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: There are additional slides on the Monty Hall problem in color and printable pdf formats. 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 actual-versus-predicted plots, which you should place in your working directory. Topic 4 Models of Ordered Data Supplementary material: R code and data for an ordered probit, which produces this output. Topic 5 Models of Nominal Data 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 9 October 2014 Problem Set 2 Due in class Thursday 16 October 2014 Problem Set 3 Due in class Thursday 30 October 2014 Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 4 Due in class Thursday 6 November 2014 Supplementary material: Data for problem 1 in comma-separated variable format. Problem Set 5 Due in class Thursday 13 November 2014 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 4 December 2014 Supplementary material: Data will be provided. Poster Presentations 20 November 2014 to 4 December 2014 Requirments and suggestions for poster presentations will be presented in class. Final Paper Due Tuesday 9 December 2014, 3:00 pm, in my Gowen mailbox See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 R and LaTeX refreshers For R Refresher you can download the slides (pdf printable version), the R code we will go through, and the csv dataset. For Latex introduction/refresher, you may want the slides and a Rnw script you can adapt and reuse. Lab 2 Heteroskedastic Normal Models You can download the slides and the R code Lab 3 Binary Data Models You can download the slides, the R code, and the csv dataset Lab 4 Goodness of Fit for Binary Data Models You can download the slides, the R code, and the csv dataset, as well as Chris' code for Actual Versus Predicted plots Lab 5 Ordered and Unordered Data Models You can download the slides, the R code Lab 6 Count Data You can download the slides, the R code, and the csv dataset Lab 7 Multilevel Data You can download the R code |

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