
Learn how p values indicate statistical significance in multiple linear regression, with separate coefficients showing significance below 0.05 for attendance and study, supporting including both in the model.
Include binary variables in regression by coding college and gender as 0/1, interpreting coefficients as comparisons to the base groups, with significant GPA differences guiding future models.
Explore how diagnostic tools identify outliers and influential observations in multiple regression using added variable plots, revealing when a few points skew results.
Explore how to compute probabilities and odds for a binary outcome in a two-by-three table, derive odds ratios between groups, and link these ideas to logistic regression.
Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies.