I often use R for computations and graphs. I will post some of the R code I use. This is only for those interested. Nothing here is required.

If you prefer to use R instead of SAS, be aware of potential difficulties when we do ANOVA. It can be very hard to get certain useful quantities from R. You can always program up the computations, but you don't get them automatically.

- Week 1: Design-based inference
- randtest.r Randomization test and comparison to T
- boot.r Simple bootstrap

- Week 2 / lab 2: Summary statistics
- tomato.r Summary statistics and graphics of tomato data

- Week 3 / lab 3: T tests and quantiles/probability calculations
- Week 4 / lab 4: Diagnostics, non-parametric tests
- sparrow2.r Diagnostic plots, Levene's test, Wilcoxon rank sum test

- Week 5 / lab 5: ANOVA
- judges.r fitting an ANOVA model, very limited facility for estimates and contrasts.

- Week 6 / lab 6: ANOVA and plots for blocked and paired data
- schiz.r paired t-test, Wilcoxon signed rank tests, and fitting ANOVA's for blocked data. Again, very limited facilities for estimates and contrasts
- schiz2.r plotting data from block designs: either using pair number as the X axis, or the block mean as the X axis.

- Week 7 / lab 7: Regression and correlation
- corr.r Correlations, including test and ci
- meat.r Regression, including ANOVA lack of fit test
- boxcox.r Estimating Box-Cox transformation power for 1-way designs.

- Week 8 / lab 8: Multiple regression
- brain.r Multiple regression

- Week 9 / lab 9: Diagnostics for multiple regression
- brain2.r Case diagnostics
- births2.r Loess and smoothing splines, Breusch-Pagan
- births3.r Regression with ar(1) error structure, Information about Durbin-Watson test

- Week 10 / lab 10: Indicator variables
- bacillus2.r Indicator variables

- Week 11 / lab 11: Model selection
- bear.r Model selection in R (at least the little I know, since I don't use R for this).

- Week 12 / lab 12: ANOVA
- ratweight.r 2 way ANOVA in R.

Beware: R works well for balanced data and badly for unbalanced data. This is one place SAS is much, much better than R.

- ratweight.r 2 way ANOVA in R.
- Week 13 / lab 13: random effects
- wool.r 1 way random effects
- wool2.r Two nested random effects
- splitteach.r Split plot analysis in R