Basics -- R environment -- Probability and distributions -- Descriptive statistics and graphics -- One and two sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- Tabular data -- Power and the computation of sample size -- Advanced data handling -- Multiple regression -- Linear models -- Logistic regression -- Survival analysis -- Rates and poisson regression -- Nonlinear curve fitting

1. Statistics -- Data processing
2. R (Computer program language)