|Terms & Properties|
Concept Unique Identifier (CUI): C0034980
NCI Thesaurus Code: C53237 (see NCI Thesaurus info)
Semantic Type: Intellectual Product
NCIt Definition: Regression analysis provides a "best-fit" mathematical equation for the relationship between the dependent variable (response) and independent variable(s) (covariates). There are two major classes of regression - parametric and non-parametric. Parametric regression requires choice of the regression equation with one or a greater number of unknown parameters. Linear regression, in which a linear relationship between the dependent variable and independent variables is posited, is an example. The aim of parametric regression is to find the values of these parameters which provide the best fit to the data. The number of parameters is usually much smaller than the number of data points. In contrast, the non-parametric regression requires no such a choice of the regression equation.
MSH Definition: 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.
Synonyms & Abbreviations: (see Synonym Details)
External Source Codes:
|NCI Thesaurus Code||C53237 (see NCI Thesaurus info)|
|AN||IM GEN only; coord NIM with specific disease or other concept (IM); no qualif; specify geog if pertinent||MSH|
|HN||80(73); was see under STATISTICS 1975-79||MSH|
|PM||80; was see under STATISTICS 1975-79||MSH|
Additional Concept Data: (none)