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Applied Linear Models with SAS
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Table of Contents

1. Introduction; 2. Principles of statistics; 3. Introduction to linear regression; 4. Assessing the regression; 5. Multiple linear regression; 6. Indicators, interactions, and transformations; 7. Nonparametric statistics; 8. Logistic regression; 9. Diagnostics for logistic regression; 10. Poisson regression; 11. Survival analysis; 12. Proportional hazards regression; 13. Review of methods; Appendix: statistical tables.

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This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models using SAS.

About the Author

Dr Daniel Zelterman is Professor of Epidemiology and Public Health in the Division of Biostatistics at Yale University. His application areas include work in genetics, HIV, and cancer. Before moving to Yale in 1995, he was on the faculty of the University of Minnesota and at the State University of New York at Albany. He is an elected Fellow of the American Statistical Association. He serves as associate editor of Biometrics and other statistical journals. He is the author of Models for Discrete Data (1999), Advanced Log-Linear Models Using SAS (2002), Discrete Distributions: Application in the Health Sciences (2004), and Models for Discrete Data, 2nd edition (2006).

Reviews

"The author is an established and immensely experienced biostatistical and statistical researcher, and an excellent educator and speaker. His skill in teaching and presentation is very obvious in the engaging, sometime humorous, and easily comprehensible presentation style of the book. I will definitely keep a copy of this book on hand for my nonstatistical collaborators and even undergraduate students interested in a lucid yet thorough introduction to day-to-day biostatistical methods and applied data analysis."
Debajyoti Sinha, The American Statistician

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