Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. Topic 1 Introduction to the Course, Probability, and R Supplementary
material: You may want to read through Kevin Quinn’s matrix algebra and probability distribution reviews, or consult my undergrad lectures on discrete and continuous distributions. For a more general review, you can find the lecture notes for the CSSS Math Camp here. Topic 2 Introduction to Maximum Likelihood Supplementary
material: The R code to simulate heteroskedastic data and model that data using a heteroskedastic normal maximum likelihood is here. Topic 3 Models of Binary Data Supplementary material: There are separate R scripts for interpreting and selecting binary logit models, as well as an example dataset. The goodness of fit code also relies on R functions for computing the percent correctly predicted and making predicted-versus-actual plots, which you should place in your working directory. An example predicted vs actual plot can be seen here. Topic 4 Models of Ordered Data Supplementary material: R code and data for an ordered probit, which produces graphics for expected value plots and first difference plots. Topic 5 Models of Nominal Data Supplementary material: R code for a multinomial logit, which produces graphics for expected value plots, first difference plots, and relative risk plots. Topic 6 Models of Count Data Supplementary material: R code for count data models, including the Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The data for these examples comes either as a complete dataset, or a dataset with zero-count observations deleted. Advanced Topic 1 Missing Data and Multiple Imputation Advanced Topic 2 Introduction to Multilevel Models: Supplementary material: For the curious, the R script used to construct the example plots in the first half of this lecture is here. Self-Study Lecture 1 Introduction to Contingency Tables Supplementary material: This lecture and the two below it introduce log-linear models of tabular data, and will not be presented as part of POLS/CSSS 510. They are posted here for interested students, especially for the use of mosaic plots to investigate cross-tabulated data (in this lecture, and in the third lecture on multidimensional tables). Students interested in a CSSS course on log-linear models should investigate CSSS 536. Self-Study Lecture 2 Log-linear Models of Contingency Tables: 2D tables Self-Study Lecture 3 Log-linear Models of Contingency Tables: 3D+ tables Student Assignments Problem Set 1 Due in class Thursday 15 October 2015 Problem Set 2 Due in class Tuesday 27 October 2015 Problem Set 3 Due in class Thursday 5 November 2015 Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 4 Due in class Thursday 19 November 2015 Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 5 Due in class Tuesday 1 December 2015 Supplementary material: Data for problem 1 in comma-separated variable format; data for problem 2 in R data format. Problem Set 6 Due in class Thursday 10 December 2015 Supplementary material: Data will be provided. Poster Presentations 1 December 2015 to 10 December 2015 Requirments and suggestions for poster presentations will be presented in class. Final Paper Due Tuesday 15 December 2015, 3:00 pm, both in my Gowen mailbox and by email See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 R-Refresher Code. Here is a file about how to think about R: Thoughts on R . Lab 2 MLE Code. Lab 3 MLE 2 Lab 4 Ordered and Unordered Categorical Data Code. Lab 5 Ordered and Unordered Categorical Data Code. Lab 6 MLE 2 |

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