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).
Regression analysis
Linear regression
Least squares
Linear least squares (mathematics)
Non-linear least squares
Least absolute deviations
Curve fitting
Smoothing
Cross-sectional study
Conditional expectation
Correlation
Correlation coefficient
Mean square error
Residual sum of squares
Explained sum of squares
Total sum of squares
Scatterplot
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
Logistic regression
Multinomial logit
Ordered logit
Probit model
Multinomial probit
Ordered probit
Poisson regression
Maximum likelihood
Cochrane–Orcutt estimation
Numerical methods for linear least squares
F-test
t-test
Lack-of-fit sum of squares
Confidence band
Coefficient of determination
Multiple correlation
Scheffé's method
Autocorrelation
Cointegration
Multicollinearity
Homoscedasticity and heteroscedasticity
Lack of fit
Non-normality of errors
Outliers
Regression model validation
Studentized residual
Cook's distance
Variance inflation factor
DFFITS
Partial residual plot
Partial regression plot
Leverage
Durbin–Watson statistic
Condition number
Model selection
Mallows's Cp
Akaike information criterion
Bayesian information criterion
Hannan–Quinn information criterion
Cross validation
Robust regression
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
Mixed model
Random effects model
Hierarchical linear models
Nonparametric regression
Isotonic regression
Semiparametric regression
Local regression
Total least squares regression
Deming regression
Errors-in-varia
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