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
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
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
- Probit model
- Poisson regression
- Maximum likelihood
- Cochrane–Orcutt estimation
Computation
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
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
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
See also
- Prediction
- Design of experiments
- Data transformation
- Box–Cox transformation
- Machine learning
- Analysis of variance
- Causal inference
| Misplaced Pages outlines | |
|---|---|