Applied Regression Analysis (BUS 41100)
This website does NOT apply for Autumn 2020. This site is maintained for reference only. For Autumn 2020, go here.
- Instructor: Max H. Farrell
- TA: TBA
- TA office hours: TBA
- Syllabus – Lots of info here! Check before you ask.
Q+A on Piazza – Your first stop for help
Jump to: Lecture Material | Homework | Handouts | Computing/R help
Before class starts
- Homework Zero – To test your readiness for this course
- Get started on R before class: see Computing below
- If you want, download all course material (slides, code, data ...) in one archive
Any updates/changes will be listed here.
- September 19: Office Hours posted above (Mon 4:30-5:30, Tue 5-6)
- October 15: Typos fixed in week 4 slides
- October 16: New version of homework 4 is posted, see email
Week 1: Introduction, Simple Linear Regression (SLR)
Week 2: Inference for SLR
Week 3: Multiple Linear Regression (MLR)
Week 4: Logistic Regression and Classification
Week 5: An Introduction to Time Series
Week 6: Panel and Clustered Data
Week 7: Regression Issues and Diagnostics
Week 8: Model Building 1
Week 9: Model Building 2 and Causality
Week 10: Discrete Outcomes: Multinomial Choice and Count Data
Week 11: Final Exam!
Homework 0 | No data required, no solutions available
Homework 1 | box plots, scatter plots, stock market, teacher's pay
Homework 2 | Monte Carlo code | tractors, smsa
Homework 3 | nutrition, beef, crime
Homework 4 | pricing experiment
Homework 5 | UK gas consumption, US gas price, furniture
Homework 6 | Grunfeld
Homework 7 | transformations, nutrition, newspapers, cheese, furniture
Homework 8 | Monte Carlo code | crime, bike sharing, pricing experiment
The default computing language for this course is R, is free (as in speech) 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!
- 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.