2005 ICPSR SUMMER PROGRAM REGRESSION ANALYSIS III: ADVANCED METHODS

This course will take a modern, data-analytic approach to the multiple regression model. Our coverage of the material will emphasize the ways that graphical tools can augment traditional methods for describing how the conditional distribution of a dependent variable changes along with the values of one or more independent variables. The course will examine the basic nature and assumptions of the linear regression model, diagnostic tools for detecting violations of the regression assumptions, and strategies for dealing with situations in which the basic assumptions are violated. The overall goal is to provide: (1) New insights about regression analysis; (2) a general overview of various modern extensions to the traditional linear model; and (3) innovative, eective methods for presenting the results from statistical investigations of empirical data. Specific topics to be covered include: data visualization and transformation; assumptions of the linear model; regression diagnostics and model assessment; robust and resistant regression; weighted least squares; generalized linear models, resampling methods; nonlinear regression; nonparametric regression; generalized additive models; and graphical regression.

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