Regression analysis is a family of statistical methods used to model and quantify the relationship between one or more predictor (independent) variables and a continuous response (dependent) variable. It estimates parameters in a specified functional form, most commonly linear, to describe how changes in predictors are associated with changes in the response, while characterizing uncertainty via residual error structures and inferential statistics. Methods include ordinary least squares, generalized least squares, and generalized linear models, among others, and are used for prediction, effect size estimation, hypothesis testing, and assessing model fit and assumptions such as linearity, homoscedasticity, and independence of errors.
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