Bobby's Teaching Page
This academic year, 2012-13, I am teaching two sections of Applied Regression Analysis (BUS41100) in the Fall Quarter within the Booth School of Business at the University of Chicago.
2012 Applied Regression Analysis (BUS41100) Sections 01 and 81
BUS 41100 is a course about regression, a powerful and widely used data analysis technique. Students will learn how to use regression to analyze a variety of complex real world problems. Heavy emphasis will be placed on analysis of actual datasets, and implementation in the R language for statistical computing. Topics covered include: simple linear regression, multiple regression, prediction, variable selection, residual diagnostics, time series (auto-regression), and classification (logistic regression).
Notices:
- The take home final has been assigned. See below.
Lectures:
- Week 1: Introduction to Correlated Data
- R code, requiring data on pickups and wages; and the correlation examples
- Week 2: Simple Linear Regression
- R code, requiring data on mutual funds and the stock market
- Week 3: Inference and
Estimation for SLR
- R code, requiring data on mutual funds
- The demo on sampling distributions for linear models requires the two files linked here
- Week 4: Diagnostics
and Transformations
- R code, requiring the Anscombe data, and data on rents, pickups, telemarketing, imports, and Consolidated Foods, Inc.
- Week 5: Multiple
Linear Regression
- R code, requiring our trusty pickup data, and some synthetic sales data
- Week 7: More Topics in MLR
- R code, requiring census data, and data on supervisors and on grades
- Week 8: Model Choice and Data Mining
- Week 9: An Introduction
to Time Series
- R code, requiring data on airline passengers, beer production, the Dow Jones Industrial Average, and on the weather
- Week 10: Binary Data
and Classification
- R code, requiring data on NBA point spreads, and German credit
Homeworks:
- Homework 1, requiring data on teacher's pay
- Homework 2, requiring data for scatter plots, on tractors, and the stock market
- Homework 3, requiring data on newspapers, and crime
- Homework 4, requiring data on transformations, cheese, and newspapers
- Homework 5, requiring data
on nutrition, and
beef.
- Solutions: in R code
- Homework 6, requiring data
on mortality and pollution,
and newspapers
- Solutions: in R code
- Homework 7, requiring data
on
UK gas consumption
- Solutions: in R code
Exams:
- The take home final
is due Wednesday December 12
- requires data on electricity, police and the spam
- The midterm project was made available on October 31. It requires data on TVs and is due on November 7; solutions.
- The midterm is on Noveber 7 (sixth week); solutions.
- Last quarter's:
- Midterm project, requiring data on property tax, with solutions
- Midterm with solutions
Computing: The recommended language for this course is R, which can be obtained from CRAN. Other languages such as MATLAB, STATA, SAS, MINITAB, etc., are allowed but are not recommended. Examples in lecture, and help in office hours, etc., will be exclusively in R.
- Some helpful video tutorials and step by step guides
- R Studio is an excelent multi-platform graphical interface to R which you will likely prefer to the default Windows/OSX GUI(s).
- An interesting NY Times article on R
- Blogging about success stories and new features in R
- A recent article using regressions to investigate house prices in Chicago with R
- Instructions for changing the default working directory for R on Windows
Miscellaneous:
- An intriguing article on using regressions to understand the nature of attraction
- A fun article on using financial statistical analysis techniques to predict the outcome of the Superbowl. (You need click on the link at the bottom-right of the page and fill your name in the form to see the article.)
Robert B. Gramacy -- 2012