Linear Models with R

Linear Models with R

4.02 (37 ratings by Goodreads)
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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, 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. It also discusses topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates numerous 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 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.
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Product details

  • Hardback | 240 pages
  • 157.48 x 238.76 x 17.78mm | 476.27g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Special ed.
  • 69 black & white illustrations, 8 black & white tables
  • 1584884258
  • 9781584884255
  • 552,654

Table of contents

INTRODUCTION Before b Initial Data Analysis When to Use Regression Analysis History ESTIMATION Linear Model Matrix Representation Estimating b Least b Examples of Calculating Gauss-Markov Theorem Goodness of Fit Example Identifiability INFERENCE Hypothesis Tests to compare models Testing Examples Permutation tests Confidence Intervals for b Confidence Intervals for Predictions Designed Experiments Observational Data Practical Difficulties DIAGNOSTICS Checking Error Assumptions Finding Unusual Observations Checking the Structure of the Model PROBLEMS WITH THE PREDICTORS Errors in Predictors Changes of Scale Collinearity PROBLEMS WITH THE ERROR Generalized Least Squares Weighted Least Squares Testing for Lack of Fit Robust Regression TRANSFORMATION Transforming the Response Transforming the Predictors VARIABLE SELECTION Hierarchical Models Testing-based Procedures Criterion-based procedures Summary SHRINKAGE METHODS Principal Components Partial Least Squares Ridge Regression STATISTICAL STRATEGY AND MODEL UNCERTAINTY Strategy An Experiment in Model Building Discussion CHICAGO INSURANCE REDLINING - A COMPLETE EXAMPLE Ecological Correlation Initial Data Analysis Initial model and Diagnostics Transformation and Variable Selection Discussion MISSING DATA ANALYSIS OF COVARIANCE A Two-Level Example Coding Qualitative Predictors A Multi-Level Factor Example ONE-WAY ANOVA The Model An Example Diagnostics Pairwise Comparisons FACTORIAL DESIGNS Two-Way Anova Two-Way Anova with One Observation per Cell Two-Way Anova with more than One Observation per Cell Larger Factorial Experiments BLOCK DESIGNS Randomized Block design Latin Squares Balanced Incomplete Block design APPENDICES R installation, Functions and Data Quick Introduction to R BIBLIOGRAPHY INDEX
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Review quote

"One danger with applied books such as this is that they become recipe lists of the kind 'press this key to get that result.' This is not so with Faraway's book. Throughout, it gives plenty of insight on what is going on, with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosenI read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear modelsI find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught." -Journal of the Royal Statistical Society "Overall, Linear Models with R is well written and, given the increasing popularity of R, it is an important contribution." -Technometrics, Vol. 47, No. 3, August 2005 "There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysisThe text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations." -Short Book Reviews of the International Statistical Institute
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Rating details

37 ratings
4.02 out of 5 stars
5 38% (14)
4 35% (13)
3 19% (7)
2 8% (3)
1 0% (0)
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