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Regression analysis
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Models
Linear regression Simple regression Polynomial regression General linear model Proportional hazards model
Generalized linear model Vector generalized linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial logistic regression Mixed logit Probit Multinomial probit Ordered logit Ordered probit Poisson
Multilevel model Fixed effects Random effects Linear mixed-effects model Nonlinear mixed-effects model
Nonlinear regression Support vector regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented
Errors-in-variables
Estimation
Least squares Linear Non-linear
Ordinary Weighted Generalized Generalized estimating equation
Partial Total Non-negative Ridge regression Regularized
Least absolute deviations Iteratively reweighted Bayesian Bayesian multivariate Least-squares spectral analysis Heteroscedasticity Consistent Regression Standard Errors Heteroscedasticity and Autocorrelation Consistent Regression Standard Errors Instrumental variables estimation
Background
Regression validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem
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