The primary goal of Introduction to Regression Modeling with R is to help students understand the types of questions regression models can and cannot answer and how to answer them. Students will become familiar with fitting both simple and complex models using R. The text stresses model interpretation rather than tedious algebra or mathematics since R calculates almost all relevant numbers. Equations are presented to help flesh out the ""why"" behind various aspects of modeling and to provide additional insights into how regression models work. Introduction to Regression Modeling with R includes numerous examples and sections not featured in earlier editions, and R code is visible throughout the book. A custom R package accompanies the text and was written to aid in regression model building and interpretation. This text is intended for those studying for professions that make use of large and potentially complex datasets arising observational studies, particularly in the fields of business and social science.

About the Author:

Adam G. Petrie holds a Ph.D. in decision sciences and engineering systems from Rensselaer Polytechnic Institute. Dr. Petrie is a lecturer in the business analytics and statistics department in the Haslam College of Business at the University of Tennessee. He teaches courses in regression, statistical theory and modeling, stochastic processes, and case studies, and regularly engages students by having them analyze datasets created from their personal information. Dr. Petrie's research interests include multivariate analysis and exploratory data analysis, statistical computing, and non-parametric models. His most recent paper discussed the use of the "snake": a new analytical and graphical method for detecting non-homogeneities and clusters in a dataset.

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