Regression Analysis: Understanding and Building Business and Economic Models Using Excel

Wilson, J. Holton

ISBN 10: 1606494341 / ISBN 13: 9781606494349

Published by Business Expert Press, 2012

About this Item

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Seller Inventory # 00089357157

Bibliographic Details

• Title: Regression Analysis: Understanding and Building Business and Economic Models Using Excel
• Publisher: Business Expert Press
• Publication Date: 2012
• Language: English
• ISBN 10: 1606494341
• ISBN 13: 9781606494349
• Binding: Soft cover
• Condition: Very Good

About this title

Synopsis

This book covers essential elements of building and understanding regression models within the context of business and economics. It is a nonmathematical treatment that is accessible, even to readers with limited statistical backgrounds. It is useful for business professionals, MBA students and others who seek to understand regression analysis without having to work through tedious mathematical and statistical theory. The importance of using regression models in modern business and economic analysis can hardly be overstated. In this book we describe exactly how such models can be developed and evaluated. The data used is real data with real world business applications, not data that has been contrived to demonstrate some purely academic point. These data are likely to be encountered and used in the actual world of business. In an appendix using screen shots and step by step instructions, we include how to do use Excel to perform regression analysis. When readers have completed this book they will understand how to build basic mathematical models illustrating business/economic relationships using regression analysis. In addition, they will know how to interpret and evaluate regression models using a five step process (which includes evaluating the model; identifying its statistical significance; determining its explanatory power; for time-series applications, identifying how the error terms are distributed; and understanding the concept of multicollinearity). Readers will understand what is possible and what to look for in evaluating regression models. It is unlikely that most readers will build such models in the course of carrying out their own professional responsibilities, but it is very likely that they will, at some point in their careers, be exposed to such models. This book will help such readers understand models that someone else has developed.

About the Author

Mt. Pleasant, MI; Professor, Department of Marketing; Central Michigan University