STA 210: Regression Analysis

Learn approaches for analyzing multivariate data sets, emphasizing analysis of variance, linear regression, and logistic regression. Learn techniques for checking the appropriateness of proposed models, such as residual analyses and case influence diagnostics, and techniques for selecting models. Gain experience dealing with the challenges that arise in practice through assignments that utilize real-world data. This class emphasizes data analysis over mathematical theory.


Portions of the course schedule and syllabus have been updated due to the transition to remote learning. Please see the Remote Learning Updates and Schedule for the most up-to-date information about the course.



Course info

Lectures

  Gross Hall 103      Mon and Wed 10:05a - 11:20a

Labs

Lab 01      Link Classroom #5       Thu 3:05p - 4:20p

Lab 02      Link Classroom #5       Thu 4:40p - 5:55p

Lab 03      Link Classroom #1       Thu 4:40p - 5:55p

Teaching team and office hours
Instructor Prof. Maria Tackett   Tue 8:30a - 10a, Thu 10:30a - 12p Old Chem 118B
TAs Youngsoo Baek   Mon 1p - 3p Old Chem 203B
Cody Coombs   Tue 1p - 3p Old Chem 203B
Sophie Dalldorf   Fri 1p - 3p Old Chem 203B
Jonathan Klus   Mon 3p - 5p Old Chem 203B
Matty Pahren   Tue 3p - 5p Old Chem 203B
Ethan Shen   Wed 3p - 5p Old Chem 203B

Textbooks
Handbook of Regression Analsyis James, Witten, Hastie, Tibshirani Springer, 1st edition, 2013
R for Data Science Chatterjee, Simonoff Wiley, 1st edition, 2013