Applied Regression Analysis (BUS 41100)
 Instructor: Max H. Farrell – max.farrell@chicagobooth.edu
 Office hours: By appointment
 TA: Omar Ghattas & Sam Higbee – reach both at max.farrell.ta@gmail.com
 TA office hours: By appointment
Course material:

 Syllabus
 This website: for all slides, homework, and data sets
 Piazza for Q+A: your first stop for help
 No Canvas site
If you are looking for old course material: 2020 remote version  2019 10week version
Before class starts:
 Homework Zero – To test your readiness for this course
 Take the course selection quiz available here to help you decide between this class and Business Statistics (41000)
 Get started on R before class: see Computing below, in particular check out the swirl package.
 If you want, download all the course material below in one archive here. This is not updated during the quarter
Notices
Any updates/changes will be listed here.
 Syllabus version 1.0 posted. Current as of first lecture.
 9/27: Syllabus version 1.2 posted.
 10/3:
 Typo fixed on slide 50 of week 1.
 Syllabus version 1.3 posted.
 10/3: Practice midterm and final exams posted below.
Lectures
These may be updated as we go along, so always download the latest version

Week 1: Introduction, Simple Linear Regression (SLR)

Week 2: Inference for SLR

Week 3: Multiple Linear Regression (MLR)

Week 4: MLR Pitfalls, Some Fixes, Clustered and Panel Data

Week 5: Causal Inference

Week 6: Logistic Regression

Week 7: Model Building

Week 8: An Introduction to Time Series

Week 9: Discrete Outcomes: Multinomial Choice and Count Data

Week 10: Final Exam!
Homework

Homework 0  No data required, no solutions available

Homework 1  box plots, scatter plots, stock market, teacher salaries

Homework 2  Monte Carlo code  SMSA data, tractors

Homework 3  beef, nutrition, crime stats, newspapers

Homework 4  pricing experiment, cheese, Grunfeld, NSW+PSID

Homework 5  community crime, bike sharing, pricing experiment

Homework 6  UK gas consumption, US gas price, furniture
Handouts
Computing
The default computing language for this course is R, is free (as in speech and beer) and available from CRAN. Other languages (e.g. python, MATLAB, STATA, ...) are allowed. Examples in lecture, homework solutions, etc., will be in R.
Get started before class starts!
 The swirl package is an interactive tool for learning R within R. A good start is the course "R Programming".
 A good introduction/tutorial to R is here.
 UCLA has a fantastic help page for R (and statistics/regression in general) with everything from installation/basic help, workedthrough examples, books, and link to more resources.
 The University offers R workshops in the Research Computing Center, see schedule here and has ebooks available here.
 The resources out there are continually changing, so you may find other options. Please let me know if you find something helpful that isn't listed here.