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Synopsis
Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors.
Features:
- Presents the theoretical framework for each regression.
- Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical.
- Uses examples based on real-life consulting projects.
- Provides complete SAS and R codes for each example.
- Includes several exercises for every regression.
Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required.
The Author:
Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.
"synopsis" may belong to another edition of this title.
About the Author
Olga Korosteleva is an associate professor of statistics in the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received a Ph.D. in statistics from Purdue University.
"About this title" may belong to another edition of this title.
- PublisherRoutledge
- Publication date2020
- ISBN 10 0367732424
- ISBN 13 9780367732424
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages324
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