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Course Criteria
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3.00 Credits
Basic ideas of statistical quality control and improvement. Topics covered: Deming's 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces. Prerequisite: 345.
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3.00 Credits
Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies. Prerequisite: 345. {Alternate Falls}
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3.00 Credits
A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan- Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models. Prerequisite: 428 or 440.
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3.00 Credits
Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling. Prerequisite: 428 or 440. {Offered upon demand}
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3.00 Credits
An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes' theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis. Prerequisite: 461 and (427 or 440). {Alternate Springs}.
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3.00 Credits
Modern topics not covered in regular course offerings.
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3.00 Credits
Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control. Prerequisite: 461. {Alternate Springs}
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3.00 Credits
Guided study, under the supervision of a faculty member, of selected topics not covered in regular course offerings.
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3.00 Credits
A detailed introduction to the SAS programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS software will be used. Prerequisite: 345, 427.
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3.00 Credits
Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab and SAS . Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics. Prerequisite: 145. {Fall}
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