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Dave Armstrong
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LaTeX Workshop
These are the notes and all files required to successfully compile the handouts from the workshop on Monday June 20 and July 18, 2011.[LaTeX Presentation][LaTeX Code to Compile Handout]
Introduction to R
These are the handouts and R code from the Introduction to R workshop offered from July 20-30, 2010.
     Lecture 1: Getting to know R/Data Types [handout][Data and R-code (zip)]
     Lecture 2: Data Types/Models [handout] [R-code]
     Lecture 3: Graphs (Traditional) [handout] [R-code] [Tables: R to Word]
     Lecture 4: Lattice Graphs [handout] [R-code] [Code and Data for Maps in R]
     Lecture 5: Repeated Calculations and other Data Issues [handout] [R-code] [Data and R-code (zip)]
     Lecture 6: Effects Package [handout] [R-code] [Data and R-code (zip)]
     Questions and wrap-up [R-code]
Regression III
Instructor:
Dave Armstrong
University of Wisconsin--Milwaukee (Political Science)
e: armstrod@uwm.edu
Teaching Assistant:
Kelly Gleason
University of Wisconsin--Milwaukee (Political Science)
e: kgleason(at)uwm(dot)edu
 
This is the website devoted to the Regression III course for the ICPSR Summer Program in Quantitative Methods. At some point before the Summer, the links may be removed in preparation for the upcoming course, though please don't hesitate to contact me if you're looking for something and can't find it.
 
Course Materials: Syllabus
Lecture 1: Introduction [slides] [R-code:text, html] [Data and R-code (zip)]

Lecture 2: OLS [slides] [R-code:text, html]

Lecture 3: Effective Model Presentation [slides] [R-code:text, html] [Example Quasi-variance Table]
     Homework 1 [data]

Lecture 4: Linearity [slides] [R-code:text, html] [Data and R-code (zip)]

Lecture 5: Non-linearity and Splines [slides] [R-code:text, html]

Lecture 6: GAMs [slides] [R-code:text, html] [Data and R-code (zip)]

Lab 1: Non-Linearity and Interactions [Answers] [R-code:text, html] [Data (Stata format)]
     Homework 2

Lecture 7: Resampling Methods [slides, addendum] [R-code: text, html, Loess Cross-validation]

Lecture 8: Outliers and Influential Data [slides] [R-code:text, html] [Data and R-code (zip)]

Lecture 9: Robust Regression [slides] [R-code:text, html] [Data and R-code (zip)]

Lecture 10: Non-Normality and Heteroskedasticity [slides] [R-code:text, html] [Data and R-code (zip)]

     Homework 3 [data]

Lecture 11: Model Selection [slides] [R-code:text, html]

Lecture 12: Mixture Models [slides] [R-code:text, html] [Data and R-Code (zip)]
Bonus: Missing Data and Multiple Imputation [slides] [R-code] [Data and R-Code (zip)]
Lab 2: Bootstrap/Cross-Validation, Outliers/Robust Regression and Mixture Models [Answers] [R-code:text, html]