Primer of Applied Regression & Analysis of Variance

Hardcover | November 15, 2000

byStanton A. Glantz, Bryan K. Slinker

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Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).

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From the Publisher

Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (...

From the Jacket

Demystifies the Use of Advanced Statistcal MethodsUnlike other texts, Primer of Applied Regression & Analysis of Variance teaches both how to understand more advanced multivariate statistical methods, as well as how to use statistical software to get the correct results. This new edition offers the modern, intuitive approaches that won...

About the Author: Professor Glantz conducts research on tobacco control and cardiology. He is author or co-author of over 100 scientific papers and six books, including Primer of Biostatistics, 5th ed. (McGraw-Hill, 2002) and Primer of Applied Regression & Analysis of Variance, 2nd ed. (McGraw-Hill, 2001). He wrote the first major revi...

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Format:HardcoverDimensions:949 pages, 9.7 × 7.9 × 1.7 inPublished:November 15, 2000Publisher:McGraw-Hill EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0071360867

ISBN - 13:9780071360869

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Extra Content

Table of Contents

Why Do Multivariate Analysis?.

The First Step: Understanding Simple Linear Regression.

Regression With Two or More Independent Variables.

Do the Data Fit the Assumptions?.

Multicollinearity and What to Do About It.

Selecting the "Best" Regression Model.

One-Way Analysis of Variance.

Two-Way Analysis of Variance.

Repeated Measures.

Mixing Continuous and Categorical Variables: Analysis of Covariance.

Nonlinear Regression.

Regression With a Qualitative Dependent Variable.

APPENDIX A. A Brief Introduction to Matrices and Vectors.

APPENDIX B. Statistical Package Cookbook.

APPENDIX C. Data for Examples.

APPENDIX D. Data for Problems.

APPENDIX E. Statistical Tables.

APPENDIX F. Solutions to Problems.

Editorial Reviews

This is a solid reference work. Biostatisticians and epidemiologists will find it useful. 3 Stars."--Doody's Review Service