The course provides an introduction into statistical methodology for life sciences and discusses a number of statistical techniques for practical data analysis. Concrete examples and case studies are used to apply the theory to practical situations.
Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalizations of linear regression (e.g., multilevel modeling, SEM, neural networks, GLM, survival analysis). In this workshop, participants will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor variables, moderation, prediction, and diagnostics. Workshop participants will practice what they learn via practical exercises.