Regression Basics

by Kahane, Leo

• ISBN13: 9780761924135
• ISBN10: 0761924132
• Format: Paperback
• Copyright: 2001-03-01
• Publisher: Sage Publications, Inc

Summary

"Kahane is to be congratulated for compressing such comprehensive coverage into so few pages." --THE STATISTICIAN "This book makes a breakthrough in terms of teaching regression analysis through real world examples that progress in complexity." --William C. Birdsall, School of Social Work, University of Michigan ". . . does a great job explaining technical concepts in a straightforward and non-technical manner. . . can give students from any discipline a head start to understanding the technical details of regression procedures . . . " --Jerry Welkenhuysen-Gybels in STATISTICAL METHODS IN MEDICAL RESEARCH Although many people have PCs with software capable of performing regression techniques, only a few know how to capitalize on the flexibility and wide application of regression analysis. This book shows readers how to get the most from regression by providing a friendly, non-technical introduction to the subject. Accessible to anyone with only an introductory statistics background, the book begins with the simplest, two-variable linear model and gradually builds towards models of more complexity, such as multivariate regression. Kahane uses three engaging examples to illustrate regression concepts. These examples show the creative way in which regression analysis can be used to determine why some professional sports players earn higher salaries than others, the factors that affect voting patterns in presidential elections, and how to determine the factors that explain the difference in abortion rates across the U.S. Plus, the data for these examples are provided in an Appendix so that readers can have tangible, hands-on experience in performing linear regression analysis. Additional outstanding features: End of chapter problems based on two additional completely worked-out examples: the effects of education and other factors on a person's salary and the factors that determine automobile prices A? Data for end of chapter problems provided at the end of the book A? Complete solutions to all problems in Appendix D so readers can check their own work A? Glossary of terms so the reader can become more confident about using the language of regression Written in a fun and engaging style, this book will equip readers with the knowledge and ability to perform and interpret the results of basic linear regression analysis.

Table of Contents

Acknowledgments	vii
Preface	ix
An Introduction to the Linear Regression Model	1	(18)
Baseball Salaries	1	(2)
Linear Regression Model Assumption	3	(5)
Population Data Versus Sample Data	8	(2)
Presidential Elections	10	(2)
Abortion Rates	12	(2)
Types of Data Sets	14	(1)
Notes	15	(2)
Problems	17	(2)
The Least-Squares Estimation Method: Fitting Lines to Data	19	(18)
Ordinary Least-Squares	19	(11)
Regression Model Assumptions and the Properties of Ordinary Least-Squares	30	(4)
Summing Up	34	(1)
Notes	34	(2)
Problems	36	(1)
Model Performance and Evaluation	37	(22)
Goodness of Fit: The R2	37	(6)
Sample Results and Population Parameters	43	(11)
Summing Up	54	(1)
Notes	55	(3)
Problems	58	(1)
Multiple Regression Analysis	59	(20)
Baseball Salaries Revisited	59	(7)
Presidential Elections Revisited	66	(2)
Abortion Rates Revisited	68	(4)
Further Considerations for the Multiple Regression Model	72	(1)
Summing Up	73	(1)
Notes	74	(2)
Problems	76	(3)
Nonlinear, Dummy, Interaction, and Time Variables	79	(34)
Nonlinear Independent Variables	79	(4)
Dummy Independent Variables	83	(9)
Interaction Variables	92	(9)
Time as an Independent Variable	101	(6)
Summing Up	107	(1)
Notes	108	(2)
Problems	110	(3)
Some Common Problems in Regression Analysis	113	(22)
The Problem of High Multicollinearity	113	(6)
Nonconstant Error Variance	119	(8)
Autocorrelated Errors	127	(5)
Notes	132	(1)
Problems	133	(2)
Where to Go From Here	135	(4)
Key Points to Bear in Mind	135	(1)
Other Topics in Regression Analysis	136	(2)
Go Forward and Regress!	138	(1)
Notes	138	(1)
Appendix A: Data Sets Used in Examples	139	(12)
Appendix B: Instructions for Using Excel and SPSS	151	(12)
Using Excel	151	(7)
Using SPSS	158	(3)
Notes	161	(2)
Appendix C: t Table	163	(2)
Appendix D: Answers to Problems	165	(22)
Glossary	187	(10)
References	197	(2)
Index	199	(3)
About the Author	202