Undergraduate Courses

Basics of probability theory. Conditional probability and Bayes theorem. Random variables and distributions. Descriptive statistics. Graphic representation and statistical measures. Con-fidence intervals. Hypothesis and significance tests. Test for fitting and contingency tables. Regression and correlation. Analysis of variance, Total variance, Distribution of variances. Analysis of variance. Non-parametric tests.

Statistical programming techniques with R. Data structures for statistical analysis. Description and visualization of multivariate data. Hypotheses tests and multivariate methodologies focusing on algorithmic techniques for distribution estimation, random number generation, confidence intervals and hypothesis tests with resampling, non parametric regression, etc.