Outline of regression analysis

Short description: 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).

## Contents

* 1 Overview articles
* 2 Non-statistical articles related to regression
* 3 Basic statistical ideas related to regression
* 4 Visualization
* 5 Linear regression based on least squares
* 6 Generalized linear models
* 7 Computation
* 8 Inference for regression models
* 9 Challenges to regression modeling
* 10 Diagnostics for regression models
* 11 Formal aids to model selection
* 12 Robust regression
* 13 Terminology
* 14 Methods for dependent data
* 15 Nonparametric regression
* 16 Semiparametric regression
* 17 Other forms of regression
* 18 See also

## 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

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