Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. Topic 1 Introduction to the Course, Probability, and R 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 The R code to simulate heteroskedastic data and model that data using a heteroskedastic normal maximum likelihood is here. Topic 3 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 and ROC plots, which you should place in your working directory. An example trio of plots showing actual versus predicted probabilities, error versus predicted probabilities, and the ROC curve can be seen here. Topic 4 R code and data for an ordered probit, which produces graphics for expected value plots and first difference plots. Topic 5 R code for a multinomial logit, which produces a variety of graphical summaries of a multinomial logit model: for expected values plotted together, expected values plotted separately in a tiled format, first difference plotted for a single scenario and all categories, relative risks plotted for a single scenario and all categories, and relative risks plotted for many scenarios at once. Topic 6 Two code examples are discussed in this lecture. - The main R script to run the models, cross-validation, and graphics
- Data (csv format) from the Washington Secretary of State & US Census
- An R helper file with cross-validation functions
The second example analyzes unbounded counts using Poisson, Negative Binomial, Quasipoisson, Zero-inflated Poisson, and Zero-inflated Negative Binomial models of foreclosure filings by Houston, Texas area Home Owner Associations (HOAs). Example output includes this plot of expected values from a zero-inflated negative binomial model. You will need: - The main R script to run the models and graphics
- Data (csv format) from HOAdata.org
Advanced Topic 1 Missing Data and Multiple Imputation See the Topic 6 example on turnout for an R code using multiple imputation of missing data. Also available is an example (R script, data, plot) showing the use of overimputation to compute coverage of multiple imputation prediction intervals for real data. Advanced Topic 2 Introduction to Multilevel Models 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 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 Due in class Thursday 15 October 2020 Due in class Tuesday 27 October 2020 Due in class Tuesday 10 November 2020 Data for problem 1 in comma-separated variable format. Due in class Thursday 19 November 2020 Data for problem 1 in comma-separated variable format. Due in class Tuesday 3 December 2020 Data for problem 1 in comma-separated variable format; data for problem 2 in R data format. Poster Presentations 8 December 2020 to 10 December 2020 Requirements and suggestions for poster presentations will be presented in class. Final Paper Due Tuesday 15 December 2020, 3:00 pm by email See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 Lab 2 Distributions and R Practice Lab 3 OLS and MLE for heteroskedastic normal Lab 4 OLS and MLE for heteroskedastic normal (cont.) Lab 5 Estimating and Interpreting Logit Models |

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