
R software
 Using
R for introductory statistics
by John Verzani (2005). Textbook presenting elementary concepts and
methods with extensive R code examples. Includes univariate, bivariate
and multivariate data, statistical distributions, simulations,
confidence intervals, significance tests, goodness of fit, linear and
nonlinear regression, ANOVA, and appendices of graphics, GUIs and
programming in R.
 Statistics:
An introduction using R
by Michael J. Crawley (2005). Introductory statistics with strong focus
on R code. Topics include variance, single and two samples, linear
modeling, regression, ANOVA and ANCOVA, Poisson data, proportions, and
censoring.
 Data
analysis and graphics using R: An examplebased approach
by John Maindonald and John Braun (2003). Textbook seeking to provide
tools for scientists performing statistical analyses. Topics include
introduction to R, statistical distributions, formal inference, linear
and multiple regression, smoothing, Poisson regression, ANOVA, time
series, treebased classification, multivariate exploration and
discrimination.
 Modern
applied statistics with SPLUS
by W. N. Venables and B. D. Ripley (4th ed, 2003). Wellrespected
textbook with extensive S (closely related to R) code, it discusses S
programming and graphics, linear and nonlinear modeling, robust
statistics, censoring, multivariate analysis, treebased methods, time
series and spatial point processes.
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