This course provides students with an introduction to computing in a popular statistical programming language such as R. Topics include data structures, reading and storing data, data transformation and manipulation, accessing and using packages, conditionals, loops, functions, graphics and data visualization, and introductory statistical methods for data analysis. No previous programming experience is required.

This course is a continuation of Statistics I and covers additional statistical concepts used in the physical sciences, social sciences, business, and health studies. Topics include, but are not limited to, regression analysis (multiple regression, logistic regression, and regression with categorical predictors), analysis of variance (one-way and two-way models), analysis of categorical variables (measures of association, chi-squared tests, odds ratio, and relative risk), and non-parametric tests. One professional statistical package, such as R, will be used throughout the course. Students should have a laptop with the required software installed.