The Regression III course takes a considerably different form than the first two regression courses at the Summer Program. This course will hopefully prepare you for the things you will encounter when you (attempt to) publish quantitative work with linear models, and more complicated ones, too.
Initial linear model classes focus on the assumptions and theoretical considerations of linear models and generally walk you through estimation and interpretation. Good courses also deal with diagnostics, though these often get less time than they should. Further, it is not always obvious what violations of these assumptions will lead to in practical terms.
This course will provide you with a systematic approach to assessing, fixing and presenting your linear model results. Though we focus almost exclusively on the linear model (we will allude to nonlinear models occasionally), the logic we follow will be helpful in dealing with nonlinear models as well. More details can be found in the syllabus
Dave’s Office Hours: 10:0011:30AM MF
TAs

Chris Schwarz (NYU, Political Science)
Office Hours: 12:302PM MF 
Nick Davis (UWMilwaukee, Political Science)
Office Hours : 12:302PM MF
1 Introduction
 Slides pdf
23 Effective Model Presentation
 Slides pdf
 Code r
 To install the most recent version of the
DAMisc
package, you can do the following:
install.packages("devtools")
library(devtools)
install_github("davidaarmstrong/damisc")
library(DAMisc)
4 Lab 1: Factors and Interactions (Angell Hall, Computer Classrooms B and C)
Homework 1: Linear Model Presentation
The posted homework was updated on Saturday 6/30 at 4PM. If you’ve already done it, there is no need to redo it. If not, this new version clarifies exactly what we are asking for on question 1.
56 Linearity
Homework 2: Transformations and Polynomials
7 Resampling Methods
8 Model Selection/Multimodel Inference
911 Flexible Methods for Nonlinearity: Splines, Smoothing, GAMs, KRLS
12 Regression Trees
13 DSS and Regression Diagnostics
Homework 3: Nonparametric Models
library(devtools)
install_github("davidaarmstrong/polywog")
14 Lab 2: Nonlinearity
For this lab, you’ll need to have the following packages installed xgboost
, earth
, rpart
, randomForest
, pdp
, ICEbox
and bartMachine
. All of these should install with a simple call to install.packages()
in the usual way. Installing polywog
from my github is a bit more complicated. I tried this on the UMich computing site and here’s what worked.
install.packages(devtools)
library(devtools)
install_github("davidaarmstrong/polywog")
At this point, the computer asked me if I wanted to install Rtools because polywog
needs something there to compile it. I said Yes
. Then, the install of polywog
failed, but the installation of Rtools continued. After Rtools finishes installing, do the following:
install.packages("Rcpp")
install_github("davidaarmstrong/polywog")
If that fails, you can try installing this Windows binary or this macOS binary. To make it work, download the file, change R’s working directory to the appropriate folder (where you downloaded polywog.zip
) and do the following:
install.packages("polywog.zip", repos=NULL)
install.packages("polywog_0.41.tgz", repos=NULL)
15 Regression Discontinuity Designs
16 Missing Data and Multiple Imputation
Homework 4 (optional)
 Instructions pdf