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
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
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)
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
A 51-amino acid pancreatic hormone that plays a major role in the regulation of glucose metabolism, directly by suppressing endogenous glucose production (GLYCOGENOLYSIS; GLUCONEOGENESIS) and indirectly by suppressing GLUCAGON secretion and LIPOLYSIS. Native insulin is a globular protein comprised of a zinc-coordinated hexamer. Each insulin monomer containing two chains, A (21 residues) and B (30 residues), linked by two disulfide bonds. Insulin is used as a drug to control insulin-dependent diabetes mellitus (DIABETES MELLITUS, TYPE 1)
