4.0

credits

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This course provides an accessible but in-depth coverage of multiple regression with a focus on sociological problems and software applications. We begin with the basics of linear regression, including estimation, statistical inference, and model assumptions. We then review several tools for diagnosing violations of statistical assumptions and what to do when things go wrong, including dealing with outliers, missing data, omitted variables, and weights. Graduate students should have completed AS.230.600 or equivalent. Undergraduates admitted with instructor's permission and AS.230.205 or equivalent.