Applied Regression Analysis (BUS 41100)
 Instructor: Max H. Farrell
 TA: Mauricio Chikitani, Gustavo Gonzalez, Min Park
 TA office hours:
 Monday 4:30–5:30pm — Harper 3A
 Tuesday 5:00–6:00pm — Harper 1009A
 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
Notices
Any updates/changes will be listed here.
 September 19: Office Hours posted above (Mon 4:305:30, Tue 56)
 October 15: Typos fixed in week 4 slides
 October 16: New version of homework 4 is posted, see email
Lectures

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

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
Handouts
Computing
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, 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.