Fall 2015

Introduction to Statistics via Modeling

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

Faculty

Xiaofei S. Wang (Sections 01 and 02)

Description

(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: preference will be given to Mathematics majors

STAT 135 - LAB

Section 01
Tu 10:00 AM - 11:20 AM MERR 131

Section 02
Tu 02:30 PM - 03:50 PM MERR 131

STAT 135 - LEC

Section 01
W 10:00 AM - 10:50 AM MERR 131
F 10:00 AM - 10:50 AM MERR 131

Section 02
W 11:00 AM - 11:50 AM MERR 131
F 11:00 AM - 11:50 AM MERR 131

This is preliminary information about books for this course. Please contact your instructor or the Academic Coordinator for the department, before attempting to purchase these books.

Section(s) ISBN Title Publisher Author(s) Comment Book Store Price
All Stats: Data & Models Pearson De Veaux, Velleman, and Bock 3rd ed/4th ed. Amherst Books TBD

These books are available locally at Amherst Books.

Offerings

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, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024