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Regression Basics Book

Regression Basics
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  • Regression Basics
  • Written by author Leo H. Kahane
  • Published by SAGE Publications, March 2001
  • 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 provi
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Authors

Acknowledgments
Preface
Ch. 1An Introduction to the Linear Regression Model1
Baseball Salaries1
Linear Regression Model Assumption3
Population Data Versus Sample Data8
Presidential Elections10
Abortion Rates12
Types of Data Sets14
Ch. 2The Least-Squares Estimation Method: Fitting Lines to Data19
Ordinary Least-Squares19
Regression Model Assumptions and the Properties of Ordinary Least-Squares30
Ch. 3Model Performance and Evaluation37
Goodness of Fit: The R[superscript 2]37
Sample Results and Population Parameters43
Ch. 4Multiple Regression Analysis59
Baseball Salaries Revisited59
Presidential Elections Revisited66
Abortion Rates Revisited68
Further Considerations for the Multiple Regression Model72
Ch. 5Nonlinear, Dummy, Interaction, and Time Variables79
Nonlinear Independent Variables79
Dummy Independent Variables83
Interaction Variables92
Time as an Independent Variable101
Ch. 6Some Common Problems in Regression Analysis113
The Problem of High Multicollinearity113
Nonconstant Error Variance119
Autocorrelated Errors127
Ch. 7Where to Go From HERE135
Key Points to Bear in Mind135
Other Topics in Regression Analysis136
Go Forward and Regress!138
App. AData Sets Used in Examples139
App. BInstructions for Using Excel and SPSS151
App. Ct Table163
App. D: Answers to Problems165
Glossary187
References197
Index199
About the Author202


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