Linear Regression Models: Applications in R

by John P. Hoffmann

Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model―logistic regression―designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features The book is aimed primarily at advanced undergraduate and graduate students in social, behavioral, health sciences, and related disciplines, taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior.

420 pages, Paperback

Published September 13, 2021

Reviews

[David · Rating 3 out of 5]

As a doctoral graduate of clinical medicine, I learned some basics in calculus, linear algebra and theory of probability when I was a freshman. Such basic math knowledge cannot support a thorough understanding in biostatistics later learned in medical school. That is why I chose to read this book with less mathematics.

The first two chapter seemed OK until I came to the third chapter of simple linear regression.

This is the first book I have read to add an error term in the formula for hatted value for outcome variable, y. At first, I guessed maybe the author just wanted to stress the difference between confidence interval and prediction interval for the linear regression model. However, later I found the error term, epsilon, referred to by the author, was the difference between true value and predicted value, which had nothing to with the sampling error.

Although some explanations and interpretations in the first two chapters are really thoughtful and inspiring, it really stopped me from getting any further when I come to such mistakes concerning basic statistical concepts. Even I, an nonprofessional reader, can find such fundamental error based on my trivial biostatistics knowledge. I could not expect not encountering any misleading statement had I continued.

Besides, the book is really long-winded and should be abridged to save readers’ time. Two many examples do not add to better understanding. I even doubt whether it could save me time if I opt to watch Gilbert Strand’s linear algebra courses before I start another book with more mathematics.