Applied Regression Analysis (BUS 41100)
- Instructor: Max H. Farrell
- Office hours: TBD
- TA: TBA
- 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
- Get started on R before class: see Computing below
Nothing updated below this point; stay tuned!
Any updates/changes will be listed here.
These will 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: Finish SLR, Start Multiple Linear Regression (MLR)
Week 4: Multiple Linear Regression Part II
Week 5: MLR Pitfalls and Some Fixes, Clustered and Panel Data
Week 6: An Introduction to Time Series
Week 7: Logistic Regression
Week 8 and Week 9: Classification and Model Building 1
Homework 0 | No data required, no solutions available
Homework 1 | box plots, scatter plots, stock market
Homework 2 | Monte Carlo code | SMSA data
Homework 3 | teacher salaries, tractors
Homework 4 | pricing experiment, cheese, Census data
Homework 5 | newspapers, NSW+PSID, beef
Homework 6 | UK gas consumption, US gas price, crime, bike sharing, pricing experiment, Grunfeld
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.