Applied Regression Analysis (BUS 41100)
- Instructor: Max H. Farrell – email@example.com
- Office hours: By appointment
- TA: Omar Ghattas & Sam Higbee – reach both at firstname.lastname@example.org
- TA office hours: By appointment
If you are looking for old course material: 2020 remote version | 2019 10-week version
- This website: for all slides, homework, and data sets
- Piazza for Q+A: your first stop for help
- No Canvas site
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
Any updates/changes will be listed here.
- Syllabus version 1.0 posted. Current as of first lecture.
- 9/27: Syllabus version 1.2 posted.
- Typo fixed on slide 50 of week 1.
- Syllabus version 1.3 posted.
- 10/3: Practice midterm and final exams posted below.
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 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
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, worked-through examples, books, and link to more resources.
- The University offers R workshops in the Research Computing Center, see schedule here and has e-books 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.