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Course Criteria
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3.00 Credits
(221) Either semester. Three credits each semester. Prerequisite: MATH 1122 or 1132 or 1152. Basic probability distributions, point and interval estimation, tests of hypotheses, correlation and regression, analysis of variance, experimental design, non-parametric procedures.
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3.00 Credits
(242Q) Either semester. Three credits. Prerequisite: STAT 2215 or 3025 or instructor consent. Credit may not be received for both STAT 3115 and 5315. Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression.
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3.00 Credits
(224Q) Either semester. Three credits. Prerequisite: MATH 2110 or 2130. Students may not receive more than three credits from STAT 3025 and STAT 3345 or from STAT 3345 and STAT 3375. Probability set functions, random variables, expectations, moment generating functions, discrete and continuous random variables, joint and conditional distributions, multinomial distribution, bivariate normal distribution, functions of random variables, central limit theorems, computer simulation of probability models.
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3.00 Credits
(230Q) Both semesters. Three credits. Prerequisite: MATH 2110 or 2130. Students may not receive credit for both STAT 3345 and STAT 3375, or both STAT 3375 and STAT 5585. The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman- Pearson theory of hypothesis testing, correlation, regression, analysis of variance.
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3.00 Credits
(231) Both semesters. Three credits. Prerequisite: STAT 3375Q. Students may not receive credit for both STAT 3445 and STAT 5685. The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman- Pearson theory of hypothesis testing, correlation, regression, analysis of variance.
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1.00 Credits
(200) Either semester. One credit. Prerequisite: STAT 2215 or 3115; and STAT 3025 or 3375. The student will attend 6-8 seminars per semester, and choose one statistical topic to investigate in detail. The student will write a well-revised, comprehensive paper on this topic, including a literature review, description of technical details, and a summary and discussion.
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1.00 Credits
(202W) Either semester. One credit. Prerequisite: STAT 2215 or 3115; and STAT 3025 or 3375, and STAT 3484; ENGL 1010 or 1011 or 3800. The student will attend 6-8 seminars per semester, and choose one statistical topic to investigate in detail. The student will write a well revised comprehensive paper on this topic, including a literature review, description of technical details, and a summary and discussion, building upon the writing experience in STAT 3484.
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3.00 Credits
(243Q) Second semester. Three credits. Prerequisite: STAT 2215 or 3025 or instructor consent. Credit may not be received for both STAT 3515 and 5515. Methods of designing experiments utilizing regression analysis and the analysis of variance.
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4.00 Credits
(261QC) Second semester. Four credits. Prerequisite: STAT 3025 or STAT 3375. Recommended preparation: An applied statistics course. Open only with consent of instructor. Introduction to computing for statistical problems; obtaining features of distributions, fitting models and implementing inference (obtaining confidence intervals and running hypothesis tests); simulationbased approaches and basic numerical methods. One hour per week devoted to computing and programming skills.
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3.00 Credits
(235) (Also offered as MATH 3170.) Either semester. Three credits. Prerequisite: STAT 3025 or 3345 or 3375 or MATH 3160 Not open for credit to students who have passed MATH 3170. Conditional distributions, discrete and continuous time Markov chains, limit theorems for Markov chains, random walks, Poisson processes, compound and marked Poisson processes, and Brownian motion. Selected applications from actuarial science, biology, engineering, or finance.
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