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. |

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