Alison H. Doswell (Sections 01 and 02) Kenton P. Eliot (Sections 01 and 02) Konso Mbakire (Sections 01 and 02) Xiaofei S. Wang (Sections 01 and 02)
(Offered as STAT 135 and MATH 135.) Introduction to Statistics via Modeling is an introductory statistics course that uses modeling as a unifying framework for much of statistics. The course provides a basic foundation in statistics with a major emphasis on constructing models from data. Students learn important concepts of statistics by mastering powerful and relatively advanced statistical techniques using computational tools. Topics include descriptive and inferential statistics, probability (including conditional probabilities and Bayes' rule), multiple regression and an introduction to causal inference. This is a more mathematically rigorous version of STAT 111, formerly MATH 130. (Students may not receive credit for both STAT 111 and MATH 135.) Four class hours per week (two will be held in the computer lab).
Requisite: MATH 111. Limited to 24 students. Fall and spring semesters. Lecturer Wang.
If Overenrolled: In the fall: priority for preregistered sophomores. In the spring: priority for preregistered first year students, then sophomores.