# 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

Course syllabus (including required and recommended texts)

Notices:

• 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)

Lectures:

• 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
• 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

Demos:

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).

Notices:

• By request, R code has been added to the end of lect4.R with sales-price prediction intervals back on the original scale.

Lectures:

Homeworks:

Exams:

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.

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 -- 2013

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