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Course Criteria
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3.00 Credits
Introduction to statistical methodology with emphasis on engineering experimentation: probability distributions, estimation, hypothesis testing, regression, and analysis of variance. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. Pre: MATH 2224. (2H,2 Credits) I,II.
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3.00 Credits
Computationally intensive computer methods used in statistical analyses. Statistical univariate and multivariate graphics; resampling methods including bootstrap estimation and hypothesis testing and simulations; classification and regression trees; scatterplot smoothing and splines. Pre: (4105, 4214). (4H,3 Credits)
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2.00 Credits
Specialized tools for design and analysis applicable to current interdisciplinary statistical consulting projects. Oral and written communication skills important to effective client-statistician interactions, including interview, report-writing, and oral presentation skills. Pre: 3006, 4204. (2H,2 Credits)
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3.00 Credits
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. I Pre: MATH 2224. (3H,3 Credits)
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3.00 Credits
Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and Latin square designs, analysis of covariance, split--plot designs, factorial and fractional designs, incomplete block designs. Project. Knowledge of WIN/MAC required. I Pre: 3006 or 3616 or 4106 or 4706 or 5605 or 5615. (3H,3 Credits)
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3.00 Credits
Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS. Project. Knowledge of WIN/MAC required. I Pre: 3006 or 3616 or 4106 or 4706 or 5606 or 5616. (3H,3 Credits)
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3.00 Credits
Introduction to Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression. Pre: MATH 2224. (3H,3 Credits)
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3.00 Credits
Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Use of SAS. Project. Knowledge of WIN/MAC required, even years. II Pre: 3006 or 4706 or 5606 or 5616. (3H,3 Credits)
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3.00 Credits
Statistical techniques for frequency data. Goodness-of-fit. Tests and measures of association for two-way tables. Log-linear models for multidimensional tables. Parameter estimation, model selection, incomplete tables, ordinal categories, logistic regression. Use of SAS and SPSSx. Project. Knowledge of WIN/MAC required, even years. II. Pre: 3006 or 3616 or 4106 or 4706 or 5606 or 5616. (3H,3 Credits)
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3.00 Credits
An applied course in time series analysis. A uniform coverage of both time domain and frequency domain methods that are used in the physical, biological, and social sciences and by applied statisticians. WIN/MAC. Odd years. II Pre: (3006 or 4106 or 4706 or 4714 or 5606 or 5616), (MATH 1206). (3H,3 Credits)
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