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STAT 774. Generalized Linear Models. 3 Credits.

This course introduces the statistical theory and inference of generalized linear models (GLMs) which deals the cases that the normality of response data is in absence. The course starts from a review of linear regression with matrix approach. The topic includes exponential distribution family, link functions, contingency tables, GLMs, quasi-GLMs, deviance, residuals, model selection and diagnostics. Students are expected to be able to apply GLMs technique to deal with real world problems in diverse areas. Prereq: STAT 768.


Program Description The Department of Statistics offers programs leading to a Ph.D. in statistics, a M.S. degree in applied statistics. The program is flexible enough to be individually planned around prior experience and in accord with professional goals.