DATA467

040667

An applied course in linear regression, analysis of variance, and generalized linear models for students who have completed a course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation, using statistical software such as R. Course content includes model building; linear regression; regression and residual diagnostics; basic experimental designs such as one-factor and two-factor ANOVA; block designs and random-effects models; introduction to exponential families and generalized linear models, including logistic and Poisson regression. Some emphasis will be devoted to matrix representations and efficient computational techniques.

GRD - Regular Grades A, B, C, D, E

Undergraduate

017877