Linear Models with R

by Julian James Faraway

A Hands-On Way to Learning Data Analysis

Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.

New to the Second Edition

Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality
Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates
Extensive use of the ggplot2 graphics package in addition to base graphics

Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.

Genres: Nonfiction, Mathematics, Computer Science, Textbooks, Science

286 pages, Kindle Edition

First published July 26, 2004

Reviews

[Eric · Rating 5 out of 5]

Solid book for a total beginner who is completely new to R.

[Betsy Rosalen · Rating 4 out of 5]

Most topics were well explained but I have the same complaint that I have with all R textbooks, needs better explanation of how to interpret R outputs in some cases. Saying, "You can see that..." does not explain HOW you can see that. Needs to be more specific. Overall very good textbook though. Relatively easy to understand given the complexity of the topic. Some topics could use more explanation like Box-Cox on predictor variables and how to choose the weight for a weighted regression. Maybe it's just me though... I definitely struggled a bit with the math.

[Rafer Dannenhauer · Rating 5 out of 5]

If you are the kind of person who likes R because it is a pain in the butt, or who likes statistics because it makes no goddamn sense, you won't like it. This book is basically idiot-proof. Trust me, I'm an idiot.

[Ostilio Portillo · Rating 5 out of 5]

Good reading for anyone interested in learning more about statistical designs. The author covers a series of different statistical models although more emphasis is placed on the discussion about several approaches associated with linear regression.
The material is presented in an in-depth manner, so it is not a book intended for someone who is a beginner in the field of statistics and more precisely in the analysis of databases. Some knowledge and understanding of the R language are required from the reader to be able to understand the multiple codes presented in the text which explain how to perform specific analysis.
Finally, the comprehension of the statistical designs is facilitated since the author only discusses examples which databases are already integrated into R; So the reader does not have to search for external sources in order to locate the required databases.

[Duncan McKinnon · Rating 4 out of 5]

Great accompanying package. R code examples were a little outdated. Needed other references to make sense of some material, but overall a thorough statistical approach.

[Goo · Rating 2 out of 5]

A very non-mathematical introduction to linear models. Useful for a birds eye view of linear models and for working with them in R. Not useful for gaining a deeper understanding and intuition of linear models. I would only recommend this book in conjunction with a more rigorous treatment such as Seber & Lee.

[R · Rating 5 out of 5]

Excellent and straight forward. Very helpful and insightful. Helped me connect many concepts.