Lectures           Click on lecture titles to view slides or the buttons to download them as PDFs.

Topic 1

Introduction to the Course and to R    

R code and data for the GDP example. R code and data from the fertility example.


You’ll find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2. (Note: These recommendations may seem dated, as many students prefer to use RStudio as an integrated design environment in combination with RMarkdown. You are free to follow that model, which minimizes start-up costs. I still prefer a combination of Emacs, the plain R console, and Latex/XeLatex for my own productivity, with occasional use of Adobe Illustrator for graphics touch-up.)

Topic 2

Review of Matrix Algebra for Regression
and Regression and Graphics in R    

We will work through Kevin Quinn’s matrix algebra review.


R code and csv data for an example of how the base graphics package can create scatterplots and perform linear regression.

Topic 3

Linear Regression in Matrix Form and
Properties and Assumptions of Linear Regression    

You may find useful three review lectures on basic probability theory, discrete distributions, and continuous distributions.

Topic 4

Inference and Interpretation of Linear Regression    

Example code for estimating a linear regression, extracting confidence intervals for the parameters, and plotting fitted values with a confidence envelope.

Topic 5

Specification and Fitting in Linear Regression    

Topic 6

Outliers and Robust Regression Techniques    

Student Assignments

Problem Set 1  

Due Tuesday, 15 April, in class

Data for problem 1 in comma-separated variable format.

Problem Set 2  

Due Friday, 25 April, in section

Data for problem 1 in comma-separated variable format.

Problem Set 3  

Due Tuesday, 6 May, in class

Five R script templates for simulation of the performance of linear regression with different kinds of data: when the Gauss-Markov assumptions apply; when there is an omitted variable; when there is selection on the response variable; when there is heteroskedasticity; and when there is autocorrelation in the response variable.

Problem Set 4  

Due Tuesday, 20 May, in class

Data for problem 2 in comma-separated variable format.

Problem Set 5 (Optional)  

Due Friday, 6 June, in section

Data for problems 1. Data for problem 2. Data for problem 3. (All data in comma-separated variable format.)

Final Paper

Due Monday, 9 June, at 3:00 PM, in my Gowen mailbox

See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers.