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Course Criteria
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1.00 - 3.00 Credits
Student studies advanced topics under weekly faculty direction and writes a paper turned in to the Associate Dean. Topics may vary. Graded Credit/No Credit only. May be repeated for credit up to 6 hours total. ([1-3] -0) S
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3.00 Credits
Principles of quantitative decision making: summarizing data, modeling uncertainty, loss functions, probability, conditional probability, random variables. Introduction to statistics: estimation, confidence intervals, hypothesis testing, regression. Introduction to statistical packages. Cannot be used to satisfy degree requirements for majors in the School of Engineering and Computer Science, or major requirements in the Schools of Management or Natural Sciences and Mathematics. Prerequisite: MATH 1306, MATH 1314 or equivalent. (3-0) S
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1.00 - 3.00 Credits
An introduction to the use of statistics packages, such as SAS, BMD, SPSS, Minitab, and S, for the analysis of data. Based primarily on self-study materials. Cannot be used to satisfy degree requirements for mathematical science majors. Prerequisite: one semester of statistics. (1-0) S
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3.00 Credits
Graphs, histograms, mean, median, standard deviation, Chebyshev's inequality, standardized scores, simple linear regression and correlation; basic rules of Probability, Bayes theorem, Normal; t, ? 2, F, binomial and Poisson distributions; point estimation; hypothesis tests and confidence intervals for means, proportions regression coefficients, and correlation; one way ANOVA; contingency tables. Applications in life sciences will be emphasized throughout the course. Cannot be used by mathematical sciences, engineering, or computer science majors to satisfy degree requirements. Prerequisite: MATH 1325 or equivalent. (3-0) Y
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3.00 Credits
Methods of data analysis used in different areas of Statistics and Actuarial Science. Sampling, fitting and testing models, regression, and comparison of populations. A statistical computer package will be used. Prerequisite: MATH 2419. (3-0) T
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3.00 Credits
Probability theory including independence, conditioning, density functions, frequently used families of distributions, random variables, expectation, moments, and the central limit theorem; statistical inference including sampling, estimation, hypothesis testing, and regression. Cannot be used by mathematical sciences, engineering, or computer science majors to satisfy degree requirements. Prerequisite: MATH 1326. (3-0) S
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3.00 Credits
Probability models, random variables, expectation, special distributions, and the central limit theorem. The theory is illustrated by numerous examples. Prerequisite: MATH 2451. (3-0) T
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3.00 Credits
Theory and methods of statistical inference. Sampling, estimation, hypothesis testing, analysis of variance, and regression with examples from the physical, social, and management sciences. Prerequisite: STAT 4351 or equivalent. (3-0) T
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3.00 Credits
Probability models and statistical methods used in insurance business. Typical loss distributions including Pareto, Weibull, Iognormal, Ioggamma, discrete and continuous mixtures. Effect of coverage modifications, and clustering in modeling. Estimation by simulation. Prerequisite: STAT 4351. (3-0) T
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3.00 Credits
Stochastic models including Markov chains, random walks, Poisson processes, renewal processes, and an introduction to time series and forecasting. Prerequisite: STAT 4351 or equivalent. (3-0) T
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