Applied Regression
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Оглавление
Colin Lewis-Beck. Applied Regression
Applied Regression
Quantitative Applications in the Social Sciences. A Sage Publications Series
Applied Regression
Contents
Series Editor’s Introduction
Preface
Acknowledgments
About the Authors
Chapter 1. Bivariate Regression: Fitting a Straight Line
Exact Versus Inexact Relationships
The Least Squares Principle
The Data
The Scatterplot
The Slope
The Intercept
Prediction
Assessing Explanatory Power: The R2
R2 Versus r
Notes
Chapter 2. Bivariate Regression: Assumptions and Inferences
The Regression Assumptions
Confidence Intervals and Significance Tests
The One-Sided Test
Significance Testing: A Rule of Thumb
Reasons Why a Parameter Estimate May Not Be Significant
The Prediction Error for y
Analysis of Residuals
The Effect of School Size on Educational Performance: A Bivariate Regression Example
Notes
Chapter 3. Multiple Regression: The Basics
The General Equation
Interpreting the Parameter Estimates
Confidence Intervals and Significance Tests
The R2
Predicting y
Dummy Variables
The Possibility of Interaction Effects
A Four-Variable Model: Overcoming Specification Error
Notes
Chapter 4. Multiple Regression: Special Topics
The Multicollinearity Problem
High Multicollinearity: An Example
The Relative Importance of the Independent Variables
Extending the Regression Model: Nonlinearity
Determinants of Presidential Popularity: A Multiple Regression Example
Presentation of Regression Results in a Research Paper
What Next?
Notes
Appendix
Index
Отрывок из книги
Second Edition
Second Edition
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On a personal note, I am particularly pleased to be able to assist in the publication of this monograph because I have known Michael Lewis-Beck since we were both graduate students at the University of Michigan many years ago, and I have subsequently had the pleasure of becoming acquainted with his son, Colin.
—John Fox
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