Spring 2016

Introduction to Statistics via Modeling

Listed in: Mathematics and Statistics, as MATH-135  |  Mathematics and Statistics, as STAT-135

Moodle sites: Section 01  |  Section 02 (Guest Accessible with password)


Joanna M. Jeneralczuk (Section 01)
Leah J. Kim (Section 01)
Sangmin Song (Section 02)
Xiaofei S. Wang (Section 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.

Cost: 170 ?


Quantitative Reasoning, Science & Math for Non-majors


2022-23: Not offered
Other years: Offered in Spring 2014, Fall 2014, Spring 2015, Fall 2015, Spring 2016, Fall 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, January 2021, Spring 2021, Fall 2021, Spring 2022