The following outline is provided as an overview of and topical guide to regression analysis:
Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables (Y) and one or more independent variables (X).
Overview articles
Regression analysis Linear regression
Non-statistical articles related to regression Least squares Linear least squares (mathematics) Non-linear least squares Least absolute deviations Curve fitting Smoothing Cross-sectional study
Basic statistical ideas related to regression Conditional expectation Correlation Correlation coefficient Mean square error Residual sum of squares Explained sum of squares Total sum of squares
Visualization Scatterplot
Linear regression based on least squares General linear model Ordinary least squares Generalized least squares Simple linear regression Trend estimation Ridge regression Polynomial regression Segmented regression Nonlinear regression
Generalized linear models
Generalized linear models Logistic regression Multinomial logit Ordered logit Probit model Multinomial probit Ordered probit Poisson regression Maximum likelihood Cochrane–Orcutt estimation
Computation Numerical methods for linear least squares
Inference for regression models F-test t-test Lack-of-fit sum of squares Confidence band Coefficient of determination Multiple correlation Scheffé's method
Challenges to regression modeling
Autocorrelation Cointegration Multicollinearity Homoscedasticity and heteroscedasticity Lack of fit Non-normality of errors Outliers
Diagnostics for regression models Regression model validation Studentized residual Cook's distance Variance inflation factor DFFITS Partial residual plot Partial regression plot Leverage Durbin–Watson statistic Condition number
Formal aids to model selection
Model selection Mallows's Cp Akaike information criterion Bayesian information criterion Hannan–Quinn information criterion Cross validation
Robust regression
Robust regression
Terminology
Linear model — relates to meaning of "linear" Dependent and independent variables Errors and residuals in statistics Hat matrix Trend-stationary process Cross-sectional data Time series
Methods for dependent data
Mixed model Random effects model Hierarchical linear models
Nonparametric regression
Nonparametric regression Isotonic regression
Semiparametric regression Semiparametric regression Local regression
Other forms of regression
Total least squares regression Deming regression Errors-in-variables model Instrumental variables regression Quantile regression Generalized additive model Autoregressive model Moving average model Autoregressive moving average model Autoregressive integrated moving average Autoregressive conditional heteroskedasticity
See also
Prediction Design of experiments Data transformation Box–Cox transformation Machine learning Analysis of variance Causal inference