Enhanced descriptions from Syndetics:

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 Rfocuses 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 http://people.bath.ac.uk/jjf23/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.

Table of contents provided by Syndetics

• Preface (p. ix)
• 1 Introduction (p. 1)
• 1.1 Before You Start (p. 1)
• 1.2 Initial Data Analysis (p. 2)
• 1.3 When to Use Regression Analysis (p. 6)
• 1.4 History (p. 7)
• 2 Estimation (p. 11)
• 2.1 Linear Model (p. 11)
• 2.2 Matrix Representation (p. 12)
• 2.3 Estimating [beta] (p. 12)
• 2.4 Least Squares Estimation (p. 13)
• 2.5 Examples of Calculating [beta] (p. 14)
• 2.6 Gauss-Markov Theorem (p. 15)
• 2.7 Goodness of Fit (p. 16)
• 2.8 Example (p. 18)
• 2.9 Identifiability (p. 21)
• 3 Inference (p. 25)
• 3.1 Hypothesis Tests to Compare Models (p. 25)
• 3.2 Testing Examples (p. 27)
• 3.3 Permutation Tests (p. 32)
• 3.4 Confidence Intervals for [beta] (p. 34)
• 3.5 Confidence Intervals for Predictions (p. 36)
• 3.6 Designed Experiments (p. 39)
• 3.7 Observational Data (p. 43)
• 3.8 Practical Difficulties (p. 47)
• 4 Diagnostics (p. 53)
• 4.1 Checking Error Assumptions (p. 53)
• 4.2 Finding Unusual Observations (p. 64)
• 4.3 Checking the Structure of the Model (p. 72)
• 5 Problems with the Predictors (p. 77)
• 5.1 Errors in the Predictors (p. 77)
• 5.2 Changes of Scale (p. 81)
• 5.3 Collinearity (p. 83)
• 6 Problems with the Error (p. 89)
• 6.1 Generalized Least Squares (p. 89)
• 6.2 Weighted Least Squares (p. 92)
• 6.3 Testing for Lack of Fit (p. 94)
• 6.4 Robust Regression (p. 98)
• 7 Transformation (p. 109)
• 7.1 Transforming the Response (p. 109)
• 7.2 Transforming the Predictors (p. 112)
• 8 Variable Selection (p. 121)
• 8.1 Hierarchical Models (p. 121)
• 8.2 Testing-Based Procedures (p. 122)
• 8.3 Criterion-Based Procedures (p. 125)
• 8.4 Summary (p. 130)
• 9 Shrinkage Methods (p. 133)
• 9.1 Principal Components (p. 133)
• 9.2 Partial Least Squares (p. 140)
• 9.3 Ridge Regression (p. 143)
• 10 Statistical Strategy and Model Uncertainty (p. 147)
• 10.1 Strategy (p. 147)
• 10.2 An Experiment in Model Building (p. 148)
• 10.3 Discussion (p. 149)
• 11 Insurance Redlining--A Complete Example (p. 151)
• 11.1 Ecological Correlation (p. 151)
• 11.2 Initial Data Analysis (p. 153)
• 11.3 Initial Model and Diagnostics (p. 156)
• 11.4 Transformation and Variable Selection (p. 158)
• 11.5 Discussion (p. 161)
• 12 Missing Data (p. 163)
• 13 Analysis of Covariance (p. 167)
• 13.1 A Two-Level Example (p. 168)
• 13.2 Coding Qualitative Predictors (p. 172)
• 13.3 A Multilevel Factor Example (p. 174)
• 14 One-Way Analysis of Variance (p. 181)
• 14.1 The Model (p. 181)
• 14.2 An Example (p. 182)
• 14.3 Diagnostics (p. 185)
• 14.4 Pairwise Comparisons (p. 186)
• 15 Factorial Designs (p. 189)
• 15.1 Two-Way ANOVA (p. 189)
• 15.2 Two-Way ANOVA with One Observation per Cell (p. 190)
• 15.3 Two-Way ANOVA with More than One Observation per Cell (p. 193)
• 15.4 Larger Factorial Experiments (p. 197)
• 16 Block Designs (p. 203)
• 16.1 Randomized Block Design (p. 204)
• 16.2 Latin Squares (p. 208)
• 16.3 Balanced Incomplete Block Design (p. 212)
• A R Installation, Functions and Data (p. 217)
• B Quick Introduction to R (p. 219)
• B.1 Reading the Data In (p. 219)
• B.2 Numerical Summaries (p. 219)
• B.3 Graphical Summaries (p. 220)
• B.4 Selecting Subsets of the Data (p. 221)
• B.5 Learning More about R (p. 222)
• Bibliography (p. 223)
• Index (p. 227)