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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Linear regression analysis is one of the most important statistical methods. It examines the linear relationship between a ... Regression analysis Marketing mix modeling Elasticities Multicollinearity Autocorrelation Outlier detection Endogeneity Sales ... Linear regression analysis is one of the most important statistical methods. It examines the linear relationship between a ... Skiera B., Reiner J., Albers S. (2018) Regression Analysis. In: Homburg C., Klarmann M., Vomberg A. (eds) Handbook of Market ...
X′X is nonsingular, if and only if the n × p matrix X has rank p (assuming n ≥ p). In regression models, the... ... A regression model is any general linear model, Y = Xβ - e where X′X is nonsingular. ... Christensen R. (1987) Regression Analysis. In: Plane Answers to Complex Questions. Springer Texts in Statistics. Springer, New ... A regression model is any general linear model, Y = Xβ - e where X′X is nonsingular. X′X is nonsingular, if and only if the n ...
Its fast, small, and very effective, and can be trained … - Selection from Regression Analysis with Python [Book] ... Summary Weve seen in this chapter how to build a binary classifier based on Linear Regression and the logistic function. ... Regression Analysis with Python by Alberto Boschetti, Luca Massaron. Stay ahead with the worlds most comprehensive technology ... Weve seen in this chapter how to build a binary classifier based on Linear Regression and the logistic function. Its fast, ...
Applied Regression Analysis, Part 766. Applied Regression Analysis, Norman Richard Draper. Probability and Statistics Series. ... procedure pure error regression analysis regression equation regression model residual sum Response variable ridge regression ... Regression_Analysis.html?id=7mtHAAAAMAAJ&utm_source=gb-gplus-shareApplied Regression Analysis. ... He is the coauthor (with Harry Smith) of Applied Regression Analysis, Third Edition, published by Wiley. ...
However, co-expression analysis using human cancer transcriptomic data is confounded by somatic copy number alterations (SCNA ... Co-expression analysis is widely used to predict gene function and to identify functionally related gene sets. ... "Genomic Regression Analysis of Coordinated Expression" (GRACE) to adjust for the effect of SCNA in co-expression analysis. The ... Genomic regression analysis of co-expression (GRACE) corrects for correlation bias due to copy number variation. a Relative ...
Insights now includes the ability to create a regression model,... ...
The difference between ... - Selection from Mastering Python Data Analysis [Book] ... Linear regression There are many different linear regression models built-in in Scikit-learn, Ordinary Least Squares (OLS) and ... Mastering Python Data Analysis by Luiz Felipe Martins, Magnus Vilhelm Persson. Stay ahead with the worlds most comprehensive ... There are many different linear regression models built-in in Scikit-learn, Ordinary Least Squares (OLS) and Least Absolute ...
Regression and ANOVA: An Integrated Approach Using SAS Software + Applied Statistics: Analysis of Variance and Regression, ... Statistical Regression with Measurement Error: Kendalls Library of Statistics 6 Chi-Lun Cheng, John W. Van Ness ... Regression and ANOVA: An Integrated Approach Using SAS Software Keith E. Muller, Bethel A. Fetterman ... Logistic Regression Using the SAS System: Theory and Application Paul D. Allison ...
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 ... It will also be a useful foil for conventional texts for the teaching of the regression model. I plan to use it for my students ...
Regression Analysis for Demand Estimation. 1065 Words , 5 Pages. *. Regression Analysis For A Dependence Method. 753 Words , 4 ... Regression Analysis. 1301 Words , 6 Pages. Introduction This presentation on Regression Analysis will relate to a simple ... Regression Analysis. 1447 Words , 6 Pages. REGRESSION ANALYSIS Correlation only indicates the degree and direction of ... More about Regression Analysis. *. Mlb Regression Analysis Data. 1212 Words , 5 Pages ...
Regression Analysis of Count Data. Series: Econometric Society Monographs (No. 30). A. Colin Cameron. University of California ... 1. Introduction; 2. Model specification and estimation; 3. Basic count regression; 4. Generalized count regression; 5. Model ... This analysis provides a comprehensive account of models and methods to interpret such data. The authors have conducted ... Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as ...
... This example will be removed in a future release. ... This example shows how to prepare exogenous data for several seemingly unrelated regression (SUR) analyses. The response and ... the number of regression variables (. nX. ), and whether to include different regression intercepts for each response series ... and specifying the number of response series and the number of regression variables. By default, vgxset. excludes regression ...
What is the regression you would run to estimate the effect of the change in the minimum wage? Answer: Regress Yi on a constant ... b. What regression would you run? Answer: Regress Yi on constant and Si . a. in both states. The second sentence is not a ... What regression would you do now? Answer: Regress Yi on a constant. 1 point for noting the interaction between NJ and time ... What regression would you run to estimate the effect of education on earnings to avoid ability bias? Answer: Regress (Yi2 − Yi1 ...
It expands on classical multivariate analysis and employs numerous examples. ... Bilinear Regression Analysis. Book Subtitle. An Introduction. Authors. * Dietrich von Rosen Series Title. Lecture Notes in ... This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class ... "The present book offers a complete presentation of the statistical techniques concerning bilinear regression analysis. … A ...
... shows that diagnostic statistics that are commonly provided with regression analysis lead to conf ... This overview examines the Soyer-Hogarth findings in light of prior research on illusions associated with regression analysis. ... shows that diagnostic statistics that are commonly provided with regression analysis lead to confusion, reduced accuracy, and ... Armstrong, J. Scott, Illusions in Regression Analysis (December 8, 2011). Available at SSRN: ...
... Brian A. Jacob, Lars Lefgren. NBER Working ... w13039 Regression Discontinuity Designs: A Guide to Practice. Lee and Lemieux. w14723 Regression Discontinuity Designs in ... "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," The Review of Economics and Statistics, MIT ... Using a regression discontinuity design, we find that the net effect of these programs was to substantially increase academic ...
In order to fill this gap, we perform a Meta-Regression-Analysis (MRA) by examining 1661 efficiency scores retrieved from 120 ... The meta-regression is estimated by using the Random Effects Multilevel Model (REML) because it controls for within- and ... The analysis yields four main results. First, parametric methods yield lower levels of banking efficiency than nonparametric ... "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57- ...
MATH 5813 Regression Analysis 3/0/3. This course involves a thorough examination of both simple linear regression models and ... FINC 6542 Investment Analysis and Portfolio Management 3/0/3 FINC 6561 International Management of Financial Institutions 3/0/3 ... PHED 8661 Critical Analysis of Professional Literature in Physical Education and Sport 3/0/3 ... NURS 6104 Scholarly Inquiry and Data Analysis in Nursing 3/0/3 ... NURS 6500 Data Analysis in Nursing 2/0/2 NURS 6501 Role of ...
Regression analysis is the analysis of the relationship between a response variable and another set of variables. The ... Regression analysis is the analysis of the relationship between a response variable and another set of variables. The ... Analysis Of The American Community Survey. 1580 Words , 7 Pages. Abstract This multiple regression project relies on secondary ... Regression Analysis : Correlation Between A Response Variable And Another Set Of Variables. 1224 Words5 Pages ...
... as it is the case in original robust ordinal regression methods. Additionally, we analyze the ranges of possible comprehensive ... which constructs the set of compatible outranking models via robust ordinal regression. Then, we consider all complete rankings ... We extend the principle of robust ordinal regression with an analysis of extreme ranking results. In our proposal, we consider ... "Extreme ranking analysis in robust ordinal regression," Omega, Elsevier, vol. 40(4), pages 488-501. ...
Applied Logistic Regression Analysis Second Edition. *Scott Menard - Sam Houston State University, USA, University of Colorado ... Updated coverage of unordered and ordered polytomous logistic regression models. Learn more about "The Little Green Book" - ... The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped ... and standardized logistic regression coefficients, and examples using SAS and SPSS are included. ...
Mapping regression residuals or the coefficients associated with Geographically Weighted Regression (GWR) analysis, will often ... OLS is the best known of all regression techniques. It is also the proper starting point for all spatial regression analyses. ... Regression analyses, on the other hand, make a stronger claim; they attempt to demonstrate the degree to which one or more ... Regression analysis is also used for prediction. You may want to understand why people are persistently dying young in certain ...
... regression analysis in which the dependent variable is assumed to be linearly related to the independent variable or variables ... regression analysis in which the dependent variable is assumed to be linearly related to the independent variable or variables. ...
Regression analysis can be very helpful for analyzing large amounts of data and making forecasts and predictions. To run ... regression analysis in Microsoft Excel, follow these instructions.... ... How to Run Regression Analysis in Microsoft Excel. ... Sample Regression Analysis for House Size. Sample Regression ... Run Regression Analysis * {"smallUrl":"https:\/\/\/images\/thumb\/8\/84\/Run-Regression-Analysis-in-Microsoft- ...
Number of Variables in Regression Analysis?. In a cross-sectional study for regression analysis... ... What is Regression Analysis? Description. Regression Analysis is a statistical forecasting method, that is concerned with ... Regression Analysis in Sales Forecasting. I need assistance in the use of regression anal... ... Regression Analysis Enables one to Determine the most Critical Predictor Variable. Great description of regresion analysis. So ...
... 2008-36-0199. ... Citation: Magri, M., "Analysis of Vehicle Customer Satisfaction Data using the Binary Logistic Regression," SAE Technical Paper ... Unlike standard regression models, the binary logistic regression is appropriate for non-continuous binary responses. It ... A case study of the analysis of a vehicle in the Brazilian market is used to illustrate its application. ...
Make research projects and school reports about Regression analysis easy with credible articles from our FREE, online ... and pictures about Regression analysis at ... Applied Regression Analysis. 3rd ed. New York: Wiley.. Dufour, ... Ordinary Least Squares Regression; Pearson, Karl; Probabilistic Regression; Probability; Regression; Regression Towards the ... For example, let the true regression model be Yi = β 1 + β 2X 2i + β 3X 3i + Ui and the estimated model be Yi = β 1 + β 2 X 2i ...
This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit ... Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the ... Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences By Jacob Cohen. , Patricia Cohen. , Stephen G. ... An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other ...
... Si-Lian Shen,1 Jian-Ling Cui,2 and Chun-Wei ... L. Zhang and C.-L. Mei, "Testing heteroscedasticity in nonparametric regression models based on residual analysis," Applied ... H. Dette and A. Munk, "Testing heteroscedasticity in nonparametric regression," Journal of the Royal Statistical Society B, vol ... J. Q. Fan, "Design-adaptive nonparametric regression," Journal of the American Statistical Association, vol. 87, no. 420, pp. ...
... or opportunity that can be examined using regression analysis. 1- Prepare a paper examining a regression analysis on your ... Regression Analysis. BrainMass Solutions Available for Instant Download. Time series analysis for Sales of roof material. ... Then, perform a regression analysis on your. LINEAR REGRESSION AND CORRELATION. Question 1. Refer to the Wage Data which ... Workplace Regression Analysis. Set up and solve a problem related to your workplace that is amenable to the use of regression ...
  • The response series might not share intercepts or regression coefficients. (
  • Estimate the regression coefficients using vgxvarx . (
  • The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. (
  • 4.3 Interpreting Multiple Regression Coefficients. (
  • The literature offers two distinct reasons for incorporating sample weights into the estimation of linear regression coefficients from a model-based point of view. (
  • The multiple regression coefficients for the dependence of sea level on wind are shown by the resultsdigplayed in Fig. 7 to be very poorly determined. (
  • Linear regression analysis is one of the most important statistical methods. (
  • However, results from co-expression analyses using standard methods are often hard to interpret because many top correlating genes arise from somatic copy number alteration (SCNA) rather than bona fide transcriptional regulation. (
  • This analysis provides a comprehensive account of models and methods to interpret such data. (
  • The authors have conducted research in the field for nearly fifteen years and in this work combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. (
  • In this way, we are able to assess its position in an overall ranking, and not only in terms of pairwise comparisons, as it is the case in original robust ordinal regression methods. (
  • ELECTREGKMS: Robust ordinal regression for outranking methods ," European Journal of Operational Research , Elsevier, vol. 214(1), pages 118-135, October. (
  • Comparative analysis of UTA multicriteria methods ," European Journal of Operational Research , Elsevier, vol. 130(2), pages 246-262, April. (
  • Longitudinal Regression Methods. (
  • Dr. Lemeshow has over thirty-five years of academic experience in the areas of regression, categorical data methods, and sampling methods. (
  • He is the coauthor of Sampling of Population: Methods and Application and Applied Logistic Regression , both published by Wiley. (
  • Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. (
  • The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. (
  • An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels. (
  • Regression methods continue to be an area of active research. (
  • Many books exist on regression analysis for those new to the subject or those who desire a better understanding of the many features and techniques used in the different regression methods. (
  • This particular class covers many biostatistical methods such multi-way and multivariate ANOVA, linear & logistic regression , discriminant analysis , and t-tests. (
  • Be able to critically appraise reports of research which have used a range of methods including linear and logistic regression. (
  • Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increased considerably in all areas of scientific inquiry, mainly as a result of model-building methods available in modern statistical software packages. (
  • Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. (
  • In this book, they 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. (
  • He is coauthor (with Pravin K. Trivedi) of the first edition of Regression Analysis of Count Data (Cambridge, 1998) and of Microeconometrics: Methods and Applications (Cambridge, 2005). (
  • Regression is a generic term for all methods attempting to fit a model to observed data in order to quantify the relationship between two groups of variables. (
  • The restriction was imposed on the assumption that the more complex regression methods of bias correction and compositing could perform poorly compared to OCF when data were missing from the training period. (
  • This course aims to teach the general concepts of regression methods and provide the framework of these methods with the aim improving statistical literacy on this important topic. (
  • Specific methods covered will be simple and multiple linear regression, logistic regression, and generalized linear models (GLM). (
  • For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. (
  • the same explanatory variables appear in the log-log equations, which is in fact OLS is equivalent to seemingly unrelated regression, it is not possible to improve the separate least-square estimation using a seemingly unrelated regression technique. (
  • Demand Estimation by Regression Method - Some Statistical Concepts for application ( All the formulae marked in red for remembering. (
  • Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. (
  • Quantile regression for robust bank efficiency score estimation ," European Journal of Operational Research , Elsevier, vol. 200(2), pages 568-581, January. (
  • We will begin with a review of basic statistical concepts and then go on to cover correlation, the development of the regression model, parameter estimation, statistical inference, and potential problems that can arise with regression analysis, applications, and interpretation. (
  • and how should it inform the specification and estimation of regression models? (
  • C. Cai , G. Wang , Y. Wen , J. Pei , X. Zhu and W. Zhuang , Superconducting transition temperature t c estimation for superconductors of the doped mgb2 system using topological index via support vector regression, Journal of Superconductivity and Novel Magnetism , 23 (2010), 745-748. (
  • While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of suffcient data, by borrowing strength across data sets, our joint estimation approach signifcantly improves the estimation effciency, which is demonstrated in a simulation study. (
  • Co-expression analysis is a widely adopted tool for functional prediction and identification of functionally related gene sets. (
  • intervals and prediction intervals from simple linear regression The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. (
  • Regression analysis is also used for prediction. (
  • First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. (
  • To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. (
  • As most practitioners may be well aware, regression serves two primary purposes -measurement and prediction. (
  • The chapter also covers topics such as prediction (using the regression line in reverse), leverage, goodness of fit, comparison between models with and without intercept, uncertainty, polynomial regression models without intercept, and an overview of robust regression through the origin. (
  • Regression analysis allows for the prediction of outcomes. (
  • Typically, statistical prediction makes use of multiple regression analysis (Marascuilo, 1971) and allows one to make inferences on a criterion variable based on what is known about one or more predictor variables. (
  • However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. (
  • Zhang, G.Y. and Ge, H.H. (2012) Prediction of Xylanase Optimal Temperature by Support Vector Regression. (
  • A regression model is any general linear model, Y = Xβ - e where X′X is nonsingular. (
  • We describe a functional regression model for predicting the angles as they change with time as a function of the target being reached, the anthropometry and other characteristics of the individual. (
  • It will also be a useful foil for conventional texts for the teaching of the regression model. (
  • 6 3.0 Model Specification 6 3.1 Linear Regression Model. (
  • Introduction This presentation on Regression Analysis will relate to a simple regression model. (
  • Initially, the regression model and the regression equation will be explored. (
  • This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. (
  • Multivariate survival analysis using Cox's regression model. (
  • The meta-regression is estimated by using the Random Effects Multilevel Model (REML) because it controls for within- and between-study heterogeneity. (
  • In a linear regression model the predictor function is linear in the parameters. (
  • In this case the regression model is slightly different. (
  • Idea that a variable depends on some characteristics (explanatory variables) is a multiple regression model: To make the derivations easier, we write some of the formulae below in terms of the simple regression model The problem with this regression is that we do not observe individual's utility and, thus, Y_i^* is unobservable. (
  • The logistic regression model can be interpreted as this regression, where the errors are assumed to satisfy all the classical assumptions except one. (
  • Non-additive robust ordinal regression: A multiple criteria decision model based on the Choquet integral ," European Journal of Operational Research , Elsevier, vol. 201(1), pages 277-288, February. (
  • Regression analysis allows you to model, examine, and explore spatial relationships, and can help explain the factors behind observed spatial patterns. (
  • GWR provides a local model of the variable or process you are trying to understand/predict by fitting a regression equation to every feature in the dataset. (
  • A good regression model can predict the outcome of a given key business indicator (dependent variable) based on the interactions of other related business drivers (explanatory variables). (
  • The meaning of the β coefficient varies with the functional form of the regression model. (
  • Sometimes researchers choose the model with the highest R 2 , but the purpose of the regression analysis is to obtain the best model based on a theoretical concept or an empirically observed phenomena. (
  • Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. (
  • A regression (trend) line model is fit to examine the relationship between daily high temperatures (T), measured in degrees, and power consumption (P), and measured in thousands of kilowatt hours in a small Kansas town. (
  • So at each collection stage take a note of anything different qualitatively for what you are measuring so that you can really narrow down what was different if your regression model changed wildly. (
  • The multiple regression analysis would then identify the relationship between the dependent variable and the explanatory variables this is then finally presented as a model (formula). (
  • 1.1 Simple Linear Regression Model. (
  • A regression model and plot are created (Figure 1). (
  • The Multiple Regression Analysis and Forecasting model provides simple and flexible input with integrated help icons to facilitate utilization. (
  • A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. (
  • Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. (
  • Using and interpreting different contrasts in linear models in R. When building a regression model with categorical variables with more than two levels (ie "Cold", "Freezing", "Warm") R is doing internally some transformation to be able to compute regression coefficient. (
  • This tutorial will explore how R can help one scrutinize the regression assumptions of a model via its residuals plot, normality histogram, and PP plot. (
  • These requirements are identical to the requirements for the Analysis of Covariance model. (
  • Chapter Three focuses on linear regression for interval-valued data within the framework of random sets, and proposes a new model that generalizes a series of existing ones. (
  • To reliably assess model validity, various error functions (whose mathematical expressions contain the number of experimental measurements, the numbers of independent variables and parameters in the regression equation as well as the measured and predicted equilibrium adsorption capacities) were used. (
  • The extensive and detailed coverage of the process of survival model fitting, as well as the applied exercises, make this textbook an excellent choice for an applied survival analysis course. (
  • Whether you are a market researcher who needs to make accurate predictions about new product launches, or a wildlife biologist keen on studying the successful breeding of wolf packs in the Rocky Mountains, you can make reliable predictions, by using the powerful Logistic Regression Analysis (LRA) model. (
  • Technically, a regression analysis model is based on the sum of squares , which is a mathematical way to find the dispersion of data points. (
  • I would like to derive the most optimal model among the Poisson regression model and the negative binomial regression model etc. (
  • The stratified Cox regression model is used to evaluate the effects of these prognostic factors, based on separate analysis for each trial, and on the combined data from both trials. (
  • The two data matrices involved in regression are usually denoted X and Y, and the purpose of regression is to build a model Y = f(X) . Such a model tries to explain, or predict, the variations in the Y-variable(s) from the variations in the X-variable(s). (
  • Univariate regression uses a single predictor, which is often not sufficient to model a property precisely. (
  • How and why to use a Statistical Regression Model? (
  • Building a regression model involves collecting predictor and response values for common samples, and then fitting a predefined mathematical relationship to the collected data. (
  • Once you have built a regression model, you can predict the unknown concentration for new samples, using the spectroscopic measurements as predictors. (
  • We develop inference tools in a semiparametric regression model with missing response data. (
  • A basic approach to statistical model building via regression analysis will be demonstrated. (
  • Harrell very nicely walks the reader through numerous analyses, explaining and defining his model-building choices at each step in the process. (
  • The Multiple Regression Analysis and Forecasting model provides a solid basis for identifying value drivers and forecasting data for input to valuation and analytical models. (
  • In this paper we describe a hierarchical regression model for meta-analysis of studies reporting estimates of test sensitivity and specificity. (
  • Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. (
  • Build a Regression Model in Excel, raw data will be provided. (
  • the robustness of a QSPR (regression) model. (
  • Regression Analysis is a statistical forecasting method, that is concerned with describing and evaluating the relationship between a particular dependent variable and one or more other variables (usually called the independent variables). (
  • The Multiple Regression Analysis and Forecasting template enables the confident identification of value drivers and forecasting business plan or scientific data. (
  • When predictive relationships have been identified by the feature selection and regression analysis, forecasting can be quickly accomplished based on a range of available methodologies and accompanying statistical strength. (
  • The Multiple regression analysis and forecasting template provides much more functionality than the Excel Analysis Toolpak such as individual regression of all independent variables, the actual level of confidence for the results, and tests of for autocorrelation and multicollinearity. (
  • The forecasting process provides options to employ 3rd polynomial, 2nd polynomial, exponential or linear trend lines on independent variables as well as the option to override independent variable forecast data with external analysis. (
  • The Multiple Regression Analysis and Forecasting template is compatible with Excel 97-2013 for Windows and Excel 2011 or 2004 for Mac as a cross platform regression and forecasting solution. (
  • Special topics also covered include regression models that include dummy variables, log-linear models, fixed effects models, a brief discussion of instrumental variables, and an introduction to time-series analysis and forecasting. (
  • The article reviews the book "Nonlinear Regression," by G.A.F. Seber and C.J. Wild. (
  • If the dependent variables are modeled as a non-linear function because the data relationships do not follow a straight line, use nonlinear regression instead. (
  • Yang, N. , Zhang, D. and Tian, Y. (2015) The Validity Analysis of Regression: Combining Uniform Experiment Design with Nonlinear Regression. (
  • NLREG is a powerful statistical analysis program that performs linear and nonlinear regression analysis, surface and curve fitting. (
  • Unlike many "nonlinear" regression programs that can only handle a limited set of function forms, NLREG can handle essentially any function whose form you can specify algebraically. (
  • NLREG performs true nonlinear regression analysis and curve fitting, it does not transform the function into a linear form. (
  • In addition to performing classic nonlinear regression, NLREG can be used to find the root or minimum value of a general multivariate, nonlinear function. (
  • The author of NLREG is available for consulting on data modeling and nonlinear regression projects. (
  • While binomial / binary logistic regression refers mostly to two possible outcomes usually coded as "0" and "1", multinomial logistic regression refers to three or more possible outcomes, such as yes/no/maybe scenarios for purchasing products. (
  • Misleading heuristics and moderated multiple regression models. (
  • The price sensitivity of selective demand: A meta-analysis of econometric models of sales. (
  • In regression models, the parameter vector β is estimable. (
  • 6 3.2 The Regression Specification Error Test 8 3.3 Non-linear models 9 3.4 Autocorrelation. (
  • Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. (
  • In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. (
  • Reciprocal Trade Agreements in Gravity Models: A Meta-Analysis ," Review of International Economics , Wiley Blackwell, vol. 18(1), pages 63-80, February. (
  • Reciprocal Trade Agreements in Gravity Models: A Meta-analysis ," Working Papers 18877, TRADEAG - Agricultural Trade Agreements. (
  • Reciprocal trade agreements in gravity models: a meta-analysis ," Economics & Statistics Discussion Papers esdp07035, University of Molise, Dept. EGSeI. (
  • This course involves a thorough examination of both simple linear regression models and multivariate models. (
  • We refer to both, the well-known UTAGMS method, which builds the set of general additive value functions compatible with DM's preferences, and newly introduced in this paper PROMETHEEGKS, which constructs the set of compatible outranking models via robust ordinal regression. (
  • The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. (
  • Updated coverage of unordered and ordered polytomous logistic regression models. (
  • Linear Regression Models using Matrix Notation. (
  • Polynomial Regression Models. (
  • Logistic Regression Models. (
  • This workshop will provide an introduction to bivariate and multiple regression models. (
  • Regression Analysis models are used to help us predict the value of one unknown variable, through one or more other variables whose values can be predetermined. (
  • Unlike standard regression models, the binary logistic regression is appropriate for non-continuous binary responses. (
  • This paper presents the binary logistic regression as an alternative to construct customer satisfaction models. (
  • Note that R 2 s of two different models are comparable only if the dependent variables and the number of observations are the same, because R 2 measures the fraction of the total variation in the dependent variable explained by the regression equation. (
  • Researchers learn how to specify regression models that directly address their research questions. (
  • Multiple Regression/Correlation and Causal Models. (
  • Random Coefficient Regression and Multilevel Models. (
  • Specific modeling techniques include: indices of spatial autocorrelation (Moran's I, Geary's C, LISA), spatial regression models (SAR and SEM), geographically weighted regression (GWR), and conditional autoregressive models (CAR). (
  • 1.2 Uses of Regression Models. (
  • Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). (
  • Correctly construct multivariate linear, logistic and Poisson regression models and to undertake survival analysis and Cox-regression modelling. (
  • Applied regression analysis and generalized linear models. (
  • Introduction All models are wrong, but some are useful - George Box Regression analysis marks the first step in predictive modeling. (
  • 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. (
  • This course develops the foundations of ordinary least squares (OLS) regression analysis and teaches students how to specify, estimate, and interpret multivariate regression models. (
  • Linear and non-linear regressions were employed for each of the isotherm models considered to describe the equilibrium data. (
  • VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models. (
  • Unlike linear regression models, which are used to predict a continuous outcome variable, logistic regression models are mostly used to predict a dichotomous categorical outcome, LRAs are frequently used in business analysis applications. (
  • This Paper presents the results of statistical analyses of ship characteristics which have been undertaken to provide input to models of ship costs and operations in particular trades. (
  • Simple linear regression models the relationship between a dependent variable and one independent variables using a linear function. (
  • Hi, I am familiar with similar analysis and with different regression models using SPSS. (
  • As a broad topic it includes analysis of variance (ANOVA), logistic regression, linear mixed models, and generalized linear models. (
  • Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. (
  • It has been shown that spatial autocorrelation and spatial heterogeneity in crash data are two critical properties when developing statistical models for macro-level safety analysis. (
  • Geographic information system (GIS) is powerful platform supporting lots of spatial regression models. (
  • Multivariate data analysis (7th ed. (
  • Analysis of panel data (3rd ed. (
  • However, co-expression analysis using human cancer transcriptomic data is confounded by somatic copy number alterations (SCNA), which produce co-expression signatures based on physical proximity rather than biological function. (
  • The results from analyses of TCGA, CCLE, and NCI60 data sets show that GRACE can improve our understanding of how a transcriptional network is re-wired in cancer. (
  • However, no method exists to remove the confounding effect of CNAs in the analysis of gene-gene co-expression using cancer transcriptome data. (
  • Through comprehensive analyses of genetics, genomics, proteomics, metabolomics, and drug response data from the public domain, we show that GRACE can improve our understanding of how a transcriptional network is re-wired in cancer. (
  • 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. (
  • The least-squares regression equation computed from their data is [pic]. (
  • This example shows how to prepare exogenous data for several seemingly unrelated regression (SUR) analyses. (
  • Using data from the National longitudinal study of youth, we find the following results for a regression of log weekly wage on years of education, experience, experience squared and an intercept: log(earnings)i = 4.016 + 0.092 · educi + 0.079 · experi − 0.002 · exper2 i (0.222) (0.008) (0.025) (0.001) a. (5 points) Construct a 95% confidence interval for the effect of years of education on log weekly earnings. (
  • Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. (
  • Regression analysis can be very helpful for analyzing large amounts of data and making forecasts and predictions. (
  • If your version of Excel displays the ribbon , go to Data , find the Analysis section, hit Data Analysis , and choose Regression from the list of tools. (
  • Data Analysis and choose Regression from the list of tools. (
  • Magri, M., "Analysis of Vehicle Customer Satisfaction Data using the Binary Logistic Regression," SAE Technical Paper 2008-36-0199, 2008, . (
  • This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. (
  • Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. (
  • 1- Prepare a paper examining a regression analysis on your collected data. (
  • Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. (
  • The first thing is that the regression tries to fit the existing data and the sample is not representative of the population, then the regression won't be useful just like estimating a distribution mean from a sample that is skewed massively to the left or right won't represent the true underlying mean of the population. (
  • Software emphasis will be given to GeoDa and R for exploratory spatial data analysis and modeling. (
  • Meta-analysis, a statistical method of pooling data from studies included in a systematic review, is often compromised by heterogeneity of its results. (
  • The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. (
  • Data analysis and regression : a second course in statistics. (
  • I thought you might be interested in this item at Title: Data analysis and regression : a second course in statistics. (
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  • To equip students with the necessary skills and knowledge to allow analysis of data with an awareness of effect modification and confounding. (
  • By means of lectures and hands-on analysis of data from real healthrelated studies, using the statistical software package STATA the student is guided through the full range of standard statistical parametric and non-parametric techniques, ranging from frequency tables to Cox's regression. (
  • Reviews the book "Regression Analysis of Count Data," by A. Colin Cameron and Pravin K. Trivedi. (
  • The chapter uses the Advertising data set available from the book's website: Testing the assumptions of linear regression. (
  • As this is a methodology I simply have to state how I will feed the data into regression analysis as opposed to enacting it. (
  • Be able to describe data and carry out linear and logistic regression and non-parametric statistics. (
  • Analyses throughout the text are performed using Stata Version 9, and an accompanying FTP site contains the data sets used in the book. (
  • This book fills this gap, providing a comprehensive, self-contained introduction to regression modeling used in the analysis of time-to-event data in epidemiological, biostatistical, and other health-related research. (
  • Standard least squares regressions were performed on the data to relate particular ship characteristics to deadweight. (
  • This will add the Data Analysis tools to the Data tab of your Excel ribbon. (
  • Relevant Skills and Experience I know statistic and data analysis very well. (
  • Learn from data science expert Michael Grogan in this tutorial that teaches you how to use regression analysis and R to uncover high-value business insights hidden inside large datasets. (
  • Based on the investigation data of social position of national women in the third phase by National Women's Federation and National Bureau of Statistics in 2010, regression analysis on sex wage difference is conducted. (
  • Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. (
  • Data analysis, particularly users of S-PLUS, with experience in the application of these tools will benefit the most from this book. (
  • Statistical techniques such as regression analysis are tools of action that enable accountants to make financial data meaningful to their clients. (
  • Chen, Y. , Ma, J. and Wang, S. (2020) Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data. (
  • Journal of Data Analysis and Information Processing , 8 , 1-19. (
  • Many studies have conducted the safety analysis of pedestrian crashes based on zone-level data and examined a lot of related features. (
  • The reproducibility of using R language to perform spatial data analysis is unparalleled, which includes plenty of spatial packages for different purposes. (
  • Although POI data may not include traditional information used in traffic accident analysis, they can represent specific land use factors with precise locations, which are expected to be highly related to pedestrian crashes in both macro- and micro-level aspects. (
  • In this paper, the principle and method of distinguish the training data and testing data were described to make a reasonable regression when uniform experiment design combined with support vector regression (SVR). (
  • Computational Statistics & Data Analysis, 54, 219-232. (
  • I have completed several PhD level thesis projects involving advanced statistical analysis of data. (
  • Results derived from linear least squares analysis ofthe smoothed data are quoted throughout the text. (
  • Examples of analysis of data will to a great degree be connected to the faculty's on-going or planned research projects. (
  • We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. (
  • In linear regression we find the "best" line through the data. (
  • an interesting application of this is " circular regression " where a circle is fitted to a set of data points. (
  • 1][2][3] To avoid making wrong inferences, regression toward the mean must be considered when designing scientific experiments and interpreting data. (
  • Sir Francis Galton first observed the phenomenon in the context of simple linear regression of data points. (
  • have completed a course in statistics that covers linear regression and logistic regression, which you can achieve by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. (
  • Connections will be made to other topics including the important ANOVA regression connection. (
  • Modules 1-3 are appropriate for introductory statistics courses while Module 4 (comparing ANOVA and regression) is appropriate for intermediate level courses. (
  • This article presents a review of the book "Multiple Regression and Analysis of Variance," by George O. Wesolowsky. (
  • In Chapter Two, the authors cover the homocedastic condition, i.e. variance of y's independent of x, errors of y's accumulative, the heterocedastic case, i.e. variance or standard deviation proportional to x values, respectively, and orthogonal regression (error in both axes). (
  • The comparative unimportance of wind, for this particular location, is supported by an analysis of the amount ofsea level variance that can be attributed to the variousinputs. (
  • These tools include Ordinary Least Squares (OLS) Regression and Geographically Weighted Regression (GWR). (
  • Geographically Weighted Regression (GWR) is one of several spatial regression techniques, increasingly used in geography and other disciplines. (
  • Then we carry out multiple regression analysis, focusing on the variables we want to use as predictors (explanatory variables). (
  • The first stage of the process is to identify the variable we want to predict (the dependent variable) and to then carry out multiple regression analysis focusing on the variables we want to use as predictors (explanatory variables). (
  • In statistics, we use regression analysis to predict the result of a categorical dependent variable based on one or more predictors or independent variables. (
  • The linear regression equation always has an error term because, in real life, predictors are never perfectly precise. (
  • Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features ," Psychometrika , Springer;The Psychometric Society, vol. 41(4), pages 505-529, December. (
  • The linear regression line is sometimes called the least squares line. (
  • What is the connection between 'least squares' and linear regression? (
  • Could 'least squares' and regression be generalized to more complicated cases than lines? (
  • The earliest form of regression was the method of least squares, which was published by Legendre in 1805, and by Gauss in 1809. (
  • C. Wang and D. X. Zhou , Optimal learning rates for least squares regularized regression with unbounded sampling, Journal of Complexity , 27 (2011), 55-67. (
  • Table 1 gives some details on the variables employed in the analysis. (
  • REGRESSION ANALYSIS Correlation only indicates the degree and direction of relationship between two variables. (
  • columns since, in this example, all exogenous variables are in the regression component of each response series. (
  • Correlation analyses and their associated graphics depicted above, test the strength of the relationship between two variables. (
  • regression analysis in which the dependent variable is assumed to be linearly related to the independent variable or variables. (
  • The multiple regression analysis would then identify the relationship between the dependent variable and the explanatory variables. (
  • Regression analysis is the statistical methodology of estimating a relationship between a single dependent variable ( Y ) and a set of predictor (explanatory/independent) variables ( X 2 , X 3 , … X k ) based on a theoretical or empirical concept. (
  • Multiple Regression/Correlation With Two or More Independent Variables. (
  • For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. (
  • Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. (
  • Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. (
  • Regression is a powerful, although often abused, method for assessing the relationship between two variables (simple linear regression). (
  • In Logistic Regression, the connection between the categorical dependent variable and the continuous independent variables is measured by changing the dependent variable into probability scores. (
  • Regression analysis helps you understand how the dependent variable changes when one of the independent variables varies and allows to mathematically determine which of those variables really has an impact. (
  • If you use two or more explanatory variables to predict the independent variable, you deal with multiple linear regression . (
  • Multivariate regression takes into account several predictive variables simultaneously, thus modeling the property of interest with more accuracy. (
  • It is shown that when missing responses are imputed using the semiparametric regression method the empirical log-likelihood is asymptotically a scaled chi-square variable or a weighted sum of chi-square variables with unknown weights in the absence of auxiliary information or in the presence of auxiliary information. (
  • Multiple Linear Regressions allows us to add more predictor variables. (
  • Second, linear regression assumes a straight line relationship, where in reality some variables are good, up to a point and then they are bad, the line would be curved. (
  • My course work in undergraduate included probability / statistics courses as well as courses in econometrics ( regression analysis ). (
  • As well, there will be a brief look into estimated regression equation. (
  • The equation for the i^th observation might be: There are many cases where the dependent variable is restricted to take on a limited range of values, for example only values 0 or 1 (binary logistic regression). (
  • it creates a single regression equation to represent that process. (
  • The fit of the regression equation is evaluated by the statistic R 2 , which measures the extent of the variation in Y explained by the regression equation. (
  • This technique will not only classify the original test cases but will also generate new test cases required for the purpose of regression testing. (
  • It is a term yielded by regression analysis that indicates the sensitivity of the dependent variable to a particular independent variable . (
  • First, linear regression assumes that the dependent variable (in this case site rank) is measured on an interval scale. (
  • In statistics, they differentiate between a simple and multiple linear regression. (
  • The current review will enable clinicians and healthcare decision-makers to appropriately interpret the results of meta-regression when used within the constructs of a systematic review, and be able to extend it to their clinical practice. (
  • We also outline how to use linear regression analysis to estimate nonlinear functions such as a multiplicative sales response function. (
  • 6 points) If we estimate the regression function with ability included. (
  • What regression would you run to estimate the effect of education on earnings to avoid ability bias? (
  • What is the regression you would run to estimate the effect of the change in the minimum wage? (
  • Our goal is to estimate the size of the vertical displacement of the treated unit from the regression line of all of the control units, indicated on the graph by the dashed arrow. (
  • We used joinpoint regression analysis to estimate the slope of mortality trends. (
  • Get R: Predictive Analysis now with O'Reilly online learning. (
  • In order to fill this gap, we perform a Meta-Regression-Analysis (MRA) by examining 1661 efficiency scores retrieved from 120 papers published over the period 2000--2014. (
  • Efficiency in banking: a meta-regression analysis ," International Review of Applied Economics , Taylor & Francis Journals, vol. 30(1), pages 112-149, January. (
  • Equilibrium exchange rates in Central and Eastern Europe: A meta-regression analysis ," Journal of Banking & Finance , Elsevier, vol. 30(5), pages 1359-1374, May. (
  • Equilibrium Exchange Rates in Central and Eastern Europe: A Meta-Regression Analysis ," William Davidson Institute Working Papers Series wp769, William Davidson Institute at the University of Michigan. (
  • Equilibrium exchange rates in Central and Eastern Europe : A meta-regression analysis ," BOFIT Discussion Papers 4/2005, Bank of Finland, Institute for Economies in Transition. (
  • Equilibrium Exchange Rates in Central and Eastern Europe: A Meta-Regression Analysis ," CEPR Discussion Papers 4869, C.E.P.R. Discussion Papers. (
  • We've seen in this chapter how to build a binary classifier based on Linear Regression and the logistic function. (
  • re: st: Standardization necessary for mediation analysis with binary outcome? (
  • The book covers, very completely, the nuances of regression modeling with particular emphasis on binary and ordinal logistic regression and parametric and nonparametric survival analysis. (
  • The Linear Regression Curve plots a line that best fits the prices specified over a user-defined time period. (
  • Think of the Linear Regression Curve as numerous lines, but both extreme ends of the lines are hidden, while the center portion is shown and is connected to other center portions of lines. (
  • The Linear Regression Curve is used mainly to identify trend direction and might sometimes be used to generate buy and sell signals. (
  • Traders might view the Linear Regression curve as the fair value for the stock, future, or forex currency pair, and any deviations from the curve as buy and sell opportunities. (
  • Generally, when price deviates a certain percentage or number of points below the Linear Regression Curve, then a trader might buy, thinking that price will revert back to fair value, which is thought to be the Linear Regression Curve. (
  • In a similar manner, when price moves above the Linear Regression Curve by a trader specified percentage or point value, then the trader might sell, believing that price will return back to the Linear Regression Curve. (
  • Since the Linear Regression Curve is great at identifying trend direction, if price is trending higher, a trader might only take buy signals when price deviated below the curve. (
  • D. Basak , S. Pal and D. C. Patranabis , Support vector regression, Neural Information Processing-Letters and Reviews , 11 (2007), 203-224. (
  • This page is about the Linear Regression Channel. (
  • Other confirmation signs like prices closing back inside the linear regression channel might be used to initiate potential buy or sell orders. (
  • When price closes outside of the Linear Regression Channel for long periods of time, this is often interpreted as an early signal that the past price trend may be breaking and a significant reversal might be near. (
  • Arguably the most popular usage of the Linear Regression concept is the Linear Regression Channel, often used by large institutions. (
  • Rabe-Hesketh, S. and Everitt, B. A handbook of statistical analyses using Stata . (
  • We extend the principle of robust ordinal regression with an analysis of extreme ranking results. (
  • Extreme ranking analysis in robust ordinal regression ," Omega , Elsevier, vol. 40(4), pages 488-501. (
  • R. He , W. S. Zheng and B. G. Hu , Maximum correntropy criterion for robust face recognition, IEEE Transactions on pattern Analysis and Machine Intelligence , 33 (2011), 1561-1576. (
  • Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions ," European Journal of Operational Research , Elsevier, vol. 191(2), pages 416-436, December. (
  • A semiparametric regression imputation estimator and an empirical likelihood based one for the mean of the response variable are defined. (
  • My teaching focus is in mathematical economics, including multivariate statistics, regression , and convex analysis . (
  • A more formal treatment of multiple regression modeling and correlation was introduced in 1903 by Galton ' s friend Karl Pearson (1857-1936). (
  • An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. (
  • The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. (
  • Overall we think the book successfully reflects more contemporary issues, thinking, and advances in multiple regression and correlation. (
  • 4. Introduction to Multiple Linear Regression. (
  • 4.1 An Example of Multiple Linear Regression. (
  • 4.6 Alternatives to Multiple Regression. (
  • 5. Plots in Multiple Regression. (
  • The multiple regression process utilizes commonly employed statistical measures to test the validity of the analysis and results are summarized in text form to be easily understood. (
  • He assisted me on Doctoral level multiple regression analysis problems. (
  • What is Multiple Linear Regression? (
  • Professor Harrell has produced a book that offers many new and imaginative insights into multiple regression, logistic regression and survival analysis, topics that form the core of much of the statistical analysis carried out in a variety of disciplines, particularly in medicine. (
  • Next, we analyze whether forms of regression analysis other than simple linear regression, including curvilinear and multiple regression analysis , would be appropriate techniques for such prognostications. (
  • A Multiple Linear Regression Analysis using a constant was computed against scores on the PDT and the present Grade for the 292 High School Students as depicted in Table 2 below. (
  • In the context of Search Engine Optimization (SEO), Multiple Linear Regression doesn't really work for two reasons. (
  • Bivariate Correlation and Regression. (
  • What are some examples of practical applications for correlation and regression analysis that might be of use to us? (
  • The goal is to get people thinking about how they can actually use correlation and regression in their real life, and where and how can they can really benefit from these techniques? (
  • In seemingly unrelated regression (SUR), each response variable is a function of a subset of the exogenous series, but not of any endogenous variable. (
  • The book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. (