Applied Regression Analysis

3 credits · Undergraduate

(3 hrs Lecture) This course provides students with the skills necessary to perform regression analyses and to interpret statistical issues related to regression applications in many areas, especially in health sciences. Topics include linear and nonlinear regression models, residual diagnostics, multicollinearity, model selection, transformations and weighted least squares, measures of influence and generalized linear models with a focus on logistic and Poisson regression. The statistical software R or SAS is used throughout the course and applications to real-life data are an integral part of the course. Requisite Courses: STAT-1302 or STAT-2001, and MATH-1201 [prerequisite(s)].