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 actualversuspredicted 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, zeroinflated Poisson, and zeroinflated negative binomial models. The data for these examples comes either as a complete dataset, or a dataset with zerocount 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. SelfStudy Lecture 1 Introduction to Contingency Tables Supplementary material: This lecture and the two below it introduce loglinear 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 crosstabulated data (in this lecture, and in the third lecture on multidimensional tables). Students interested in a CSSS course on loglinear models should investigate CSSS 536. SelfStudy Lecture 2 Loglinear Models of Contingency Tables: 2D tables SelfStudy Lecture 3 Loglinear 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 Tuesday 28 October 2014 Supplementary material: Data for problem 1 in commaseparated variable format. Problem Set 4 Due in class Thursday 6 November 2014 Supplementary material: Data for problem 1 in commaseparated variable format. Problem Set 5 Due in class Thursday 13 November 2014 Supplementary material: Data for problem 1 in commaseparated 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
Lab 5 Ordered and Unordered Data Models
Lab 6 Count Data

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