J. Holton Wilson - Regression Analysis: Understanding and Building Business and Economic Models Using Excel, Second Edition

Regression Analysis

Regression Analysis

Understanding and Building Business and Economic Models Using Excel

Second Edition

J. Holton Wilson, Barry P. Keating, and Mary Beal

Regression Analysis: Understanding and Building Business and Economic Models Using Excel, Second Edition

Copyright Business Expert Press, LLC, 2016.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any meanselectronic, mechanical, photocopy, recording, or any other except for brief quotations, not to exceed 400 words, without the prior permission of the publisher.

First published in 2012 by
Business Expert Press, LLC
222 East 46th Street, New York, NY 10017
www.businessexpertpress.com

ISBN-13: 978-1-63157-385-9 (paperback)

ISBN-13: 978-1-63157-386-6 (e-book)

Business Expert Press Quantitative Approaches to Decision Making Collection

Collection ISSN: 2163-9515 (print)

Collection ISSN: 2163-9582 (electronic)

Cover and interior design by Exeter Premedia Services Private Ltd.,
Chennai, India

First edition: 2012

Second edition: 2016

10 9 8 7 6 5 4 3 2 1

Printed in the United States of America.

This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. 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. It is especially useful for those engaged in working with numberspreparing forecasts, budgeting, estimating the effects of business decisions, and any of the forms of analytics that have recently become so useful.

This book is a nontheoretical treatment that is accessible to readers with even a limited statistical background. This book specifically does not cover the theory of regression; it is designed to teach the correct use of regression, while advising the reader of its limitations and teaching about common pitfalls. It is useful for business professionals, MBA students, and others with a desire to understand regression analysis without having to work through tedious mathematical/statistical theory.

This book describes exactly how regression models are developed and evaluated. Real data are used, instead of contrived textbook-like problems. The data used in the book are the kind of data managers are faced with in the real world. 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.

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.

Regression analysis, ordinary least squares (OLS), time-series data, cross-sectional data, dependent variables, independent variables, point estimates, interval estimates, hypothesis testing, statistical significance, confidence level, significance level, p-value, R-squared, coefficient of determination, multicollinearity, correlation, serial correlation, seasonality, qualitative events, dummy variables, nonlinear regression models, market share regression model, Abercrombie & Fitch Co.

OTHER TITLES IN QUANTITATIVE APPROACHES TODECISION MAKING COLLECTION

Donald N. Stengel, California State University, Fresno, Editor

• Service Mining: Framework and Application by Wei-Lun Chang
• Regression Analysis: Unified Concepts, Practical Applications, and Computer Implementation by Bruce L. Bowerman, Richard T. OConnell, and Emily S. Murphree
• Experimental Design: Unified Concepts, Practical Applications, and Computer Implementation by Bruce L. Bowerman, Richard T. OConnell, and Emily S. Murphree
• An Introduction to Survey Research by Ernest L. Cowles and Edward Nelson
• Business Applications of Multiple Regression, Second Edition by Ronny Richardson
• Business Decision-Making: Streamlining the Process for More Effective Results by Milan Frankl
• Operations Methods: Managing Waiting Line Applications, Second Edition by Kenneth A. Shaw

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Chapter 1 Preview

When you have completed reading this chapter you will:

• Realize that this is a practical guide to regression not a theoretical discussion.
• Know what is meant by cross-sectional data.
• Know what is meant by time-series data.
• Know to look for trend and seasonality in time-series data.
• Know about the three data sets that are used the most for examples in the book.
• Know how to differentiate between nominal, ordinal, interval, and ratio data.
• Know that you should use interval or ratio data when doing regression.
• Know how to access the Data Analysis functionality in Excel.

Introduction

The importance of the use of regression models in modern business and economic analysis can hardly be overstated. In this book, you will see exactly how such models can be developed. When you have completed the book you will understand how to construct, interpret, and evaluate regression models. You will be able to implement what you have learned by using Data Analysis in Excel to build basic mathematical models of business and economic relationships.

You will not know everything there is to know about regression; however, you will have a thorough understanding about what is possible and what to look for in evaluating regression models. You may not ever actually build such a model in your own work but it is very likely that you will, at some point in your career, be exposed to such models and be expected to understand models that someone else has developed.

Initial Data Issues

Before beginning to look at the process of building and evaluating regression models, first note that nearly all of the data used in the examples in this book are real data, not data that have been contrived to show some purely academic point. The data used are the kind of data one is faced with in the real world. Data that are used in business applications of regression analysis are either cross-sectional data or time-series data. We will use examples of both types throughout the text.