The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describes exactly how regression models are developed and evaluated -where real data is used, instead of contrived textbook-like problems. Completing this book will allow you to understand and build basic business/economic models using regression analysis. You will be able to interpret the output of those models and you will be able to evaluate the models for accuracy and shortcomings. Even if you never build a model yourself, at some point in your career it is likely that you will find it necessary to interpret one; this book will make that possible. Included are instructions for using Microsoft Excel to build business/economic models using regression analysis with an appendix using screen shots and step-by-step instructions.

Dr. J. Holton Wilson is professor emeritus in Marketing at Central Michigan University. He has a BA in both economics and chemistry from Otterbein College, an MBA from Bowling Green State University (statistics), and a PhD from Kent State University (majors in both marketing and economics).

Chapter 1 Background Issues for Regression Analysis .......................1 Chapter 2 Introduction to Regression Analysis ...............................11 Chapter 3 Th e Ordinary Least Squares (OLS) Regression Model ..........................................................23 Chapter 4 Evaluation of Ordinary Least Squares (OLS) Regression Models .........................................................39 Chapter 5 Point and Interval Estimates From a Regression Model ........................................................65 Chapter 6 Multiple Linear Regression ............................................75 Chapter 7 A Market Share Multiple Regression Model ...................95 Chapter 8 Qualitative Events and Seasonality in Multiple Regression Models .....................................105 Chapter 9 Nonlinear Regression Models ......................................125 Chapter 10 Abercrombie & Fitch Co.-Regression Case Study .....139 Chapter 11 Th e Formal Ordinary Least Squares (OLS) Regression Model ........................................................157 Appendix Some Statistical Background ........................................169 Index .................................................................................................175