Data Analysis

3 credits · Graduate

This course serves both as a refresher for undergraduate statistics and as a preparation for more advanced courses. This course will cover fundamental principles of statistical thinking including probability theory, distributions, modeling, parameter fitting, error estimation, statistical significance and cross-validation. In addition, the course will cover all statistical tests that are part of the generalized mixed effect models: n-way analysis of variance (ANOVA), multiple regression, analysis of covariance, logistic regression, between subjects, within subjects, mixed designs and designs with random factors. Students will also be introduced to statistical programming using the computer language R.