essentially, regression is your "best guess" at predicting what might happen in the future by using the data available to you. (
  • The purpose of this page is to show how to use various data analysis commands. (
  • In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses. (
  • For our data analysis below, we are going to expand on Example 3 about applying to graduate school. (
  • Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. (
  • Note: This course uses the 'Data Analysis' tool box which is standard with the Windows version of Microsoft Excel. (
  • At the end of this module, you'll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions. (
  • To accommodate such heterogeneity, we propose a concave fusion approach to identifying the subgroup structures and estimating the treatment effiects for a semiparametric linear regression with censored data. (
  • Hierarchical linear models : applications and data analysis methods / Stephen W. Raudenbush, Anthony S. Bryk. (
  • Estudios Clínicos Latino América (ECLA) Foundation (Rosario, Argentina) covered all the costs related to the data collection, statistical analyses and writing of the manuscript. (
  • The results evolved from the neural network training were compared with the results of regression model and experimental data. (
  • The output from Neural Network approach had greater consistency with the experimental data than the output from conventional regression analysis. (
  • In addition to their data types, many statistical analysis types only work for given sets of data distributions and relations between variables. (
  • In practical terms this means that not only you have to adapt your analysis techniques to types of measures but you also (roughly) should respect other data assumptions. (
  • The goal of statistical analysis is quite simple: find structure in the data. (
  • Data was collected using employee engagement inventory, psychological empowerment, and commitment to change inventory, and was analysed using descriptive analysis and SEM. (
  • In regression analysis , the coefficient of determination is a measure of goodness-of-fit (i.e. how well or tightly the data fit the estimated model). (
  • This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models. (
  • Methods: Structured additive regression models using Bayesian inference based on Markov chain Monte Carlo (MCMC) simulation techniques were fitted using age standardized FBCM rates and county level attributes data obtained from Surveillance Epidemiology & End Results (SEER) program for the years 1990 to 2014. (
  • Statistical Analysis and Data Mining, 7(4): 272-281 (2014). (
  • With the data in Minitab Statistical Software, the team used regression analysis to understand the relationship between sales operations and revenue. (
  • After their initial analysis revealed that optimal revenue could be achieved by combining a higher transfer rate with a lower turnover in contractors and services, the team began digging deeper into their sales operation data to determine where process changes could be made. (
  • Research Design - A retrospective, propensity score-matched cohort analysis of linked population-based health administrative data was carried out. (
  • We were also unable to replicate some of the data used in the meta-analysis. (
  • First-line ovulation induction for polycystic ovary syndrome: an individual participant data meta-analysis. (
  • Subsequent analysis of that data has observation that animals in the house can decrease the risk of shown that the significant difference in the response to cat asthma. (
  • Linear regression analysis is the statistical confirmation of these logical assumptions. (
  • Multiple linear regression analysis is a method for estimating the effects of several factors concurrently. (
  • We used SAS 9.3 for the statistical analyses and to account for the complex sampling design. (
  • We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity. (
  • We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity.RESULTS: We included 19 studies on 589,649 participants (2040 incident dementia cases) followed up for up to 42 years. (
  • All meta-analyses were carried out using STATA V.8 software. (
  • Random-effects meta-analyses examined perinatal outcomes associated with preconception and antenatal ART initiation as well as according to trimesters of antenatal initiation. (
  • This systematic review and meta-analyses is registered with PROSPERO, number CRD42021248987. (
  • Multinomial logistic regression: This is similar to doing ordered logistic regression, except that it is assumed that there is no order to the categories of the outcome variable (i.e., the categories are nominal). (
  • Multinomial regression assessed the associations between maternal metabolic parameters and offspring's BMI trajectories. (
  • This idea gets back to one of the assumptions of regression, which is that the errors of prediction are normally distributed (homoscedasticity). (
  • OLS regression: This analysis is problematic because the assumptions of OLS are violated when it is used with a non-interval outcome variable. (
  • A review of surveys conducted to date reveals considerable variation in both the choice and use of survey methods and in the assumptions made in the analysis and interpretation of findings. (
  • Censored quantile regressions will be used in order to estimate in a more flexible and robust way the effect of covariates on the conditional distribution of duration. (
  • The mathematical technique used to estimate this trend-regression model is the Kaiman filter. (
  • We will build a regression model and estimate it using Excel. (
  • Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. (
  • In a two-part series, Prof. Jo Røislien, professor of medical statistics at the Faculty of Health Sciences at the University of Stavanger, will take you through some common methods of statistical analysis. (
  • Join this webinar to understand how and when you can use both methods of statistical analysis. (
  • abstract = "INTRODUCTION: We conducted a meta-analysis of the conflicting epidemiologic evidence on the association between midlife body mass index (BMI) and dementia.METHODS: We searched standard databases to identify prospective, population-based studies of dementia risk by midlife underweight, overweight, and obesity. (
  • Below is a list of some analysis methods you may have encountered. (
  • Historically, the majority of the clinical screening methods consisted of surveys developed using logistic regression analyses to predict diabetes [ 8 - 13 ]. (
  • The simplest regression models involve a single response variable Y and a single predictor variable X. STATGRAPHICS will fit a variety of functional forms, listing the models in decreasing order of R-squared. (
  • In STATGRAPHICS, the Regression Model Selection procedure implements such a scheme, selecting the models which give the best values of the adjusted R-Squared or of Mallows' Cp statistic. (
  • A cross-validation and comparison with logistic regression models with least absolute shrinkage and selection operator penalization underline the advantages of the approach. (
  • In all models and subgroup analyses for smoking attitudes, an increased rate of decline was observed after 2010 (OR 0.88, CI 0.86 to 0.90). (
  • Models were robust to sensitivity analyses. (
  • Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. (
  • We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. (
  • The lecture are very exciting and detailed, though little hard and too straight forward sometimes, but Youtube helped in Regression models. (
  • Spline regression models / Lawrence C. Marsh, David R. Cormier. (
  • Considerations regarding the selection of regression models in the academic context and in organizational practice are provided. (
  • The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences. (
  • An often overlooked problem in building statistical models is that of endogeneity, a term arising from econometric analysis, in which the value of one independent variable is dependent on the value of other predictor variables. (
  • Because the strain is in part determined by the presence of these toxins, including both strain and genotype in the model means that the standard errors for variables for the Shiga-containing strains and bloody diarrhea symptom are likely to be too high, and hence the significance levels (p values) obtained from the regression models are higher than the true probability because of a type I error. (
  • Mathematical models for the surface area of secondary clarifier were developed for wastewater generated from a dairy industry and from domestic sources, by correlating the parameters namely, surface area per unit flow rate (A/Q), influent concentration (C(O)), underflow concentration (C(U)), recycling ratio (r) and Mean Cell Residence Time (theta C) using multiple regression analysis. (
  • We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. (
  • In linear regression (see LINEAR MODELS ) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. (
  • In logistic regression (see LOGISTIC MODELS ) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. (
  • It will also be a useful foil for conventional texts for the teaching of the regression model. (
  • Regression analysis is used to model the relationship between a response variable and one or more predictor variables. (
  • आप एक पूर्ण Linear Regression course की तलाश कर रहे हैं जो आपको वह सब कुछ सिखाता है जो आपको Python में Linear Regression model बनाने के लिए चाहिए, है ना? (
  • Create a linear regression model in Python and analyze its result. (
  • And after running an analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. (
  • This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. (
  • So what we have to do instead is make the assumption that that zero, one outcome comes from a Bernoulli distribution governed by a particular probability and we're going to use logistic regression to develop this particular model. (
  • Respiratory resistance (Rrs) and elastance (Ers) are commonly measured in artificially-ventilated patients or animals by multiple linear regression of airway opening pressure (Pao) versus flow (V') and volume (V), according to the first order model: Pao = P0 + Ers.V + Rrs.V', where P0 is the static recoil pressure at end-expiration. (
  • 5) Consider a linear regression model with the predictor variables X1, X2, and X3. (
  • Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression. (
  • For more than one explanatory variable, then the model is called multiple linear regression. (
  • It is recommended at least 10 times as many cases as the number of independent variables in regression model. (
  • This factor regression tool supports factor regression analysis of individual assets or a portfolio of assets using the given risk factor model. (
  • WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. (
  • nag_correg_mixeff_reml ( g02ja ) fits a linear mixed effects regression model using restricted maximum likelihood (REML). (
  • Fit the neighborhood component analysis model for regression. (
  • This study presents comparative analysis of multiple linear regression model and quadratic regression. (
  • To identify factors associated with the development of Pulmonary embolism, a multivariable Binary Logistic Regres- sion model with sensitivity analysis was run. (
  • Meta-analysis of the natural logarithm of age-standardised M:F ratio (logSR) estimates was performed using a DerSimonian and Laird random effects model. (
  • In particular, the treatment effiects are subject-dependent and subgroup-specific, and our concave fusion penalized method conducts the subgroup analysis without needing to know the individual subgroup memberships in advance. (
  • We performed quality assessments and subgroup and sensitivity analyses, and assessed the effect of adjustment for confounders. (
  • Therefore, in this study, our aim is to provide a systematic review and meta-analysis of both categorical and continuous risk factors for suicidal ideation, suicide attempts, and suicide. (
  • The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. (
  • Li M, Sun Y, Yang J, de Martel C, Charvat H, Clifford GM, Vaccarella S, Wang L. Time trends and other sources of variation in Helicobacter pylori infection in mainland China: A systematic review and meta-analysis. (
  • The Neural Networks classification, the Bayesian Decision Making and the Nonparametric Regression approaches are used and compared. (
  • The dependent variable should be a numeric variable in linear regression. (
  • This blog focuses on simple linear regression, which is when you have one independent variable (x-variable) for a single dependent variable (y-variable). (
  • In multiple regression, the dependent variable is considered to depend on more than a single independent variable. (
  • Of 51 874 unique citations, 25 studies (eight prospective and 17 retrospective cohort studies) were eligible for analysis, including 40 920 women living with HIV. (
  • In the field of statistics regression analysis uses the techniques of modeling and analyzing multiple variables focusing on the relationships between dependent and independent variables helping the analyst to understand how the change of criterion in one independent variable affects the criterion of other dependent variables. (
  • Regression Analysis In the field of statistics regression analysis refers to the techniques for modeling as well as analyzing multiple variables. (
  • There are more advanced techniques like multiple linear regression, which is when you have multiple dependent variables for a single independent variable. (
  • The multiple linear regression indicates how well the returns of the given assets or a portfolio are explained by the risk factor exposures. (
  • There are numerous occasions where the use of multiple regression analysis is appropriate. (
  • An index based on multiple logistic regression that combined optic disc variables with axial length was also explored with the aim of improving diagnostic accuracy of disc variables. (
  • Axial length adjustments to disc variables in the form of multiple logistic regression indices led to a slight but insignificant improvement in diagnostic accuracy. (
  • Multiple and generalized nonparametric regression / John Fox. (
  • Besides, a new factor (brain metastasis) was identified by 1:1 PSM -based multiple Cox regression, apart from the above prognostic factors for OS. (
  • Used widely for prediction and forecasting, regression analysis is also used for exploring relationships. (
  • So, instead of relying on common sense or our gut instinct to make a prediction, we can utilize regression analysis. (
  • Unlike other regression procedures, estimates can be derived even in the case where the number of predictor variables outnumbers the observations. (
  • A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. (
  • Bonjour M, Charvat H, Franco EL, Piñeros M, Clifford GM, Bray F, Baussano I. Global estimates of expected and preventable cervical cancers among girls born between 2005 and 2014: a birth cohort analysis. (
  • This means that the regression equation for standardized variables is:Y' = rX. (
  • First, compute the predicted values of y using the regression equation and store them as a new variable in Poverty_Regress called Predicted. (
  • If you wanted to be more accurate, simply plug in and x value (the year) into the regression equation. (
  • If you hover your mouse over the trend line you are presented with summary statistics such as the regression equation, R-squared value and the p-value. (
  • The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. (
  • The 5% flow offset did not modify the results of Fourier analysis, but increased Rrs and Ers from linear regression by 15.8 +/- 4.6% and 4.55 +/- 0.64%, respectively. (
  • The study presents the preliminary results of the analysis on the roles of non-profit organisations in the marketing of settlements regarding their performance in terms of creativity, innovativeness and competitiveness. (
  • Provide a brief interpretation and analysis of the results of the regression for the given table. (
  • Results of search for 'ccl=su:{Regression analysis. (
  • Towards the end of module we introduce the 'Dummy variable regression' which is used to incorporate categorical variables in a regression. (
  • Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. (
  • You get to understand the interpretation of Regression output in the presence of categorical variables. (
  • REGRESSION coefficient in ORDERED categorical LOGISTIC analysis. (
  • Several techniques have been evolved including linear regression, ordinary least square regression, and nonparametric regression. (
  • We also performed a cohort study meta-analysis as a supplemental analysis. (
  • The first two were also significant in the cohort meta-analysis. (
  • g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. (
  • Future studies may combine the above-mentioned variables by using multivariate predictive analysis techniques to objectively stratify suicidality in schizophrenia. (
  • In the process he also displays the best fitting straight line with a slope of 0.481 and Y intercept of 15.8468 and the regression line can be used for predicting. (
  • is the slope of the regression line. (
  • Two-Stage least squares (2SLS) regression analysis is a statistical technique that is used in the analysis of structural equations . (
  • Select two-stage least squares (2SLS) regression analysis from the regression option. (
  • A Linear Regression trendline uses the least squares method to plot a straight line through prices so as to minimize the distances between the prices and the resulting trendline. (
  • A Linear Regression trendline is simply a trendline drawn between two points using the least squares fit method. (
  • This study reports a combined use of ordinary Fourier transform infrared spectroscopy (FT-IR) in conjunction with partial-least-squares (PLS) multivariate regression for accurate determination of the percent compositions of four essential oils (EOs) (wintergreen, tea tree, rosemary, and lemon eucalyptus oils) that were adulterated either with lemongrass essential oil or peppermint essential oil. (
  • The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. (
  • The current meta-analysis aims to determine risk factors associated with suicidality in subjects with schizophrenia. (
  • Sensitivity analyses for HbA1c showed a relationship at 3 months (p=0.006) and 1 year (p=0.002). (
  • Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. (
  • A powerful regression extension known as 'Interaction variables' is introduced and explained using examples. (
  • It built upon a long history of software development for spatial analysis. (
  • The search was car- ried out in October 2021, using the descriptors Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, in Virtual Health Library, Scopus, Web of Science, Portal CAPES. (
  • Spatial Analysis. (
  • Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, na Biblio- teca Virtual em Saúde, Scopus, Web of Science, Portal CAPES. (