Handbook of regression methods

Description

Content: Introduction. Simple Linear Regression. The Basics of Regression Models. Statistical Inference. Statistical Intervals. Assessing Regression Assumptions. ANOVA I. Multiple Linear Regression. Multiple Regression. Matrix Notation in Regression. Indicator Variables. Multicollinearity. ANOVA II. Advanced Regression Diagnostic Methods. Influential Data Values. Measurement Errors and Instrumental Variables Regression. Weighted Least Squares and Robust Regression Procedures. Correlated Errors and Autoregressive Structures. Crossvalidation and Model Selection Methods. Advanced Regression Models. Biased Regression Methods and Regression Shrinkage. Piecewise and Nonparametric Methods. Regression Models with Censored Data. Nonlinear Regression. Regression Models with Counts as Responses. Multivariate Multiple Regression. Data Mining. Miscellaneous Topics. Appendices.