There are about 5 homework problems every chapter, half of which will user R and the other half theory/math, with one comprehensive final assigment with 10 problems mostily in R. Strangely during the end, the course basically becomes a design and analysis of experiment class for two long chapters. The course will teach you some coding essentials, as well as regression in R. Overall, the course goes by fast, all content will reappear in depth in Stat5131 applied regression analysis.

Interaction Regression Model Nested Model F test Model Assumptions R Coding Design & Analysis of Experiments - Completely Random Design - Random Block Design - Random Two Factor Factorial Design

Provides comprehensive lectures, will explain things in depth. Creates a Review exam to study for exams. He usually follows a consistent schedule and will expect you to know things from stat2037 prob & stat I

Make sure to show your work on all math problems, especially when standardizing to a standard normal distribution. Include test statistics, null hypthesis, alpha and rejection region/p-value Also, in all regression models error e ~iid N(0,1) must be explicitly stated. He can be a stickler for these things, and rightfully so.