Multivariate Statistics

Graduate / Professional

Students learn both practical data analysis and the basic underlying theory. There is a slightly higher emphasis on probability theory than in many applied statistics courses, with the goal of making concepts such as confidence intervals and p-values easier to understand. However, care is taken to make this material accessible by providing numerous psychology-relevant examples and avoiding unnecessary mathematical details. To promote deeper understanding, commonly used statistical models and tests (t-tests, ANOVA, simple linear regression, multiple regression, ANCOVA) are explained as special cases of linear regression rather than separate procedures. Other topics include mixed effects models and generalized linear models (e.g. logistic regression) besides other material depending on time and student interest. Alongside statistical theory, students learn to use R, a computer programming language specialized for data analysis. T 2:35pm-3:50pm