The course aims to get students familiar with statistical reasoning, fitting and interpreting statistical models, and using data to make decisions. Students are assumed to have some background in statistical concepts and have a basic understanding of common descriptive statistics and bivariate association (e.g., Pearson correlation).

This course will focus on using statistical methods to answer research questions and make decisions from data. General linear models (i.e., regression) will serve as the fundamental building block that students will be exposed to answer questions about associations of quantitative outcomes with quantitative and categorical predictors. Statistical estimation, model building, and inference using statistical software will serve as primary topics which students will gain a stronger understanding by the end of the course.