outline of regression analysis
{{Short description|1=Overview of and topical guide to regression analysis}}
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
Visualization
Linear regression based on least squares
Generalized linear models
Computation
Inference for regression models
Challenges to regression modeling
Diagnostics for regression models
Formal aids to model selection
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
{{sisterlinks|Regression analysis}}
- Prediction
- Design of experiments
- Data transformation
- Box–Cox transformation
- Machine learning
- Analysis of variance
- Causal inference
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