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Linear Models with R
Books on regression and the analysis of variance abound-many are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R.
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http: //www.stat.lsa.umich.edu/ faraway/LMR/.
The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it.
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http: //www.stat.lsa.umich.edu/ faraway/LMR/.
The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it.
240 pages, Hardcover
First published July 26, 2004
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Displaying 1 - 7 of 7 reviews
June 15, 2025
Solid book for a total beginner who is completely new to R.
June 3, 2019
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.
December 24, 2013
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.
October 14, 2021
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.
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.
May 15, 2019
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.
September 12, 2020
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.
March 26, 2017
Excellent and straight forward. Very helpful and insightful. Helped me connect many concepts.
Displaying 1 - 7 of 7 reviews