Generalized Linear Models

Paperback | August 15, 2003

byJohn P. Hoffmann

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This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.

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This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous...

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Without requiring mathematical training beyond algebra and introductory statistics, Generalized Linear Models shows readers how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing and business. By including numerous exercises a...

Format:PaperbackDimensions:216 pages, 9 × 6.9 × 0.7 inPublished:August 15, 2003Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0205377939

ISBN - 13:9780205377930

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Table of Contents

Each chapter concludes with “Conclusion” and “Exercises.”

Introduction.


1. A Review of the Linear Regression Model.

Issues of Interest.

How to Estimate a Linear Regression Model.

A Detailed Example of an OLS Regression Model.

The Assumptions of the OLS (Linear) Regression Model.

Interaction Terms in the OLS (Linear) Regression Model.



2. Introduction to Generalized Linear Models.

The Role of the Link Function.

The Binomial Distribution.

The Multinomial Distribution.

The Poisson Distribution.

The Negative Binomial Distribution.

How Do We Estimate Regression Models Based on These Distributions?

How to Check the Significance of Coefficients and the “Fit” of the Model.



3. Logistic and Probit Regression Models.

What Are the Alternatives to the Linear Regression Model?

Diagnostic Tests for the Logistic Regression Model.



4. Ordered Logistic and Probit Regression Models.

Alternative Models for Ordinal Dependent Variables.

The Ordered Logistic Regression Model.

Testing the Proportional Odds Assumption.

The Ordered Probit Regression Model.

Introducing Multiple Independent Variables.



5. The Multinomial Logistic Regression Model.

Introducing Multiple Independent Variables.

Diagnostic Tests for the Multinomial Logistic Regression Model.

Alternatives to the Multinomial Logistic Regression Model.



6. Poisson and Negative Binomial Regression Models.

The Poisson Regression Model.

The Overdispersed Poisson Regression Model.

The Negative Binomial Regression Model.

Diagnostic Tests for the Poisson Regression Model.

Other Models for Count Variables.



7. Event History and Survival Models.

Continuous versus Discrete Time Models.

Censoring and Time-Dependent covariates.

The Basics: Survivor and Hazard Functions and Curves.

Parametric Event History Models.

The Cox Proportional Hazards Model.



8. Where Do We Go from Here?

Sample Selection.

Endogeneity.

Longitudinal Data.

Multilevel Models.

Nonparametric regression.



Appendix A. Stata, SPSS, and SAS Programs for Examples in Chapters.


References.