Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. Topic 1 Introduction to the Course and to R R code and data for the GDP example. R code and data from the fertility example. Topic 2 Review of Matrix Algebra for Regression We will work through Kevin Quinn’s matrix algebra review (pdf). For a general review of the basic math we’ll need in the course, you can find the lecture notes for the CSSS Math Camp here. Topic 3 Linear Regression in Matrix Form and You may find useful three review lectures on basic probability theory, discrete distributions, and continuous distributions. Topic 4 Inference and Interpretation of Linear Regression Example code for estimating a linear regression, extracting confidence intervals for the parameters, and plotting fitted values with a confidence envelope. Topic 5 Specification and Fitting in Linear Regression Topic 6 Outliers and Robust Regression Techniques Student Assignments Due Tuesday, 15 April, in class Data for problem 1 in comma-separated variable format. Due Friday, 25 April, in section Data for problem 1 in comma-separated variable format. Due Tuesday, 6 May, in class Five R script templates for simulation of the performance of linear regression with different kinds of data: when the Gauss-Markov assumptions apply; when there is an omitted variable; when there is selection on the response variable; when there is heteroskedasticity; and when there is autocorrelation in the response variable. Due Tuesday, 20 May, in class Data for problem 2 in comma-separated variable format. Due Friday, 6 June, in section Data for problems 1. Data for problem 2. Data for problem 3. (All data in comma-separated variable format.) Final Paper Due Monday, 9 June, at 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 Introduction to R Lab 1 code and a page of links to R and LaTeX Resources. Lab 2 Beginning with data: reading in, exploring, and even regressing Lab 2 code and data for Example 1 and Example 2. Lab 3 More practice with regression and plotting Lab 3 code. Data is the same rossoil data as Example 1 in Lab 2. Lab 4 Useful things, largely loops and simulation Lab 4 code. Data for the first set of examples. Data for the second (model simulation) example. Lab 5 More on models and interpretation, simulation in detail Lab 5 code. Data for the first set of examples. It's the same rossoil data we've been using for weeks. Lab 6 Miscellaneous, transformation, and heteroskedasticity Lab 6 code. This will use two datasets: the same rossoil data we've been using and the democracy data from earlier. Lab 6+ Intro to LaTeX workshop Here is the tex file and pdf. Please also download the duck. Lab 7 Intro to logit and functions (and a bit of outliers) Lab 7 code. This will use two datasets: the same rossoil data we've been using and NES data for logit examples. Lab 8 Intro to simcf/tile (including for logit) Here is Lab 8 code. This lab will use the same datasets as last week's lab: the rossoil and NES data. We will also access the data for HW4 online in the script. Lab 8 Very brief intro to useful functions like apply() and plyr package Here is Lab 9 code. All data will be called within script. |

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