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 132Q
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
TA: Jining Qin Email: email@example.com Office Hours: Thursdays 4:00-5:00 BH 132Q
Course Assistant: Mari-Alice McShane firstname.lastname@example.org 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).
Data Analysis Project 1 due Friday October 13 by 3:00. Upload it using Canvas.
Data Analysis Project 2 due Tues Nov 21 at 5:00. Upload it on Canvas.
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. Homework 5 due Friday October 20 by 3:00. Upload it using Canvas. Homework 6 due Friday October 27 by 3:00. Upload it using Canvas. Homework 7 due Friday November 3 by 3:00. Upload it using Canvas. Homework 8 due Friday November 10 by 3:00. Upload it using Canvas. Homework 9 due Friday December 1 by 3:00. Upload it using Canvas. Homework 10 due Friday December 8 by 3:00. Upload it using Canvas.
SolutionsHomework 1 Solutions Homework 2 Solutions Homework 3 Solutions Homework 4 Solutions Test 1 Test 1 Solutions Homework 5 Solutions Homework 6 Solutions Homework 7 Solutions Homework 8 Solutions Homework 9 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 Lecture 11 was review Lecture 12 was the test Lecture Notes 13 Lecture Notes 14 Lecture Notes 15 Lecture Notes 16 Lecture Notes 17 Lecture Notes 18 Lecture Notes 19 Lecture Notes 20 Lecture Notes 21 Lecture Notes 22 Lecture Notes 24 Lecture Notes 27 Nonparametric Regression Causal Inference