36-401 Modern Regression
Instructor: Larry Wasserman
Time: Tuesday and Thursday 12:00 - 1:20
Place: PH 100
Office Hour: Tuesdays 1:30 - 2:30 Baker Hall 132G
TA InformationTA: Collin Eubanks (Head TA) Email: email@example.com Office Hours: Thursdays 1:30 - 2:30 BH 132A
TA: Riccardo Fogliato Email: firstname.lastname@example.org Office Hours: Wednesdays 3:30 - 4:30 BH 132A
TA: Boyan Duan Email: email@example.com Office Hours: Thursdays 10:30 - 11:30 Wean Hall 4625
TA: Xiaoyi Gu Email: firstname.lastname@example.org Office Hours: Friday 10:00-11:00 BH132Q
Course Assistant: Mari-Alice McShane email@example.com Office: Baker Hall 229K
SyllabusClick here for syllabus
Course DescriptionThis course is an introduction to applied data analysis. We will explore data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. The course includes a review and discussion of exploratory methods, informal techniques for summarizing and viewing data. We then consider simple linear regression, a model that uses only one predictor. After briefly reviewing some linear algebra, we turn to multiple linear regression, a model that uses multiple variables to predict the response of interest. For all models, we will examine the underlying assumptions. More specifically, do the data support the assumptions? Do they contradict them? What are the consequences for inference? Finally, we will explore extra topics such as nonlinear regression or regression with time-dependent data. A minimum grade of C in any one of the pre-requisites is required. A grade of C is required to move on to 36-402 or any 36-46x course. Prerequisites: At least a C grade in (36-226 or 36-625 or 73-407 or 36-310) and (21-240 or 21-241).
Textbook: Applied Linear Regression Models, Fourth Edition by Kutner, Nachtsheim and Neter.
R Stuff An R Tutorial data for R Tutorial R reference card A thorough R tutorial
PrerequisitesPrerequisites: At least a C grade in (36-226 or 36-625 or 73-407 or 36-310) and (21-240 or 21-241).
AssignmentsAssignments are due on Fridays at 3:00 p.m. Upload your assignment in Canvas.
No late assignments will be accepted. If you need an extension due to illness, email me BEFORE the deadline.
Homework 1 due Friday September 8 by 3:00. Upload it using Canvas. Homework 2 due Friday September 15 by 3:00. Upload it using Canvas. Homework 3 due Friday September 22 by 3:00. Upload it using Canvas. Homework 4 due Friday September 29 by 3:00. Upload it using Canvas.
SolutionsHomework 1 Solutions Homework 2 Solutions
Lecture Notes (Written by Professor Cosma Shalizi)Download the notes and bring them to class.
Lecture Notes 1 Lecture 2 was an R tutorial Lecture Notes 3 Lecture Notes 4 Lecture Notes 5 Lecture Notes 6 Lecture Notes 7 Lecture Notes 8 Lecture Notes 9 Lecture Notes 10