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Intermediate Quantitative Methods: The General Linear Model

This course is designed to meet the data analytic needs of the doctoral students whose dissertation relies on the analysis of quantitative data. Procedures important to the data analyst are covered including data entry and definition, treating missing data, detecting outliers, and transforming distributions. First term topics include multiple regression, analysis of covariance, repeated measures analysis of variance, and multivariate analysis of variance and covariance. Second term topics emphasize categorical data analysis, odds, rations, standardization, log linear models, logistic regression. Other topics include multinominal logistic models, survival analysis, principle components, and factor analysis. The approach is conceptual with heavy reliance on computer software packages. Appropriate for doctoral students desiring specialized knowledge beyond the introductory sequence.

Course #
Applied Statistics, Social Science, and Humanities