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Course Criteria
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3.00 Credits
Prerequisite(s): (STAT 2040 or STAT 2050 and MATH 2210 each with a grade of C or higher) and University Advanced Standing. Introduces mathematical statistics for scientists and engineers. Includes counting techniques, random variables, expected values, joint and marginal distributions, point estimation, hypothesis testing, analysis of variance, and regression.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Provides students in non-mathematical disciplines the ability to answer typical research questions for their senior projects or graduate-level research. Includes linear regression, transformations, variable selection techniques, logistic regression, indicator variables, multicollinearity, and ARIMA time series. Satisfies the VEE statistics requirement for the Society of Actuaries. Introduces standard software as a tool for statistical analysis.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Introduces the design and analysis of randomized comparative experiments. Includes single factor ANOVAs, randomized block designs, latin squares, factorial designs, and nested and split plot designs. Covers mixed models including random effects and computation of expected mean squares to form appropriate F-ratios. Uses SAS statistical program software to perform statistical analysis.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Introduces survey sampling including simple random sampling, stratified random sampling, systematic and cluster sampling. Discusses ratio and difference estimators, weighting for non-responses, eliminating sources of bias and designing the questionnaire.
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3.00 Credits
Prerequisite(s): STAT 3040 or STAT 4710 with a grade of C or higher and University Advanced Standing. Teaches how to perform statistical inference on Markov chains, including classifying states, computing mean and variance of recurrence times, and investigating long-run limiting behavior to model physical systems uses the Poisson process. Teaches how to calculate and analyze queuing characteristics of each of the popular queuing models.
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3.00 Credits
Prerequisite(s): MATH 2270 and STAT 4710, both with a grade of C or higher, and University Advanced Standing. Introduces multivariate data analysis. Covers inference on data arising from the multivariate normal distribution using MANOVA, principal component analysis, factor analysis, canonical correlation analysis, discriminant analysis, and cluster analysis. Uses statistical software throughout.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Introduces nonparametric statistical procedures to apply in situations when parametric statistics (usually based on normality) are not appropriate. Covers types of nonparametric analyses that includes one and two sample hypothesis tests, goodness-of-fit tests, contingency tables, block designs, and regression analysis.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Presents the theory and methods of quality monitoring including process capability, control charts, acceptance sampling, quality engineering, and quality design.
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3.00 Credits
Prerequisite(s): STAT 2040 or STAT 2050 with a grade of C or higher and University Advanced Standing. Pre- or Corequisite(s): MATH 2210 or MATH 221H. Introduces mathematical statistics including random variables, set theory, transformations, expectation, joint and marginal distributions, moment generating functions, and order statistics.
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3.00 Credits
Prerequisite(s): STAT 4710 with a grade of C or higher and University Advanced Standing. Is a continuation of STAT 4710. Includes estimation, sufficiency, completeness, hypothesis testing, statistical inference with the normal distribution, and Bayesian statistics.
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