Outline of regression analysis
From Wikipedia, the free encyclopedia
The following outline is provided as an overview of and topical guide to regression analysis:
Regression analysis – in statistics, this includes any technique 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[edit]
[edit]
- Least squares
- Linear least squares (mathematics)
- Non-linear least squares
- Least absolute deviations
- Curve fitting
- Smoothing
- Cross-sectional study
[edit]
- Conditional expectation
- Correlation
- Correlation coefficient
- Mean square error
- Residual sum of squares
- Explained sum of squares
- Total sum of squares
Visualization[edit]
Linear regression based on least squares[edit]
- 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[edit]
- Generalized linear models
- Logistic regression
- Ordered logit
- Probit model
- Ordered probit
- Poisson regression
- Maximum likelihood
- Cochrane–Orcutt estimation
Computation[edit]
Inference for regression models[edit]
- F-test
- t-test
- Lack-of-fit sum of squares
- Confidence band
- Coefficient of determination
- Multiple correlation
- Scheffé's method
Challenges to regression modeling[edit]
- Autocorrelation
- Cointegration
- Multicollinearity
- Homoscedasticity and heteroscedasticity
- Lack of fit
- Non-normality of errors
- Outliers
Diagnostics for regression models[edit]
- 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[edit]
- Model selection
- Mallows's Cp
- Akaike information criterion
- Bayesian information criterion
- Hannan–Quinn information criterion
- Cross validation
Robust regression[edit]
Terminology[edit]
- 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[edit]
Nonparametric regression[edit]
Semiparametric regression[edit]
Other forms of regression[edit]
- 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[edit]
| Definitions from Wiktionary | |
| Media from Commons | |
| News stories from Wikinews | |
| Quotations from Wikiquote | |
| Source texts from Wikisource | |
| Textbooks from Wikibooks | |
| Learning resources from Wikiversity | |
- Prediction
- Design of experiments
- Data transformation
- Box–Cox transformation
- Machine learning
- Analysis of variance
- Causal inference
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