Table of Contents

1.Introduction 2.Estimation 3.Inference 4.Prediction 5.Explanation 6.Diagnostics 7.Problems with the Predictors 8.Problems with the Errors 9.Transformation10.Model Selection 11.Shrinkage Methods 12.Insurance Redlining —A Complete Example 13.Missing Data 14.Categorical Predictors 15.One Factor Models 16.Models with Several Factors 17.Experiments with Blocks 18.About Python

About the Author

Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.

Reviews

'Multiple Python program scripts and screenshots of the outcomes fill the book, and each chapter suggests numerous exercises for training in coding. The book presents an amazingly valuable source of knowledge on statistical modeling and Python tools for students and practitioners.'- Stan Lipovetsky, Technometrics, Vol 63, Issue 3 2021'Therefore, this book is very valuable for understanding paired and multivariate linear regressions.[...] The book is clearly structured, containing all the necessary theoretical calculations and theoretical results on which the calculations are based'- Igor Malyk, International Society for Clinical Biostatistics, 72, 2021