Applied Regression Analysis (BUS 41100)
This website is ONLY for Autumn 2020, the remote version of 41100. For course material from previous quarters, go here.
 Instructor: Max H. Farrell
 Office hours (over Zoom): Wednesday 4:30 – 6PM, Saturday 11:30AM – 1PM
 TA: Gustavo Gonzalez
 Syllabus
Course material:
 This website: for all slides, homework, and data sets
 Piazza for Q+A: your first stop for help
 Zoom: all lectures will be over zoom, links will be sent in advance to students, and recordings will be available
 No Canvas site
Before class starts:
 Homework Zero – To test your readiness for this course
 Get started on R before class: see Computing below
Notices
Any updates/changes will be listed here.
 Syllabus updated: version 1.2 has new course schedule, including the midterm!
 Syllabus updated again: version 1.3.1
 New version of homeworks 5 and 6 posted
Lectures
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

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
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.