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Course Criteria
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4.00 Credits
Distribution free estimation and hypothesis testing procedures. Includes methods for use in one and two sample location and dispersion problems, nonparametric alternatives to ANOVA and regression, goodness of fit tests, measures of association, and tests for randomness. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 466
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4.00 Credits
Stochastic models for discrete time series in the time domain, moving average processes, autoregressive processes, model identification, parameter estimation, and forecasting. Statistical computing software packages are used. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 361 or STT 561
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4.00 Credits
Statistical process control for attributes and variables data: probability distributions, sampling plans, control charts, statistical control, process capability, process improvement, tolerance intervals, evolutionary operation, and applications. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 361 or STT 363
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4.00 Credits
Censoring and truncation, survival and hazard functions, estimation and hypothesis tests, Cox proportional hazards model, diagnostics of the Cox model; state of the art software for survival analysis models. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 361
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4.00 Credits
Stochastic concept of a queuing process is developed. Theories and applications of single and many server queues are presented. Emphasis on applications in engineering and computer science. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 360 or STT 363
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4.00 Credits
The statistical methods suitable for analysis of data arising in biological and related studies. Estimation and hypothesis testing are reviewed. Methods include one and two sample tests, simple and multiple regression, and analysis of variance. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Restrictions: May not be enrolled in one of the following Majors: Mathematics Applied Statistics Prerequisites: STT 265
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4.00 Credits
Probability, random variables, density and distribution functions, expectation, moment generating functions, special discrete and continuous distributions; joint, marginal and conditional distributions; independence, properties of expected values, functions of random variables. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 360 and MTH 232
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4.00 Credits
Limiting distributions, central limit theorem, statistics and sampling distribution point estimation, properties of estimators, sufficiency and completeness, interval estimation, hypothesis testing, most powerful and UMP tests, likelihood ration tests. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Prerequisites: STT 361 and STT 461
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4.00 Credits
Bootstrapping is a computing intensivemethod of data analysis by computing distributions. The method, including permutation tests, can be adapted easily to many classical problems. Doftware used for the course includes SPLUS and Mathematica. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Restrictions: May not be enrolled in one of the following Majors: Mathematics Applied Statistics Prerequisites: Undergraduate level STT 360 Minimum Grade of B and Undergraduate level STT 361 Minimum Grade of B
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4.00 Credits
Classical statistical techniques for analysis and interpretation of research data including the use of statistical software packages. Includes descriptive statistics, one and two sample inferences, regression and correlation analysis. Credit Hours: 4.000 Levels: Undergraduate Schedule Types: Lecture Corequisites: STT 466W Prerequisites: ( MTH 253 or MTH 255) and ( STT 265 or STT 361)
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