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| 09/04 - 12/20 | ||||||
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1:35 pm |
1:35 pm |
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Subject: Statistics (STAT)
CRN: 41513
Lecture
St Paul: O'Shaughnessy Science Hall 429
Requirements Met:
Writing in the Discipline
Applied linear regression models. Simple linear regression; introduction, inferences, diagnostics, remedial measures, simultaneous inference. Matrix approach in linear regression. Multiple regression; inference, remedial measures, extra sums of squares, partial determinations, standardized models, use of indicator and mixed variables, polynomial regression, model selection and validation, diagnostics, remedial measures, multicollinearity and effects, autocorrelation. Single and multi-factor analysis of variance: analysis of factor level means, interactions, inferences, diagnostics and remedial measures. A statistical package must be used as tool. Optional topics may include: logistic regression, design of experiments, and forecasting. Prerequisite: One of the following, STAT 201, STAT 220, STAT 333, MATH 303
4 Credits