Advanced Regression Models with SAS and R
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Book Description
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 rightskewed, categorical and hierarchical observations. The book presents the theory as well as fully workedout 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, rightskewed, longitudinal and hierarchical.
 Uses examples based on reallife 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.
Table of Contents
1 Introduction: General and Generalized Linear Regression Models
Definition of General Linear Regression Model
Definition of Generalized Linear Regression Model
Parameter Estimation and Significance Test for Coefficients
Fitted Model
Interpretation of Estimated Regression Coefficients
Model GoodnessofFit Check
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
2 Regression Models for Response with Rightskewed Distribution
BoxCox Power Transformation
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Gamma Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
3 Regression Models for Binary Response
Binary Logistic Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Prohibit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Complementary LogLog Model
Model Definition and Development
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Exercises
4 Regression Models for Categorical Response
Cumulative Logit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Cumulative Prohibit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients 2
Predicted Probabilities
SAS Implementation
R Implementation
Example
Cumulative Complementary LogLog Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Generalized Logit Model for Nominal Response
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Exercises
5 Regression Models for Count Response
Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zerotruncated Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
Implementation
R Implementation
Example
Zeroinflated Poisson Regression Model
Model Definition
Fitted Model 3
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Hurdle Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
6 Regression Models for OverDispersed Count Response
Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zerotruncated Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zeroinflated Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Hurdle Negative Binomial Regression Model
Model Definition 4
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
7 Regression Models for Proportion Response
Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zeroinflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Oneinflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zerooneinflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
8 General Linear Regression Models for Repeated Measures Data
Random Slope and Intercept Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model GoodnessofFit Check
Predicted Response
SAS Implementation
R Implementation
Example
Mixed Model with Covariance Structure for Error
Model Definition
Coefficients, and Predicted Response
Model Goodnessof_t Check
SAS Implementation
R Implementation
Example
Generalized Estimating Equations Model
Model Definition
Fitted Model
Model GoodnessofFit Check
SAS Implementation
R Implementation
Example
Exercises
9 Generalized Linear Regression Model for Repeated Measures Data
Generalized Linear Mixed Model
Models Definition
Fitted Model, Interpretation, Prediction
Model GoodnessofFit Check
SAS Implementation
R Implementation
Example
Generalized Estimating Equations Model
Model Definition
SAS Implementation
R Implementation
Example
Exercises
10 Hierarchical Regression Model
Hierarchical Regression Model for Normal Response
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model GoodnessofFit Check
Predicted Response
SAS Implementation
R Implementation
Example
Hierarchical Regression Model for Other Distributions
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model GoodnessofFit Check
Predicted Response
SAS Implementation
R Implementation
Examples
Exercises
Author(s)
Biography
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.
Reviews
"This book can be summarized as a cookbook of various types of regression model and their implementations using SAS and R statistical software. The chapters follow a specific pattern of presentation, starting with a brief introduction to the theory behind a technique, followed by the SAS and R implementations... At the end of each chapter there are exercise problems that interested readers and students would find useful. The book also has a companion website with all the data in CSV format, while a solutions manual to the exercise problems is available for instructors... This book is unique in the sense that it provides recipes for almost all types of regression model. The author intelligently avoids much about the theory but does not ignore it altogether. The theory is presented at a minimum level and in an amount that is necessary for those interested... It should be especially useful for R users who will find all of the various packages that are needed for these regression models."
 Enayet Raheem, ISCB December 2019
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