Buch, Englisch, 324 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 616 g
Buch, Englisch, 324 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 616 g
ISBN: 978-0-367-73242-4
Verlag: Chapman and Hall/CRC
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.
Autoren/Hrsg.
Weitere Infos & Material
Multiple Linear Regression Models. Regression Models for Binary Response. Regression Models for Categorical Response. Generalized Linear Models for Count Response. Poisson Regression Model. Generalized Linear Models for Over-dispersed Count Response. Negative Binomial Regression Model. Regression Models for Continuous Proportion Response. Linear Mixed Models for Longitudinal Data. Generalized Linear Mixed Models for Longitudinal Data. Generalized Estimating Equations Regression Model. Hierarchical Regression Models. Structural Equation Modelling.
