Regression Analysis by Example

作者: Samprit Chatterjee , Ali S. Hadi

出版社: Wiley-Interscience

出版时间: 2006-07

版次: 1

ISBN: 9780471746966

装帧: 精装

开本: 16开

纸张: 胶版纸

页数: 375页

分类: 外文古旧书>英文书>工程技术

The essentials of regression analysis through practical applications

  Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

  This new edition features the following enhancements:

  Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis

  A new chapter entitled Further Topics discusses advanced areas of regression analysis

  Reorganized, expanded, and upgraded exercises appear at the end of each chapter

  A fully integrated Web page provides data sets

  Numerous graphical displays highlight the significance of visual appeal

  Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

  An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. SAMPRIT CHATTERJEE, PHD, is Professor of Health Policy at Mount Sinai School of Medicine. He is also Professor Emeritus of Statistics at New York University. A well-known research scientist and Fulbright scholar, Dr. Chatterjee has co-authored Sensitivity Preface

1　Introduction

  1.1  What Is Regression Analysis?

  1.2  Publicly Available Data Sets

  1.3  Selected Applications of Regression Analysis

  1.4  Step in Regression Analysis

  1.5  Scope and Organization of the Book

  Exercise

2　Simple Linear regression

  2.1  Introduction

  2.2  Covariance and Correlation Coefficient

  2.3  Example: Computer Repair Data

  2.4  The Simple Linear Regression Model

  2.5  Parameter Estimation

  2.6  Tests of hypotheses

  2.7  Confidence Intervals

  2.8  Predictions

  2.9  Measuring the Quality of Fit

  2.10  Regression Line Through the Origin

  2.11  Trivial Regression Models

  2.12  Bibliographic Notes

  Exercises

3　Multiple Linear Regression

4　Regression Diagnostics: Detection of Model Violations

5　Qualitative Variables as Predictors

6　Transformation of Variables

7　Weighted Least Squar45es

8　The Problem of Correlated Errors

9　Analysis of Collinear Data

10　Biased Estimation of Regression Coefficients

11　Variable Selection Procedures

12　Logistic Regression

13　Further Topics

Appendix A: Statistical Tables

References

Index
内容简介:
The essentials of regression analysis through practical applications

  Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

  This new edition features the following enhancements:

  Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis

  A new chapter entitled Further Topics discusses advanced areas of regression analysis

  Reorganized, expanded, and upgraded exercises appear at the end of each chapter

  A fully integrated Web page provides data sets

  Numerous graphical displays highlight the significance of visual appeal

  Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

  An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
作者简介:
SAMPRIT CHATTERJEE, PHD, is Professor of Health Policy at Mount Sinai School of Medicine. He is also Professor Emeritus of Statistics at New York University. A well-known research scientist and Fulbright scholar, Dr. Chatterjee has co-authored Sensitivity
目录:
Preface

1　Introduction

  1.1  What Is Regression Analysis?

  1.2  Publicly Available Data Sets

  1.3  Selected Applications of Regression Analysis

  1.4  Step in Regression Analysis

  1.5  Scope and Organization of the Book

  Exercise

2　Simple Linear regression

  2.1  Introduction

  2.2  Covariance and Correlation Coefficient

  2.3  Example: Computer Repair Data

  2.4  The Simple Linear Regression Model

  2.5  Parameter Estimation

  2.6  Tests of hypotheses

  2.7  Confidence Intervals

  2.8  Predictions

  2.9  Measuring the Quality of Fit

  2.10  Regression Line Through the Origin

  2.11  Trivial Regression Models

  2.12  Bibliographic Notes

  Exercises

3　Multiple Linear Regression

4　Regression Diagnostics: Detection of Model Violations

5　Qualitative Variables as Predictors

6　Transformation of Variables

7　Weighted Least Squar45es

8　The Problem of Correlated Errors

9　Analysis of Collinear Data

10　Biased Estimation of Regression Coefficients

11　Variable Selection Procedures

12　Logistic Regression

13　Further Topics

Appendix A: Statistical Tables

References

Index

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Regression Analysis by Example

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