Applied Regression Modeling: A Business Approach
by Iain Pardoe
Read the preface
This book has developed from class notes written for the "Business Statistics" course taken primarily by undergraduate business majors in their junior year at the University of Oregon. This course is essentially an applied regression course, and incoming students have already taken an introductory probability and statistics course.
The book is suitable for any undergraduate second statistics course in which regression analysis is the main focus. It would also be suitable for use in an applied regression course for nonstatistics major graduate students, including MBAs. Mathematical details have deliberately been kept to a minimum, and the book does not contain any calculus. Instead, emphasis is placed on applying regression analysis to data using statistical software, and understanding and interpreting results.
Chapter 1 reviews essential introductory statistics material, while Chapter 2 covers simple linear regression. Chapter 3 introduces multiple linear regression, while Chapters 4 and 5 provide guidance on building regression models, including transforming variables, using interactions, incorporating qualitative information, and using regression diagnostics. Each of these chapters includes homework problems, mostly based on analyzing real datasets provided with the book. Chapter 6 contains two in-depth case studies, while Chapter 7 introduces extensions to linear regression and outlines some related topics. The appendices contain instructions on using statistical software (SPSS, Minitab, SAS, and R/S-PLUS) to carry out all the analyses covered in the book, a table of critical values for the t-distribution, notation and formulas used throughout the book, a glossary of important terms, a short mathematics refresher, and brief answers to selected homework problems.
The first five chapters of the book have been successfully used in quarter-length courses over the last several years. An alternative approach for a quarter-length course would be to skip some of the material in Chapters 4 and 5 and substitute one or both of the case studies in Chapter 6, or briefly introduce some of the topics in Chapter 7. A semester-length course could comfortably cover all the material in the book.
The website for the book contains supplementary material designed to help both the instructor teaching from this book and the student learning from it. There you'll find all the datasets used for examples and homework problems in formats suitable for the statistical software packages SPSS, Minitab, SAS, and R, as well as the Microsoft Excel spreadsheet package. (There is information on using Excel for some of the analyses covered in the book in the appendices, but statistical software is necessary to carry out all the analyses.) The website also includes information on obtaining a solutions manual containing complete answers to all the homework problems, as well as further ideas for organizing class-time around the material in the book.