An R Companion to Applied RegressionSAGE, 2010 M11 29 - 472 pages This is a broad introduction to the R statistical computing environment in the context of applied regression analysis. It is a thoroughly updated edition of John Fox's bestselling text An R and S-Plus Companion to Applied Regression (SAGE, 2002). The Second Edition is intended as a companion to any course on modern applied regression analysis. The authors provide a step-by-step guide to using the high-quality free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, enhanced coverage of R graphics and programming, and substantial web-based support materials. |
Contents
Chapter 1 Getting Started With R | 1 |
Chapter 2 Reading and Manipulating Data | 43 |
Chapter 3 Exploring and Transforming Data | 107 |
Chapter 4 Fitting Linear Models | 149 |
Chapter 5 Fitting Generalized Linear Models | 229 |
Chapter 6 Diagnosing Problems in Linear and Generalized Linear Models | 285 |
Chapter 7 Drawing Graphs | 329 |
Chapter 8 Writing Programs | 359 |
| 425 | |
Author Index | 431 |
| 433 | |
| 441 | |
| 447 | |
| 448 | |
About the Authors | 449 |
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Common terms and phrases
additional analysis ANOVA applied argument axis called car package chapter character close coefficients column command computed conditional contains corresponding create data frame data set default defined described deviance discussed distribution draw effect elements equal error estimated example expression factor FALSE female Figure formula function given GLMs graph graphics income interactions Intercept labels levels linear models male matrix mean method missing names normal NULL object observations operators parameter plot points predictors present prestige printed problem produces prof programming provides regression regressors represent residuals response result returns sample scale scatterplot Section shown simple specified squares standard statistical suggest summary tion transformation TRUE usual values variable variance vector weights Windows women
