DiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaHumanitiesInformation ScienceNamed GroupsHealth CareGeographicals
Regression AnalysisLogistic ModelsRisk FactorsLinear ModelsCross-Sectional StudiesMultivariate AnalysisProspective StudiesCohort StudiesRetrospective StudiesQuestionnairesAge FactorsPredictive Value of TestsOdds RatioCase-Control StudiesTime FactorsFollow-Up StudiesSex FactorsPrognosisPrevalenceTreatment OutcomeSocioeconomic FactorsRisk AssessmentSeverity of Illness IndexNeoplasm Regression, SpontaneousBody Mass IndexProportional Hazards ModelsUnited StatesJapanBiological MarkersLongitudinal StudiesSmokingPregnancyIncidenceInfant, NewbornChinaReproducibility of ResultsROC CurveChi-Square DistributionAnalysis of VarianceComorbidityConfidence IntervalsSensitivity and SpecificityGenotypeSurvival AnalysisEducational StatusObesityNetherlandsHealth StatusEuropean Continental Ancestry GroupHealth SurveysReference ValuesEpidemiologic MethodsModels, StatisticalDisease ProgressionPoisson DistributionSurvival RateBlood PressureAsian Continental Ancestry GroupDepressionHypertensionRiskSex DistributionKaplan-Meier EstimateAge DistributionAfrican AmericansDemographyTaiwanAlcohol DrinkingQuality of LifeAnthropometryUrban PopulationDiabetes Mellitus, Type 2RegistriesNorwayBody WeightAgingChronic DiseaseStatistics as TopicDietSocial ClassCardiovascular DiseasesGenetic Predisposition to DiseasePolymorphism, Single NucleotideStress, PsychologicalResidence CharacteristicsRural PopulationOccupational ExposureC-Reactive ProteinGestational AgeRegression (Psychology)StrokeRepublic of KoreaStatistics, NonparametricLeast-Squares AnalysisBrazilPolymorphism, GeneticData Interpretation, StatisticalBreast NeoplasmsDenmarkMagnetic Resonance ImagingGermanyCoronary Artery DiseaseLife StyleHospitalizationSwedenRecurrenceProbabilityAlgorithmsBirth WeightHealth Knowledge, Attitudes, PracticeHispanic AmericansBlood GlucoseHIV InfectionsPostoperative ComplicationsKidney Failure, ChronicRenal DialysisDiabetes MellitusActivities of Daily LivingHospital MortalityOccupational DiseasesItalyEmploymentSelf ReportData CollectionForecastingNeoplasm StagingSex CharacteristicsAfrican Continental Ancestry GroupHealth Status IndicatorsBody CompositionIndiaSocial SupportCaliforniaHealth BehaviorOutcome Assessment (Health Care)Environmental ExposureFinlandTomography, X-Ray ComputedBody HeightIncomeSpainMyocardial InfarctionMothersCreatinineConfounding Factors (Epidemiology)ExerciseLength of StayStudentsOverweightSeasonsNeuropsychological TestsAdaptation, PsychologicalTexasCognition DisordersParityPovertyNeoplasmsCoronary DiseaseMotor ActivityUrban HealthMortalityPopulation SurveillanceSubstance-Related DisordersSelf ConceptMaternal AgeCholesterol, HDLFamily CharacteristicsDisability EvaluationRemission, SpontaneousParentsAcute DiseaseTumor Markers, BiologicalInterviews as TopicMarital StatusGlomerular Filtration RateArea Under CurveAnxietyPsychiatric Status Rating ScalesWaist CircumferenceHemoglobin A, GlycosylatedOccupationsWorkplaceTurkeyCause of DeathAdolescent BehaviorAttitude to HealthCanadaPatient Acceptance of Health CareMetabolic Syndrome XBody Weights and MeasuresHealth Services AccessibilityBiometryNutritional StatusLung NeoplasmsLipidsInsulin ResistanceHealth Care SurveysContinental Population GroupsCausalityAllelesPregnancy Outcome
Regression Analysis: 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.Logistic Models: 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.Risk Factors: 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.Linear Models: 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.Cross-Sectional Studies: 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.Multivariate Analysis: 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.Prospective Studies: 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.Cohort Studies: 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.Retrospective Studies: 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.Questionnaires: 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 Factors: 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.Predictive Value of Tests: 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.Odds Ratio: 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.Case-Control Studies: 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.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Follow-Up Studies: 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.Sex Factors: 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.Prognosis: 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.Prevalence: 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.Treatment Outcome: 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.Socioeconomic Factors: Social and economic factors that characterize the individual or group within the social structure.Risk Assessment: 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)Severity of Illness Index: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.Neoplasm Regression, Spontaneous: Disappearance of a neoplasm or neoplastic state without the intervention of therapy.Body Mass Index: 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)Proportional Hazards Models: 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.United StatesJapanBiological Markers: 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.Longitudinal Studies: Studies in which variables relating to an individual or group of individuals are assessed over a period of time.Smoking: Inhaling and exhaling the smoke of burning TOBACCO.Pregnancy: The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.Incidence: 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.Infant, Newborn: An infant during the first month after birth.China: A country spanning from central Asia to the Pacific Ocean.Reproducibility of Results: 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.ROC Curve: 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.Chi-Square Distribution: 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.Analysis of Variance: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.Comorbidity: 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.Confidence Intervals: 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.Sensitivity and Specificity: 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)Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Survival Analysis: 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 Status: Educational attainment or level of education of individuals.Obesity: 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).Netherlands: 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.Health Status: The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.European Continental Ancestry Group: Individuals whose ancestral origins are in the continent of Europe.Health Surveys: A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.Reference Values: 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.Epidemiologic Methods: Research techniques that focus on study designs and data gathering methods in human and animal populations.Models, Statistical: 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.Disease Progression: 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.Poisson Distribution: 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.Survival Rate: 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.Blood Pressure: PRESSURE of the BLOOD on the ARTERIES and other BLOOD VESSELS.Asian Continental Ancestry Group: Individuals whose ancestral origins are in the southeastern and eastern areas of the Asian continent.Depression: Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.Hypertension: 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.Risk: The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.Sex Distribution: 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.Kaplan-Meier Estimate: 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)Age Distribution: 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.African Americans: Persons living in the United States having origins in any of the black groups of Africa.Demography: Statistical interpretation and description of a population with reference to distribution, composition, or structure.TaiwanAlcohol Drinking: Behaviors associated with the ingesting of alcoholic beverages, including social drinking.Quality of Life: 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.Anthropometry: The technique that deals with the measurement of the size, weight, and proportions of the human or other primate body.Urban Population: The inhabitants of a city or town, including metropolitan areas and suburban areas.Diabetes Mellitus, Type 2: 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.Registries: The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.NorwayBody Weight: The mass or quantity of heaviness of an individual. It is expressed by units of pounds or kilograms.Aging: The gradual irreversible changes in structure and function of an organism that occur as a result of the passage of time.Chronic Disease: 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)Statistics as Topic: 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.Diet: Regular course of eating and drinking adopted by a person or animal.Social Class: A stratum of people with similar position and prestige; includes social stratification. Social class is measured by criteria such as education, occupation, and income.Cardiovascular Diseases: Pathological conditions involving the CARDIOVASCULAR SYSTEM including the HEART; the BLOOD VESSELS; or the PERICARDIUM.Genetic Predisposition to Disease: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.Polymorphism, Single Nucleotide: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.Stress, Psychological: Stress wherein emotional factors predominate.Residence Characteristics: Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.Rural Population: The inhabitants of rural areas or of small towns classified as rural.Occupational Exposure: The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.C-Reactive Protein: A plasma protein that circulates in increased amounts during inflammation and after tissue damage.Gestational Age: 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.Regression (Psychology): 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).Stroke: 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)Republic of Korea: 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.Statistics, Nonparametric: 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)Least-Squares Analysis: 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.BrazilPolymorphism, Genetic: 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.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Breast Neoplasms: Tumors or cancer of the human BREAST.DenmarkMagnetic Resonance Imaging: 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.GermanyCoronary Artery Disease: Pathological processes of CORONARY ARTERIES that may derive from a congenital abnormality, atherosclerotic, or non-atherosclerotic cause.Life Style: Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)Hospitalization: The confinement of a patient in a hospital.SwedenRecurrence: The return of a sign, symptom, or disease after a remission.Probability: The study of chance processes or the relative frequency characterizing a chance process.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Birth Weight: The mass or quantity of heaviness of an individual at BIRTH. It is expressed by units of pounds or kilograms.Health Knowledge, Attitudes, Practice: 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).Hispanic Americans: 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.Blood Glucose: Glucose in blood.HIV Infections: Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).Postoperative Complications: 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.Kidney Failure, Chronic: 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.Renal Dialysis: Therapy for the insufficient cleansing of the BLOOD by the kidneys based on dialysis and including hemodialysis, PERITONEAL DIALYSIS, and HEMODIAFILTRATION.Diabetes Mellitus: A heterogeneous group of disorders characterized by HYPERGLYCEMIA and GLUCOSE INTOLERANCE.Activities of Daily Living: The performance of the basic activities of self care, such as dressing, ambulation, or eating.Hospital Mortality: A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.Occupational Diseases: Diseases caused by factors involved in one's employment.ItalyEmployment: The state of being engaged in an activity or service for wages or salary.Self Report: Method for obtaining information through verbal responses, written or oral, from subjects.Data Collection: 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.Forecasting: 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.Neoplasm Staging: Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.Sex Characteristics: 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.African Continental Ancestry Group: Individuals whose ancestral origins are in the continent of Africa.Health Status Indicators: The measurement of the health status for a given population using a variety of indices, including morbidity, mortality, and available health resources.Body Composition: The relative amounts of various components in the body, such as percentage of body fat.IndiaSocial Support: 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.CaliforniaHealth Behavior: 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.Outcome Assessment (Health Care): 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).Environmental Exposure: 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.FinlandTomography, X-Ray Computed: Tomography using x-ray transmission and a computer algorithm to reconstruct the image.Body Height: The distance from the sole to the crown of the head with body standing on a flat surface and fully extended.Income: Revenues or receipts accruing from business enterprise, labor, or invested capital.Spain: Parliamentary democracy located between France on the northeast and Portugual on the west and bordered by the Atlantic Ocean and the Mediterranean Sea.Myocardial Infarction: NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION).Mothers: Female parents, human or animal.CreatinineConfounding Factors (Epidemiology): 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.Exercise: 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.Length of Stay: The period of confinement of a patient to a hospital or other health facility.Students: Individuals enrolled in a school or formal educational program.Overweight: 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".Seasons: 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)Neuropsychological Tests: 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.Adaptation, Psychological: 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)TexasCognition Disorders: Disturbances in mental processes related to learning, thinking, reasoning, and judgment.Parity: The number of offspring a female has borne. It is contrasted with GRAVIDITY, which refers to the number of pregnancies, regardless of outcome.Poverty: 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.Neoplasms: 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.Coronary Disease: 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.Motor Activity: The physical activity of a human or an animal as a behavioral phenomenon.Urban Health: The status of health in urban populations.Mortality: All deaths reported in a given population.Population Surveillance: 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.Substance-Related Disorders: Disorders related to substance abuse.Self Concept: A person's view of himself.Maternal Age: The age of the mother in PREGNANCY.Cholesterol, HDL: Cholesterol which is contained in or bound to high-density lipoproteins (HDL), including CHOLESTEROL ESTERS and free cholesterol.Family Characteristics: Size and composition of the family.Disability Evaluation: 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.Remission, Spontaneous: A spontaneous diminution or abatement of a disease over time, without formal treatment.Parents: Persons functioning as natural, adoptive, or substitute parents. The heading includes the concept of parenthood as well as preparation for becoming a parent.Acute Disease: Disease having a short and relatively severe course.Tumor Markers, Biological: 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.Interviews as Topic: 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.Marital Status: A demographic parameter indicating a person's status with respect to marriage, divorce, widowhood, singleness, etc.Glomerular Filtration Rate: 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.Area Under Curve: 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)Anxiety: Feeling or emotion of dread, apprehension, and impending disaster but not disabling as with ANXIETY DISORDERS.Psychiatric Status Rating Scales: Standardized procedures utilizing rating scales or interview schedules carried out by health personnel for evaluating the degree of mental illness.Waist Circumference: 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.Hemoglobin A, Glycosylated: 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.Occupations: Crafts, trades, professions, or other means of earning a living.Workplace: Place or physical location of work or employment.TurkeyCause of Death: Factors which produce cessation of all vital bodily functions. They can be analyzed from an epidemiologic viewpoint.Adolescent Behavior: Any observable response or action of an adolescent.Attitude to Health: Public attitudes toward health, disease, and the medical care system.Canada: The largest country in North America, comprising 10 provinces and three territories. Its capital is Ottawa.Patient Acceptance of Health Care: The seeking and acceptance by patients of health service.Metabolic Syndrome X: 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)Body Weights and Measures: Measurements of the height, weight, length, area, etc., of the human and animal body or its parts.Health Services Accessibility: 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.Biometry: The use of statistical and mathematical methods to analyze biological observations and phenomena.Nutritional Status: State of the body in relation to the consumption and utilization of nutrients.Lung Neoplasms: Tumors or cancer of the LUNG.Lipids: 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)Insulin Resistance: 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.Health Care Surveys: Statistical measures of utilization and other aspects of the provision of health care services including hospitalization and ambulatory care.Continental Population Groups: Groups of individuals whose putative ancestry is from native continental populations based on similarities in physical appearance.Causality: 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.Alleles: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.Pregnancy Outcome: 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.

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)

Simple Regression Analysis in Public Health | CourseraSimple Regression Analysis in Public Health | Coursera
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 ...
Rectified Hydrocarbons by-products from... - Registration Dossier - ECHARectified Hydrocarbons by-products from... - Registration Dossier - ECHA
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 linear regression analysis in excel ideas - Midirymultiple regression analysis excel template linear regression analysis in excel ideas - Midiry
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 ...
The price is right!? A meta-regression analysis on willingness to pay for local food<...The price is right!? A meta-regression analysis on willingness to pay for local food<...
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 ...
The Impact of Natural Amenity on Farmland Values: A Quantile Regression ApproachThe Impact of Natural Amenity on Farmland Values: A Quantile Regression Approach
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.
Ch6 regression explanation - How to perform Simple Regression using Excel 1 Open up excel and verify that you have the data...Ch6 regression explanation - How to perform Simple Regression using Excel 1 Open up excel and verify that you have the data...
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 latest versionNonlinear regression analysis - CurveFitter latest version
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.
Simple Regression Analysis in R - R Programing - Business Intelligence & Analytics Community for Digital TransformationSimple Regression Analysis in R - R Programing - Business Intelligence & Analytics Community for Digital Transformation
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...
GI DL - GPU-based regression analysis on sparse gridsGI DL - GPU-based regression analysis on sparse grids
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 ...
Annotated Stata Output Simple Regression AnalysisAnnotated Stata Output Simple Regression Analysis
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 ...
Quantile regression models of animal habitat relationshipsQuantile regression models of animal habitat relationships
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
Addressing multicollinearity in regression models: a ridge regression application - Munich Personal RePEc ArchiveAddressing multicollinearity in regression models: a ridge regression application - Munich Personal RePEc Archive
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 ...
SLR ANOVA - Simple Regression Analysis of Variance (ANOVA) - NumXL SupportSLR ANOVA - Simple Regression Analysis of Variance (ANOVA) - NumXL Support
Calculates the regression model analysis of the variance (ANOVA) values. Syntax SLR_ANOVA(X, Y, Intercept, Return_type) X is the...
Univariate Regression - Regression - Approximation - Maths Reference with Worked ExamplesUnivariate Regression - Regression - Approximation - Maths Reference with Worked Examples
Univariate regression, polynomial regression, orthogonal polynomials, nonlinear - References for Univariate Regression with worked examples
R Nonlinear Regression Analysis - All-inclusive Tutorial for Newbies! - DataFlairR Nonlinear Regression Analysis - All-inclusive Tutorial for Newbies! - DataFlair
Learn about nonlinear regression analysis in R Programming with the concept of logistic regression, nonlinear regression models, generalized additive models and self-starting functions.
Current StudentsCurrent Students
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.. ...
IDEALS @ Illinois: Income Inequality and Wage Differentials: A Quantile Regression ApproachIDEALS @ Illinois: Income Inequality and Wage Differentials: A Quantile Regression Approach
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 ...
Applied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google BooksApplied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google Books
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.
Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006-2016 -...Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006-2016 -...
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 ...
Identifying Genuine Effects in Observational Research by Means of Meta-RegressionsIdentifying Genuine Effects in Observational Research by Means of Meta-Regressions
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 -- Nonlinear Regression Analysis ProgramNLREG -- Nonlinear Regression Analysis Program
NLREG performs linear and nonlinear regression analysis and curve fitting. NLREG can handle linear, polynomial, exponential, logistic, periodic, and general nonlinear functions.
NLREG -- Nonlinear Regression Analysis ProgramNLREG -- Nonlinear Regression Analysis Program
NLREG performs linear and nonlinear regression analysis and curve fitting. NLREG can handle linear, polynomial, exponential, logistic, periodic, and general nonlinear functions.
ASQ: Nonlinear Regression AnalysisASQ: Nonlinear Regression Analysis
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.. ...
Consistent specification testing of quantile regression modelsConsistent specification testing of quantile regression models
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. [+] [-] ...
Asymptotic theory in fixed effects panel data seemingly unrelated partially linear regression models<...Asymptotic theory in fixed effects panel data seemingly unrelated partially linear regression models<...
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...Birth Weight and Systolic Blood Pressure in Adolescence and Adulthood: Meta-Regression Analysis of Sex- and Age-specific...
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...Probit Regression Analysis in Estimating the Effect of Learning Assisted by Cabri 3D on Students' Mathematical Understanding...
Probit Regression Analysis in Estimating the Effect of Learning Assisted by Cabri 3D on Students Mathematical Understanding Ability
Frontiers | A Review of R-packages for Random-Intercept Probit Regression in Small Clusters | Applied Mathematics and StatisticsFrontiers | A Review of R-packages for Random-Intercept Probit Regression in Small Clusters | Applied Mathematics and Statistics
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
Lack of Fit in Ordinal Regression -- Analysis/Alternatives? | Physics Forums - The Fusion of Science and CommunityLack of Fit in Ordinal Regression -- Analysis/Alternatives? | Physics Forums - The Fusion of Science and Community
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 ...
Multiple regression analysis tutorialsMultiple regression analysis tutorials
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 ...
9780415670784: Meta-Regression Analysis in Economics and Business (Routledge Advances in Research Methods) - AbeBooks - T.D....9780415670784: Meta-Regression Analysis in Economics and Business (Routledge Advances in Research Methods) - AbeBooks - T.D....
AbeBooks.com: 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.
Does economics add up? An introduction to meta-regression analysis - DRODoes economics add up? An introduction to meta-regression analysis - DRO
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. ...
Regression Analysis with Excel using BenefitsRegression Analysis with Excel using Benefits
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.
Regression Analysis: AIU job satisfaction dataRegression Analysis: AIU job satisfaction data
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.
An Introduction to Econometrics | The MIT PressAn Introduction to Econometrics | The MIT Press
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 ...
Specifying the Polynomial Regression Analysis :: SAS/IML(R) Studio 13.2: Users GuideSpecifying the Polynomial Regression Analysis :: SAS/IML(R) Studio 13.2: User's Guide
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. ...
Resampling Techniques in Regression Analysis for Model Simplification, 978-3-659-14290-1, 3659142905 ,9783659142901 Di ...Resampling Techniques in Regression Analysis for Model Simplification, 978-3-659-14290-1, 3659142905 ,9783659142901 Di ...
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
Search Results for high-density lipoprotein | Human KineticsSearch Results for "high-density lipoprotein" | Human Kinetics
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 ...
NAIFA - National Association of Independent Fee AppraisersNAIFA - National Association of Independent Fee Appraisers
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.. ...
City Research Online - Who benefits from reducing the cost of formality? Quantile regression discontinuity analysisCity Research Online - Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis
This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on micro-firms performance and produce a clear characterization of who benefits from these programs.. ...
Determinants of Obesity in Turkey: A Quantile Regression Analysis from a Developing Country | IZA -...Determinants of Obesity in Turkey: A Quantile Regression Analysis from a Developing Country | IZA -...
This study investigates the factors that may influence the obesity in Turkey which is a developing country by implementing Quantile Regression (QR) me...
Large Changes in Fiscal Policy: Taxes Versus SpendingLarge Changes in Fiscal Policy: Taxes Versus Spending
We examine the evidence on episodes of large stances in fiscal policy, both in cases of fiscal stimuli and in that of fiscal adjustments in OECD countries from 1970 to 2007. Fiscal stimuli based upon tax cuts are more likely to increase growth than those based upon spending increases. As for fiscal adjustments, those based upon spending cuts and no tax increases are more likely to reduce deficits and debt over GDP ratios than those based upon tax increases. In addition, adjustments on the spending side rather than on the tax side are less likely to create recessions. We confirm these results with simple regression analysis. ...
Using Quantile Regression in Hedonic Analysis to Reveal Hedonic Submarket Competition - Texas Tech University ScholarsUsing Quantile Regression in Hedonic Analysis to Reveal Hedonic Submarket Competition - Texas Tech University Scholars
TY - JOUR. T1 - Using Quantile Regression in Hedonic Analysis to Reveal Hedonic Submarket Competition. AU - Farmer, Michael. AU - Lipscombe, C.. PY - 2010/12. Y1 - 2010/12. M3 - Article. SP - 385. EP - 400. JO - Journal of Real Estate Research. JF - Journal of Real Estate Research. ER - ...
Download Regression Analysis and Forecasting 1.0 - Multiple Regression Analysis and ForecastingDownload Regression Analysis and Forecasting 1.0 - Multiple Regression Analysis and Forecasting
Downloads: 166, Size: 0.13 MB, License: shareware. Regression Analysis and Forecasting1.0 - Multiple Regression Analysis and Forecasting The Multiple Regression Analysis and Forecasting model provides a solid basis for id
Regression Analysis by Example, 5th Edition - Statistics ViewsRegression Analysis by Example, 5th Edition - Statistics Views
Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.. Preface xiv 1 Introduction 1. 1.1 What Is Regression Analysis? 1. 1.2 Publicly Available Data Sets 2. 1.3 Selected Applications of Regression Analysis 3. 1.4 Steps in Regression Analysis 13. 1.5 Scope and Organization of the Book 21. Exercises 23. 2 Simple Linear Regression 25. 2.1 Introduction 25. 2.2 Covariance and Correlation Coefficient 25. 2.3 Example: Computer Repair Data 30. 2.4 The Simple Linear ...
multiple regression analysismultiple regression analysis
We have posted previously about the higher prevalence of obesity and overweight in people with learning disabilities. The researchers in this study were interested in looking at the data on body weight status and lipids levels of adults with learning disabilities who also had mental health problems. They carried out a stepwise multiple regression analysis, [read the full story…]. ...
Videos in: 2016 | Edge.orgVideos in: 2016 | Edge.org
A huge range of science projects are done with these multiple regression things. The results are often somewhere between meaningless and quite damaging. ... I hope that in the future, if Im successful in communicating with people about this, that therell be a kind of upfront warning in New York Times articles: These data are based on multiple regression analysis. This would be a sign that you probably shouldnt read the article because youre quite likely to get non-information or misinformation.. RICHARD NISBETT is a professor of psychology and co-director of the Culture and Cognition Program at the University of Michigan. He is the author of Mindware: Tools for Smart Thinking; and The Geography of Thought. Richard Nisbetts Edge Bio Page. ...
Help Online - Origin Help - Regression and Curve FittingHelp Online - Origin Help - Regression and Curve Fitting
Regression analysis is the study of the relationship between one or several predictors (independent variables) and the response (dependent variable). To perform regression analysis on a dataset, a regression model is first developed. Then the best fit parameters are estimated using something like the least-square method. Finally, the quality of the model is assessed using one or more hypothesis tests. From a mathematical point of view, there are two basic types of regression: linear and nonlinear. A model where the fit parameters appear linearly in the Least Squares normal equations is known as a "linear model"; otherwise it is "nonlinear". In many scientific experiments, the regression model has only one or two predictors, and the aim of regression is to fit a curve or a surface to the experimental data. So we may also refer to regression analysis as "curve fitting" or "surface fitting." ...
An Empirical Analysis of Family Cost of Children : A Comparison of Ordinary Least Square Regression and Quantile RegressionAn Empirical Analysis of Family Cost of Children : A Comparison of Ordinary Least Square Regression and Quantile Regression
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.. ...
Bayesian hierarchical regression model to detect quantitative trait loci, NC DOCKS (North Carolina Digital Online Collection of...Bayesian hierarchical regression model to detect quantitative trait loci, NC DOCKS (North Carolina Digital Online Collection of...
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 ...
OSA | Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulationOSA | Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation
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 ...
PPT - Notes 5: Simple Linear Regression PowerPoint Presentation - ID:333739PPT - Notes 5: Simple Linear Regression PowerPoint Presentation - ID:333739
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
Introduction to Linear Regression Analysis, 5th Edition | Regression Analysis | General & Introductory Statistics | Subjects |...Introduction to Linear Regression Analysis, 5th Edition | Regression Analysis | General & Introductory Statistics | Subjects |...
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 ...
204 14 simp lin reg - Chapter 14 Simple Linear Regression Hypotheses tests and Confidence Intervals In simple linear regression...204 14 simp lin reg - Chapter 14 Simple Linear Regression Hypotheses tests and Confidence Intervals In simple linear regression...
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
Linear Regression Excel Templates Multiple Linear Regression Analysis Using Microsoft Excel - SPREADSHEET DESIGNSLinear Regression Excel Templates Multiple Linear Regression Analysis Using Microsoft Excel - SPREADSHEET DESIGNS
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Unconditional quantile regression analysis of UK inbound tourist expenditures<...Unconditional quantile regression analysis of UK inbound tourist expenditures<...
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 http://creativecommons.org/licenses/by-nc-nd/4.0/ 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 ...
Building a Simple Linear Regression Model with Sci-kit LearnBuilding a Simple Linear Regression Model with Sci-kit Learn
In this guide, we will learn how to build a Simple Linear Regression Model using Sci-kit Learn. Simple Linear Regression is a allgorithm
Regression Analysis Models to Predict the 28 -day Compressive Strength Using Accelerated Curing Tests | Journal of...Regression Analysis Models to Predict the 28 -day Compressive Strength Using Accelerated Curing Tests | Journal of...
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 ...
Ventricular twist and strain in hypertrophic cardiomyopathy: relation to exercise capacity | HeartVentricular twist and strain in hypertrophic cardiomyopathy: relation to exercise capacity | Heart
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) ...
Urbanizations effects on the urban-rural income gap in China: A meta-regression analysisUrbanization's effects on the urban-rural income gap in China: A meta-regression analysis
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
Nonparametric Regression Methods For Longitudinal Data Analysis Mixed Effects Modeling Approaches PDF Book - Golden Resource...Nonparametric Regression Methods For Longitudinal Data Analysis Mixed Effects Modeling Approaches PDF Book - Golden Resource...
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
Choosing Appropriate Regression Model in the Presence of MulticolinearityChoosing Appropriate Regression Model in the Presence of Multicolinearity
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.
Whats the difference between correlation and simple linear regression? - Cross ValidatedWhat's the difference between correlation and simple linear regression? - Cross Validated
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.. ...
A note on the estimation of linear regression models with Heteroskedastic measurement errors | Fed in PrintA note on the estimation of linear regression models with Heteroskedastic measurement errors | Fed in Print
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 ...
Studenters prokrastineringsbeteende förklaras mer av deras grad grit än av deras inre motivationStudenters prokrastineringsbeteende förklaras mer av deras grad grit än av deras inre motivation
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 ...
Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor - Introduction and Module 1A: Simple...Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor - Introduction and Module 1A: Simple...
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 ...
Checking Simple Linear Regression Analysis Using 5Ss - iSixSigmaChecking Simple Linear Regression Analysis Using 5S's - iSixSigma
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...Japan Geoscience Union Meeting 2016/Classification and Regression Tree Analysis of the Relationship between the Yellow Dust...
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
How to perform conditional logistic regression analysis on matched pairs, when you dont have time-to-event data? | Statistics...How to perform conditional logistic regression analysis on matched pairs, when you don't have time-to-event data? | Statistics...
I have atherosclerosis data set matched for age and gender. My doctoral advisory members suggested me to perform conditional logistic regression instead of...
The Missing Observations in the Time Series of the Dependent Variable and Their Location in the Simple Linear Regression...The Missing Observations in the Time Series of the Dependent Variable and Their Location in the Simple Linear Regression...
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 ...
ASSESSMENT OF NON-LINEAR REGRESSION APPROACH FOR BACK-ANALYSIS ON TUNNELLING-INDUCED SURFACE SETTLEMENT- A CASE STUDY IN HO...ASSESSMENT OF NON-LINEAR REGRESSION APPROACH FOR BACK-ANALYSIS ON TUNNELLING-INDUCED SURFACE SETTLEMENT- A CASE STUDY IN HO...
ASSESSMENT OF NON-LINEAR REGRESSION APPROACH FOR BACK-ANALYSIS ON TUNNELLING-INDUCED SURFACE SETTLEMENT- A CASE STUDY IN HO CHI MINH CITY
Articles: How Statists Are Getting Away with ItArticles: How Statists Are Getting Away with It
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, ...
LETTER TO THE EDITOR: Lack of Efficacy of the Combination of Pamidronate and Vitamin D on Regression of Prostate Cancer in the...LETTER TO THE EDITOR: Lack of Efficacy of the Combination of Pamidronate and Vitamin D on Regression of Prostate Cancer in the...
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 ...
11 Simple Linear Regression Models Essay - 1362 Words | Major Tests11 Simple Linear Regression Models Essay - 1362 Words | Major Tests
Performance Evaluation: Simple Linear Regression Models Hongwei Zhang http://www.cs.wayne.edu/~hzhang Statistics is the art of lying by means of figures. ---...
FRI0143 Prevalence and determinants of peripheral endothelial dysfunction in a cohort of rheumatoid arthritis patients:...FRI0143 Prevalence and determinants of peripheral endothelial dysfunction in a cohort of rheumatoid arthritis patients:...
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); ...
City Research Online - Who benefits from reducing the cost of formality? Quantile regression discontinuity analysisCity Research Online - Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis
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 ...
DiVA - Search resultDiVA - Search result
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 ...
Simple Linear Regression | Introduction to Statistics | JMPSimple Linear Regression | Introduction to Statistics | JMP
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.
Data-driven approach to machine condition prognosis using least square regression trees - University of Huddersfield RepositoryData-driven approach to machine condition prognosis using least square regression trees - University of Huddersfield Repository
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 AnalysisLinear Regression Analysis
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 ...
Free Regression Analysis Essay Examples, Paper Sample TopicsFree Regression Analysis Essay Examples, Paper Sample Topics
Download an essay example of Regression Analysis on FreeEssayHelp. Huge database of popular free topics, dozen types of essays, term papers, case studies on Regression Analysis.
Linear RegressionLinear Regression
Simple linear regression. How to define least-squares regression line. How to find coefficient of determination. Includes video lesson on regression analysis.
Regression Analysis with R【全本 书评 在线阅读】-当当云阅读Regression Analysis with R【全本 书评 在线阅读】-当当云阅读
Build effective regression models in R to extract valuable insights from real data About This Book ? Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values ? From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R ? A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Who This Book Is For This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful What You Will Learn ? Get started with the journey of data
Identifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation TechniqueIdentifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation Technique
The use of [1] Box-Cox power transformation in regression analysis is now common; in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.
Modern Regression Methods, 2nd Edition | Regression Analysis | General & Introductory Statistics | Subjects | WileyModern Regression Methods, 2nd Edition | Regression Analysis | General & Introductory Statistics | Subjects | Wiley
Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one.The American Statistician The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems. (Computing Reviews) 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. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression.
Factors associated with overweight: are the conclusions influenced by choice of the regression method? | BMC Public Health |...Factors associated with overweight: are the conclusions influenced by choice of the regression method? | BMC Public Health |...
Different analytical techniques have been used to study the determinants of overweight. However, certain commonly used techniques may be limited by the continuous nature and skewed distribution of body mass index (BMI) data. In this article, different regression models are compared to identify the best approach for analysing predictors of BMI. Data collected on 2270 nurses at 18 public hospitals in Rio de Janeiro, RJ (2010-2011) were analysed (80.6 % of the respondents). The explanatory variables considered were age, marital status, race/colour, mothers schooling, domestic overload, years worked at night, consumption of fried food, physical inactivity, self-rated health and BMI at age 20 years. In addition to gamma regression, regarded as the reference method for selecting the set of explanatory variables described here, other modelling strategies - including linear, quantile (for the 0.25, 0.50 and 0.75 quantiles), binary and multinomial logistic regression - were compared in terms of final results
Regression Analysis by J. Holton Wilson - Free PDF Ebooks DownloadsRegression Analysis by J. Holton Wilson - Free PDF Ebooks Downloads
Overview: The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The book provides a non-theoretical treatment that is accessible to readers with even a limited statistical background. This book describes exactly how regression models are developed and evaluated. The data used in the book are the kind of data managers are faced with in the real world.. The book provides instructions and screen shots for using Microsoft Excel to build business/economic regression models. Upon completion, the reader will be able to interpret the output of the regression models and evaluate the models for accuracy and shortcomings ...
Search Results for polynomial regression analysis | ASHSSearch Results for "polynomial regression analysis" | ASHS
NO 3 ) 2 , H 3 BO 3 , and KNO 3 concentration was evaluated using polynomial regression analysis and models were evaluated by R 2 values. Osmoticum concentration was initially evaluated using a contour plot (Minitab 15; Minitab Inc., State College. ...
Linear Regression Analysis (Math 212) by NPTEL On IIT Kanpur - Regression Online Course/MOOCLinear Regression Analysis (Math 212) by NPTEL On IIT Kanpur - Regression Online Course/MOOC
Course Description: The course focuses on the topics of linear regression analysis. The emphasis will be more on the development of tools from the statistical theories and concept along with their utility in real life data applications. The course starts with a description of need of regression analysis and lays the foundation of simple linear regression model. ...
A Quadratic Regression Analysis of the Effect of Three Levels of NPK Fertilizer on the Yield of Yellow MaizeA Quadratic Regression Analysis of the Effect of Three Levels of NPK Fertilizer on the Yield of Yellow Maize
Fertilizers are essential to modern agriculture; their overuse can have harmful effects on plants, crops and soil quality. Thus, the study seeks to investigate, if (actually) the trio of Nitrogen, Phosphorus and Potassium (NPK) contribute to the growth and yield of yellow maize, and to determine at what proportion each of the elements is to be applied for optimum yield. Our findings revealed that Nitrogen and Phosphoric fertilizer contributed significantly to the yield of yellow maize while there was no significant effect of Potassium Further analysis on the mean separation of Nitrogen and Phosphorus using Duncans Multiple Range Test-(DMRT) showed Nitrogen at 50 kg/ha as significantly higher than the other levels. For phosphorus, its effect at 20 kg/ha was significantly higher than the other levels. Thus, the derived quadratic model: .
Politics & International Relations: Political Research Methods - RoutledgePolitics & International Relations: Political Research Methods - Routledge
By Christer Thrane. This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm…. Paperback - 2019-10-28 ...
Synchronous crop failures and climate-forced production variability | Science AdvancesSynchronous crop failures and climate-forced production variability | Science Advances
Fig. 1 El Niño climate teleconnections in JAS (July, August, September), NDJ (November, December, January), and MAM (March, April, May).. Partial regression coefficients for the standardized ENSO time expansion coefficients 1 + 2 (A1 * σA1 + A2 * σA2 in Eq. 2) from the multilinear regression analysis. Partial regression coefficients are shown for three stages in the life cycle of an El Niño event: a developing El Niño (A), a mature El Niño (B), and a decaying El Niño (C). Colors are sea surface temperature anomalies (in °C) of the ocean and soil moisture anomalies (in kg/m2) over land; contours are 200-hPa geopotential height anomalies (contours every 5 hPa), and vectors are winds at 925 hPa. ...
EpistasisEpistasis
Variance functionVariance function
Probit modelProbit model
Explained variationExplained variation
Errors and residualsErrors and residuals
Tikhonov regularizationTikhonov regularization
Linear least squaresLinear least squares
Gauss-Markov theoremGauss-Markov theorem
Probability distribution fittingProbability distribution fitting
Minimum wage in the United StatesMinimum wage in the United States
Новый Палгрейв: словарь по экономике - ВикипедияНовый Палгрейв: словарь по экономике - Википедия
Peer pressurePeer pressure
Gumbel distributionGumbel distribution
Greek letters used in mathematics, science, and engineeringGreek letters used in mathematics, science, and engineering
Stimulus-response modelStimulus-response model
PressPress
Imputation (statistics)Imputation (statistics)
Linear probability modelLinear probability model
Hedge relationship (finance)Hedge relationship (finance)
European Programme for Intervention Epidemiology TrainingEuropean Programme for Intervention Epidemiology Training
Level of measurementLevel of measurement
Edward TufteEdward Tufte
John TukeyJohn Tukey
Projection matrixProjection matrix
Coefficient of determinationCoefficient of determination
Grey box modelGrey box model
Mikhail KharitMikhail Kharit
Homogeneity (statistics)Homogeneity (statistics)
Observational errorObservational error
ChickenpoxChickenpox
Alternative medicineAlternative medicine
Quantitative trait locusQuantitative trait locus
Cushings diseaseCushing's disease
Internet of thingsInternet of things
Mann-Whitney U testMann-Whitney U test
Refractive indexRefractive index
Mark and recaptureMark and recapture
BioarchaeologyBioarchaeology
Barney and Betty HillBarney and Betty Hill
SpirometrySpirometry
කොලෙස්ටරෝල් - විකිපීඩියා, නිදහස් විශ්වකෝෂයකොලෙස්ටරෝල් - විකිපීඩියා, නිදහස් විශ්වකෝෂය
AltruismAltruism
Isotonic regressionIsotonic regression
Hunter syndromeHunter syndrome
AnxietyAnxiety
Narcissistic personality disorderNarcissistic personality disorder
Food chemistryFood chemistry
Probability distributionProbability distribution
Vitamin CVitamin C
Sara Josephine BakerSara Josephine Baker
AutophagyAutophagy
AutophagyAutophagy
LamotrigineLamotrigine
InfluenzaInfluenza
Strok bahasa Indonesia, ensiklopedia bebasStrok bahasa Indonesia, ensiklopedia bebas
Vitamin D toxicityVitamin D toxicity
Good manufacturing practiceGood manufacturing practice
EtestEtest
DiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaHumanitiesInformation ScienceNamed GroupsHealth CareGeographicals
Regression AnalysisLogistic ModelsRisk FactorsLinear ModelsCross-Sectional StudiesMultivariate AnalysisProspective StudiesCohort StudiesRetrospective StudiesQuestionnairesAge FactorsPredictive Value of TestsOdds RatioCase-Control StudiesTime FactorsFollow-Up StudiesSex FactorsPrognosisPrevalenceTreatment OutcomeSocioeconomic FactorsRisk AssessmentSeverity of Illness IndexNeoplasm Regression, SpontaneousBody Mass IndexProportional Hazards ModelsUnited StatesJapanBiological MarkersLongitudinal StudiesSmokingPregnancyIncidenceInfant, NewbornChinaReproducibility of ResultsROC CurveChi-Square DistributionAnalysis of VarianceComorbidityConfidence IntervalsSensitivity and SpecificityGenotypeSurvival AnalysisEducational StatusObesityNetherlandsHealth StatusEuropean Continental Ancestry GroupHealth SurveysReference ValuesEpidemiologic MethodsModels, StatisticalDisease ProgressionPoisson DistributionSurvival RateBlood PressureAsian Continental Ancestry GroupDepressionHypertensionRiskSex DistributionKaplan-Meier EstimateAge DistributionAfrican AmericansDemographyTaiwanAlcohol DrinkingQuality of LifeAnthropometryUrban PopulationDiabetes Mellitus, Type 2RegistriesNorwayBody WeightAgingChronic DiseaseStatistics as TopicDietSocial ClassCardiovascular DiseasesGenetic Predisposition to DiseasePolymorphism, Single NucleotideStress, PsychologicalResidence CharacteristicsRural PopulationOccupational ExposureC-Reactive ProteinGestational AgeRegression (Psychology)StrokeRepublic of KoreaStatistics, NonparametricLeast-Squares AnalysisBrazilPolymorphism, GeneticData Interpretation, StatisticalBreast NeoplasmsDenmarkMagnetic Resonance ImagingGermanyCoronary Artery DiseaseLife StyleHospitalizationSwedenRecurrenceProbabilityAlgorithmsBirth WeightHealth Knowledge, Attitudes, PracticeHispanic AmericansBlood GlucoseHIV InfectionsPostoperative ComplicationsKidney Failure, ChronicRenal DialysisDiabetes MellitusActivities of Daily LivingHospital MortalityOccupational DiseasesItalyEmploymentSelf ReportData CollectionForecastingNeoplasm StagingSex CharacteristicsAfrican Continental Ancestry GroupHealth Status IndicatorsBody CompositionIndiaSocial SupportCaliforniaHealth BehaviorOutcome Assessment (Health Care)Environmental ExposureFinlandTomography, X-Ray ComputedBody HeightIncomeSpainMyocardial InfarctionMothersCreatinineConfounding Factors (Epidemiology)ExerciseLength of StayStudentsOverweightSeasonsNeuropsychological TestsAdaptation, PsychologicalTexasCognition DisordersParityPovertyNeoplasmsCoronary DiseaseMotor ActivityUrban HealthMortalityPopulation SurveillanceSubstance-Related DisordersSelf ConceptMaternal AgeCholesterol, HDLFamily CharacteristicsDisability EvaluationRemission, SpontaneousParentsAcute DiseaseTumor Markers, BiologicalInterviews as TopicMarital StatusGlomerular Filtration RateArea Under CurveAnxietyPsychiatric Status Rating ScalesWaist CircumferenceHemoglobin A, GlycosylatedOccupationsWorkplaceTurkeyCause of DeathAdolescent BehaviorAttitude to HealthCanadaPatient Acceptance of Health CareMetabolic Syndrome XBody Weights and MeasuresHealth Services AccessibilityBiometryNutritional StatusLung NeoplasmsLipidsInsulin ResistanceHealth Care SurveysContinental Population GroupsCausalityAllelesPregnancy Outcome
3 Ways to Run Regression Analysis in Microsoft Excel - wikiHow3 Ways to Run Regression Analysis in Microsoft Excel - wikiHow
Linear Regression: Models, Analysis and Applications - Nova Science PublishersLinear Regression: Models, Analysis and Applications - Nova Science Publishers
Multiple Regression Analysis and Forecasting User GuideMultiple Regression Analysis and Forecasting User Guide
NLREG -- Nonlinear Regression Analysis ProgramNLREG -- Nonlinear Regression Analysis Program
ASQ: Nonlinear Regression AnalysisASQ: Nonlinear Regression Analysis
Lack of Fit in Ordinal Regression -- Analysis/Alternatives? | Physics Forums - The Fusion of Science and CommunityLack of Fit in Ordinal Regression -- Analysis/Alternatives? | Physics Forums - The Fusion of Science and Community
Regression Analysis | SpringerLinkRegression Analysis | SpringerLink
Regression Analysis | SpringerLinkRegression Analysis | SpringerLink
Summary - Regression Analysis with Python [Book]Summary - Regression Analysis with Python [Book]
Applied Regression Analysis - Norman Richard Draper - Google BooksApplied Regression Analysis - Norman Richard Draper - Google Books
Genomic regression analysis of coordinated expression | Nature CommunicationsGenomic regression analysis of coordinated expression | Nature Communications
Insights for ArcGIS - Regression Analysis Video | EsriInsights for ArcGIS - Regression Analysis Video | Esri
Linear regression - Mastering Python Data Analysis [Book]Linear regression - Mastering Python Data Analysis [Book]
Regression Analysis | General & Introductory Statistics | Subjects | WileyRegression Analysis | General & Introductory Statistics | Subjects | Wiley
Regression Analysis | SAGE Publications IncRegression Analysis | SAGE Publications Inc
Regression Analysis - 1445 Words | BartlebyRegression Analysis - 1445 Words | Bartleby
Regression Analysis of Count Data - Cambridge University PressRegression Analysis of Count Data - Cambridge University Press
Implement Seemingly Unrelated Regression AnalysesImplement Seemingly Unrelated Regression Analyses
계량문제1 | Econometrics | Regression Analysis계량문제1 | Econometrics | Regression Analysis
Bilinear Regression Analysis - An Introduction | Dietrich von Rosen | SpringerBilinear Regression Analysis - An Introduction | Dietrich von Rosen | Springer
Illusions in Regression Analysis by J. Scott Armstrong :: SSRNIllusions in Regression Analysis by J. Scott Armstrong :: SSRN
Remedial Education and Student Achievement: A Regression-Discontinuity AnalysisRemedial Education and Student Achievement: A Regression-Discontinuity Analysis
Efficiency in banking: a meta-regression analysisEfficiency in banking: a meta-regression analysis
Regression Analysis : Correlation Between A Response... | BartlebyRegression Analysis : Correlation Between A Response... | Bartleby
Extreme ranking analysis in robust ordinal regressionExtreme ranking analysis in robust ordinal regression
Applied Logistic Regression Analysis | SAGE Publications IncApplied Logistic Regression Analysis | SAGE Publications Inc
STAT3341 | REGRESSION ANALYSIS | Birzeit UniversitySTAT3341 | REGRESSION ANALYSIS | Birzeit University
Regression analysis basicsRegression analysis basics
Linear regression analysis | Define Linear regression analysis at Dictionary.comLinear regression analysis | Define Linear regression analysis at Dictionary.com
Regression Analysis - Knowledge CenterRegression Analysis - Knowledge Center
Analysis of Vehicle Customer Satisfaction Data using the Binary Logistic RegressionAnalysis of Vehicle Customer Satisfaction Data using the Binary Logistic Regression
Regression analysis facts, information, pictures | Encyclopedia.com articles about Regression analysisRegression analysis facts, information, pictures | Encyclopedia.com articles about Regression analysis
Testing Heteroscedasticity in Nonparametric Regression Based on Trend AnalysisTesting Heteroscedasticity in Nonparametric Regression Based on Trend Analysis
Regression AnalysisRegression Analysis
Regression AnalysisRegression Analysis
DiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaHumanitiesInformation ScienceNamed GroupsHealth CareGeographicals
Regression AnalysisLogistic ModelsRisk FactorsLinear ModelsCross-Sectional StudiesMultivariate AnalysisProspective StudiesCohort StudiesRetrospective StudiesQuestionnairesAge FactorsPredictive Value of TestsOdds RatioCase-Control StudiesTime FactorsFollow-Up StudiesSex FactorsPrognosisPrevalenceTreatment OutcomeSocioeconomic FactorsRisk AssessmentSeverity of Illness IndexNeoplasm Regression, SpontaneousBody Mass IndexProportional Hazards ModelsUnited StatesJapanBiological MarkersLongitudinal StudiesSmokingPregnancyIncidenceInfant, NewbornChinaReproducibility of ResultsROC CurveChi-Square DistributionAnalysis of VarianceComorbidityConfidence IntervalsSensitivity and SpecificityGenotypeSurvival AnalysisEducational StatusObesityNetherlandsHealth StatusEuropean Continental Ancestry GroupHealth SurveysReference ValuesEpidemiologic MethodsModels, StatisticalDisease ProgressionPoisson DistributionSurvival RateBlood PressureAsian Continental Ancestry GroupDepressionHypertensionRiskSex DistributionKaplan-Meier EstimateAge DistributionAfrican AmericansDemographyTaiwanAlcohol DrinkingQuality of LifeAnthropometryUrban PopulationDiabetes Mellitus, Type 2RegistriesNorwayBody WeightAgingChronic DiseaseStatistics as TopicDietSocial ClassCardiovascular DiseasesGenetic Predisposition to DiseasePolymorphism, Single NucleotideStress, PsychologicalResidence CharacteristicsRural PopulationOccupational ExposureC-Reactive ProteinGestational AgeRegression (Psychology)StrokeRepublic of KoreaStatistics, NonparametricLeast-Squares AnalysisBrazilPolymorphism, GeneticData Interpretation, StatisticalBreast NeoplasmsDenmarkMagnetic Resonance ImagingGermanyCoronary Artery DiseaseLife StyleHospitalizationSwedenRecurrenceProbabilityAlgorithmsBirth WeightHealth Knowledge, Attitudes, PracticeHispanic AmericansBlood GlucoseHIV InfectionsPostoperative ComplicationsKidney Failure, ChronicRenal DialysisDiabetes MellitusActivities of Daily LivingHospital MortalityOccupational DiseasesItalyEmploymentSelf ReportData CollectionForecastingNeoplasm StagingSex CharacteristicsAfrican Continental Ancestry GroupHealth Status IndicatorsBody CompositionIndiaSocial SupportCaliforniaHealth BehaviorOutcome Assessment (Health Care)Environmental ExposureFinlandTomography, X-Ray ComputedBody HeightIncomeSpainMyocardial InfarctionMothersCreatinineConfounding Factors (Epidemiology)ExerciseLength of StayStudentsOverweightSeasonsNeuropsychological TestsAdaptation, PsychologicalTexasCognition DisordersParityPovertyNeoplasmsCoronary DiseaseMotor ActivityUrban HealthMortalityPopulation SurveillanceSubstance-Related DisordersSelf ConceptMaternal AgeCholesterol, HDLFamily CharacteristicsDisability EvaluationRemission, SpontaneousParentsAcute DiseaseTumor Markers, BiologicalInterviews as TopicMarital StatusGlomerular Filtration RateArea Under CurveAnxietyPsychiatric Status Rating ScalesWaist CircumferenceHemoglobin A, GlycosylatedOccupationsWorkplaceTurkeyCause of DeathAdolescent BehaviorAttitude to HealthCanadaPatient Acceptance of Health CareMetabolic Syndrome XBody Weights and MeasuresHealth Services AccessibilityBiometryNutritional StatusLung NeoplasmsLipidsInsulin ResistanceHealth Care SurveysContinental Population GroupsCausalityAllelesPregnancy Outcome
VarianceCoefficientPredictorsNonlinear regression analysisModelDataModelsMethodsMultivariateInterpretExamplesMultipleDependent variableQuantileOrdinal regressionParametersApplication of regressionResultsEstimateEstimationNonlinear RegressionMultinomial logistic regressionDataModelsGeographically Weighted RegressionVarianceLeast squaresEconometricsDependentPurpose of regressionVariablesSimple and multiple linear regTypes of regression analysisCorrelation and regression analysisMeta-regression analysisBinaryCurveCommonlyParametricSupport vector regressionLinear Regression ChannelStatistical analysesQuantile regressionSemiparametric regressionPerform a regressionMultivariate StatisticsSeemingly unrelatedApplications of regressionPredict
Variance1
Coefficient3
Predictors2
Nonlinear regression analysis2
Model9
Data12
Models7
Methods2
Multivariate2
Interpret1
Examples1
Multiple2
Dependent variable1
  • On the other hand we can say that, Regression analysis is the statistical technique that identifies the relationship between two or more quantitative variables: a dependent variable, whose value is to be predicted, and an independent or explanatory variable (or variables), about which knowledge is available. (assignmentpoint.com)
Quantile4
Ordinal regression1
Parameters1
Application of regression1
  • Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. (statisticsviews.com)
Results3
Estimate1
Estimation10
Nonlinear Regression8
Multinomial logistic regression1
Data60
  • Multivariate data analysis (7th ed. (springer.com)
  • Analysis of panel data (3rd ed. (springer.com)
  • 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. (nature.com)
  • 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. (nature.com)
  • However, no method exists to remove the confounding effect of CNAs in the analysis of gene-gene co-expression using cancer transcriptome data. (nature.com)
  • 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. (nature.com)
  • As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. (sagepub.com)
  • Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. (sagepub.com)
  • The least-squares regression equation computed from their data is [pic]. (bartleby.com)
  • This example shows how to prepare exogenous data for several seemingly unrelated regression (SUR) analyses. (mathworks.com)
  • 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. (scribd.com)
  • Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. (springer.com)
  • The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. (sagepub.com)
  • Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. (wikipedia.org)
  • Regression analysis can be very helpful for analyzing large amounts of data and making forecasts and predictions. (wikihow.com)
  • 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. (wikihow.com)
  • Data Analysis and choose Regression from the list of tools. (wikihow.com)
  • Magri, M., "Analysis of Vehicle Customer Satisfaction Data using the Binary Logistic Regression," SAE Technical Paper 2008-36-0199, 2008, https://doi.org/10.4271/2008-36-0199 . (sae.org)
  • 1- Prepare a paper examining a regression analysis on your collected data. (brainmass.com)
  • 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. (ecampus.com)
  • 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. (physicsforums.com)
  • Software emphasis will be given to GeoDa and R for exploratory spatial data analysis and modeling. (umich.edu)
  • Meta-analysis, a statistical method of pooling data from studies included in a systematic review, is often compromised by heterogeneity of its results. (nih.gov)
  • It outlines theoretical principals underlying the techniques utilized in regression analysis and illustrates their application on a variety of data sets. (routledge.com)
  • Data analysis and regression : a second course in statistics. (worldcat.org)
  • I thought you might be interested in this item at http://www.worldcat.org/oclc/693419875 Title: Data analysis and regression : a second course in statistics. (worldcat.org)
  • Add tags for "Data analysis and regression : a second course in statistics. (worldcat.org)
  • To equip students with the necessary skills and knowledge to allow analysis of data with an awareness of effect modification and confounding. (york.ac.uk)
  • 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. (york.ac.uk)
  • Reviews the book "Regression Analysis of Count Data," by A. Colin Cameron and Pravin K. Trivedi. (ebscohost.com)
  • The chapter uses the Advertising data set available from the book's website: Testing the assumptions of linear regression. (pearltrees.com)
  • 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. (coursera.org)
  • As this is a methodology I simply have to state how I will feed the data into regression analysis as opposed to enacting it. (mathhelpforum.com)
  • Be able to describe data and carry out linear and logistic regression and non-parametric statistics. (york.ac.uk)
  • Analyses throughout the text are performed using Stata Version 9, and an accompanying FTP site contains the data sets used in the book. (worldcat.org)
  • 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. (worldcat.org)
  • Standard least squares regressions were performed on the data to relate particular ship characteristics to deadweight. (bitre.gov.au)
  • This will add the Data Analysis tools to the Data tab of your Excel ribbon. (ablebits.com)
  • Relevant Skills and Experience I know statistic and data analysis very well. (freelancer.com)
  • 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. (safaribooksonline.com)
  • 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. (umn.edu)
  • Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. (springer.com)
  • Data analysis, particularly users of S-PLUS, with experience in the application of these tools will benefit the most from this book. (springer.com)
  • Statistical techniques such as regression analysis are tools of action that enable accountants to make financial data meaningful to their clients. (thefreedictionary.com)
  • Chen, Y. , Ma, J. and Wang, S. (2020) Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data. (scirp.org)
  • Journal of Data Analysis and Information Processing , 8 , 1-19. (scirp.org)
  • Many studies have conducted the safety analysis of pedestrian crashes based on zone-level data and examined a lot of related features. (scirp.org)
  • 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. (scirp.org)
  • The reproducibility of using R language to perform spatial data analysis is unparalleled, which includes plenty of spatial packages for different purposes. (scirp.org)
  • 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. (scirp.org)
  • By performing a regression analysis on this survey data, we can determine whether or not these variables have impacted overall attendee satisfaction, and if so, to what extent. (surveygizmo.com)
  • 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). (scirp.org)
  • Computational Statistics & Data Analysis, 54, 219-232. (scirp.org)
  • We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. (hu-berlin.de)
  • In linear regression we find the "best" line through the data. (fruition.net)
  • an interesting application of this is " circular regression " where a circle is fitted to a set of data points. (nlreg.com)
  • To avoid making wrong inferences, regression toward the mean must be considered when designing scientific experiments and interpreting data. (scribd.com)
  • Sir Francis Galton first observed the phenomenon in the context of simple linear regression of data points. (scribd.com)
  • Simply put, Data Analysis Using Regression and Multilevel/Hierarchical Models is the best place to learn how to do serious empirical research. (columbia.edu)
  • Data Analysis Using Regression and Multilevel/Hierarchical Models is destined to be a classic! (columbia.edu)
Models37
Geographically Weighted Regression2
Variance1
Least squares6
Econometrics1
Dependent17
Purpose of regression1
  • This technique will not only classify the original test cases but will also generate new test cases required for the purpose of regression testing. (igi-global.com)
Variables14
Simple and multiple linear reg1
Types of regression analysis2
  • While there are other types of regression analysis, teaching regression is not the objective here. (isixsigma.com)
  • The results of the three types of regression analysis of the measurements in the table above are shown below. (libreoffice.org)
Correlation and regression analysis3
Meta-regression analysis6
  • 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. (repec.org)
  • Efficiency in banking: a meta-regression analysis ," International Review of Applied Economics , Taylor & Francis Journals, vol. 30(1), pages 112-149, January. (repec.org)
  • Equilibrium exchange rates in Central and Eastern Europe: A meta-regression analysis ," Journal of Banking & Finance , Elsevier, vol. 30(5), pages 1359-1374, May. (repec.org)
  • 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. (repec.org)
  • 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. (repec.org)
  • Equilibrium Exchange Rates in Central and Eastern Europe: A Meta-Regression Analysis ," CEPR Discussion Papers 4869, C.E.P.R. Discussion Papers. (repec.org)
Binary3
Curve7
Commonly7
Parametric2
Support vector regression1
  • D. Basak , S. Pal and D. C. Patranabis , Support vector regression, Neural Information Processing-Letters and Reviews , 11 (2007), 203-224. (aimsciences.org)
Linear Regression Channel4
Statistical analyses1
Quantile regression1
Semiparametric regression1
  • A semiparametric regression imputation estimator and an empirical likelihood based one for the mean of the response variable are defined. (hu-berlin.de)
Perform a regression1
Multivariate Statistics1
Seemingly unrelated1
Applications of regression1
  • The book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. (ecampus.com)
Predict3
What is Regression Analysis? definition and meaning
What is Regression Analysis? definition and meaning (investorwords.com)
3 Ways to Run Regression Analysis in Microsoft Excel - wikiHow
3 Ways to Run Regression Analysis in Microsoft Excel - wikiHow (wikihow.com)
Regression Analysis - Knowledge Center
Regression Analysis - Knowledge Center (12manage.com)
CiteSeerX - Citation Query Quantile treatment effects in the regression discontinuity design.
CiteSeerX - Citation Query Quantile treatment effects in the regression discontinuity design. (citeseer.ist.psu.edu)
SU19: Statistical Modeling and Regression Analysis
SU19: Statistical Modeling and Regression Analysis (edx.org)
Regression Analysis - Inkings
Regression Analysis - Inkings (davidmacinnisgill.com)
PLOS ONE: Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability...
PLOS ONE: Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability... (journals.plos.org)
Neighborhood greenspace and health in a large urban center | Scientific Reports
Neighborhood greenspace and health in a large urban center | Scientific Reports (nature.com)
Regression analysis
Regression analysis (pavo.ihmc.us)
Epistasis - Wikipedia
Epistasis - Wikipedia (en.wikipedia.org)
Urinary CE-MS peptide marker pattern for detection of solid tumors | Scientific Reports
Urinary CE-MS peptide marker pattern for detection of solid tumors | Scientific Reports (nature.com)
Evaluation of a Digital Diabetes Prevention Program Adapted for Low-Income Patients, 2016-2018
Evaluation of a Digital Diabetes Prevention Program Adapted for Low-Income Patients, 2016-2018 (cdc.gov)
Ancient DNA, pig domestication, and the spread of the Neolithic into Europe | PNAS
Ancient DNA, pig domestication, and the spread of the Neolithic into Europe | PNAS (pnas.org)
Step Back: Lessons for U.S. Foreign Policy from the Failed War on Terror | Cato Institute
Step Back: Lessons for U.S. Foreign Policy from the Failed War on Terror | Cato Institute (cato.org)
Regression Analysis | SpringerLink
Regression Analysis | SpringerLink (link.springer.com)
Bilinear Regression Analysis - An Introduction | Dietrich von Rosen | Springer
Bilinear Regression Analysis - An Introduction | Dietrich von Rosen | Springer (springer.com)
Technical efficiency of public health centers in three districts in Ethiopia: two-stage data envelopment analysis | SpringerLink
Technical efficiency of public health centers in three districts in Ethiopia: two-stage data envelopment analysis | SpringerLink (link.springer.com)
The spectrum of thyroid disease and risk of new onset atrial fibrillation: a large population cohort study | The BMJ
The spectrum of thyroid disease and risk of new onset atrial fibrillation: a large population cohort study | The BMJ (bmj.com)
Nanopore sequencing at Mars, Europa, and microgravity conditions | npj Microgravity
Nanopore sequencing at Mars, Europa, and microgravity conditions | npj Microgravity (nature.com)
Variance function - Wikipedia
Variance function - Wikipedia (en.wikipedia.org)
Applied Regression Analysis - Norman R. Draper, Norman Richard Draper, Harry Smith - Google Books
Applied Regression Analysis - Norman R. Draper, Norman Richard Draper, Harry Smith - Google Books (books.google.com)
Craniofacial anthropometric investigation of relationships between the nose and nasal aperture using 3D computed tomography of...
Craniofacial anthropometric investigation of relationships between the nose and nasal aperture using 3D computed tomography of... (nature.com)