Applied Regression

Applied Regression
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Описание книги

Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. Authors Colin Lewis-Beck and Michael Lewis-Beck then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.

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

.....

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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