Linear regression - theory, methods and application(s). Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Includes: Experiential Learning Activity Precludes additional credit for STAT 3503. Prerequisite(s): i) STAT 2559 with a grade of C- or higher, or STAT 2509 with a grade of B or higher, or DATA 1519 with a grade of C or higher; and ii) a grade of C- or higher in MATH 1152 or MATH 1107 or MATH 1104; or permission from the School of Mathematics and Statistics. Lectures three hours a week, laboratory one hour a week.
Linear regression - theory, methods and application(s). Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Includes: Experiential Learning Activity Precludes additional credit for STAT 3503. Prerequisite(s): i) STAT 2559 with a grade of C- or higher, or STAT 2509 with a grade of B or higher, or DATA 1519 with a grade of C or higher; and ii) a grade of C- or higher in MATH 1152 or MATH 1107 or MATH 1104; or permission from the School of Mathematics and Statistics. Lectures three hours a week, laboratory one hour a week.
