# Outline of regression analysis

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Outline of regression analysis

In statistics, regression analysis includes any technique for learning about the relationship between one or more dependent variables Y and one or more independent variables X.

The following outline is an overview and guide to the variety of topics included within the subject of regression analysis.

## General regression analysis concepts

 Overview articles Non-statistical articles related to regression Basic statistical ideas related to regression Visualization 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 Computation Numerical methods for linear least squares Inference for regression models Challenges to regression modeling Autocorrelation Cointegration Multicollinearity Homoscedasticity and heteroscedasticity Lack of fit Non-normality of errors Outliers Diagnostics for regression models Formal aids to model selection Model selection Mallows' Cp Akaike information criterion Bayesian information criterion Hannan–Quinn information criterion Cross validation Robust regression Terminology Linear model — relates to meaning of "linear" Dependent and independent variables Errors and residuals in statistics Hat matrix Trend stationary Cross-sectional data Time series Methods for dependent data Mixed model Random effects model Hierarchical linear models Nonparametric regression Semiparametric 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

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