Bobby's Teaching Page
This academic year, 2013-14, I am teaching three sections of Applied Regression Analysis (BUS 41100) in the Fall Quarter, and one section of Bayesian Inference (BUS 41913) in the Spring Quarter, within the Booth School of Business at the University of Chicago.
2014 Fall BUS 41913 Bayesian Inference
BUS 41913 is a graduate course in Bayesian Inference. The course will focus on understanding the principles underlying Bayesian modeling and on building experience in the use of Bayesian analysis for making inference about real world problems. Particular attention will be paid to the computational techniques (e.g., MCMC) needed for most problems and their implementation in the R language for statistical computing.
Course syllabus (including required and recommended texts)
- The Final is ready (datafile), and is due 13 June 2014.
- The "Midterm" exam will be on Tuesday 20 May; The 15 May class will be devoted to review and questions. Solutions here.
- We will have a short Quiz at the end of class on Thursday 17 April.
Homeworks: (due in class at the start of lecture on the date indicated)
- Homework 1 covering Parts 0-1, due 10 April 2014
- solutions: pdf
- Homework 2 covering Part 2, due 17 April 2014
- Homework 3 covering Part 3, due 24 April 2014
- Homework 4 covering Part 4, due 1 May 2014
- Homework 5 covering Part 5, due 8 May 2014
- Homework 6 covering Part 6, due 15 May 2014
- Homework 7 covering Part 7, due 29 May 2014
- Homework 8 covering Part 8, due 5 June 2014
- Parts 0 & 1: Introduction and fundamentals
- Part 2: One parameter models
- Part 3: Monte Carlo inference
- Part 4: Multi-parameter and normal models
- Part 5: MCMC: Metropolis and Gibbs samplers
- supplementary MH proof sketch
- Part 6: Multivariate normal and linear models
- Part 7: Hierarchical modeling
- Part 8: Model criticism, selection, and averaging
- Part 9: GLMs and hierarchical LMs and GLMs
- Part 10: Latent variables and missing data
- Parts 0 & 1: Rare event and binomal, Poisson, and normal examples
- Part 2: beta binomal, and poisson examples on education v. fertility (datafile) and heart transplants
- Part 3: plug-in Monte Carlo, rejection sampling, and importance sampling
- Part 4: midge, IQ, and CBS poll
- Part 5: Gibbs (for normals), Metropolis-Hastings (for normals), a RW-MH example, and effective sample size
- Part 6: bivariate normal, random Wishart draws, reading (datafile), and O2 (datafile)
- Part 7: math scores, (datafile), and rat tumors, (datafile)
- Part 8: diabetes, and RJ-MCMC
- Part 9: sparrow (datafile), and math scores (datafile), and mice (datafile)
- Part 10: data augmentation
Computing: The recommended language for this course is R, which can be obtained from CRAN. Other languages such as MATLAB are allowed but are not recommended. Examples in lecture, and help in office hours, etc., will be exclusively in R.
2013 Applied Regression Analysis (BUS41100) Sections 01, 02 and 85
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).
- By request, R code has been added to the end of lect4.R with sales-price prediction intervals back on the original scale.
- Week 1: Introduction to Correlated Data
- Week 2: Simple Linear Regression
- Week 3: Inference and Estimation for SLR
- Week 4: Diagnostics and Transformations
- Week 5: Multiple Linear Regression
- Week 7: More Topics in MLR
- Week 8: Model Choice and Data Mining
- Week 9: An Introduction to Time Series
- Week 10: Binary Data and Classification
- 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
- Solutions: in R code
- Homework 6, requiring data
on mortality and pollution,
- Solutions: in R code
- Homework 7, requiring data
UK gas consumption
- Solutions: in R code
- The take home final is due Friday December 13
- The midterm project, requiring data on property tax was assigned on October 25th. It is due November 1/2 at the start of class; solutions
- The midterm is on Noveber 1/2 (sixth week); solutions.
- Last quarter's:
- The take home final will be available on November 22, and is due on Friday December 13.
Computing: The recommended language for this course is R, which can be obtained from CRAN. Other languagehs 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.
- The University offers R tutoring in the Regenstein library
- 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
- 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 -- 2013