ABSTRACT
A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
Introduction. Estimation. Inference. Prediction. Explanation. Diagnostics. Problems with the Predictors. Problems with the Error. Transformation. Model Selection. Shrinkage Methods. Insurance Redlining-A Complete Example. Missing Data. Categorical Predictors. One Factor Models. Models with Several Factors. Experiments with Blocks. Appendix: About R. Bibliography. Index.
