Advanced Regression Models with SAS and R

ISBN-13: 9780367732424 / Angielski / Miękka / 2020 / 324 str.

Olga Korosteleva

Opis

The material covered by this book consists of a collection of regression models beyond linear regression. The linear regression model is presented in the first chapter in order to refresh the reader’s memory, emphasize certain points, and set the notation.The subsequent chapters talk about models for categorical data, count data, proportions, lo

Recenzje

"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

Spis treści

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.

O autorze

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.