STAT 109: Intro to Statistical Modelling (Spring 2020)

Teaching Fellow

Stat 109 is a second course in statistical inference and is a further examination of statistics and data analysis beyond the introductory course. Topics include t‐tools and permutation‐based alternatives including bootstrapping, analysis of variance, linear regression, model checking and refinement. Statistical computing and simulation based emphasis will also be covered as well as basic programming in the R statistical package. Emphasis is made on thinking statistically, evaluating assumptions, and developing tools for real‐life applications. Note that Stat 109 cannot be taken for credit if Stat 139 has already been taken.

By the end of the course, students should be able to evaluate the strengths and weaknesses of a variety of statistical techniques appearing in the media, scientific literature, or students’ own work. Given a data set, students should be able to:

• state hypotheses;

• explore the data using statistical software;

• determine which statistical model may be appropriate;

• apply corresponding hypotheses tests;

• check the assumptions behind these tests and models;

• interpret the results of the analysis to draw conclusions about the hypotheses.