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SPN 689: Graduate Seminar
1.00 Credits
Miami University-Hamilton
Introduces new teaching assistants to strategies for teaching listening, speaking, reading, and writing skills as well as culture in a cross-language setting. Summer only.
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SPN 689 - Graduate Seminar
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SPN 700: Research for Master's Thesis
3.00 Credits
Miami University-Hamilton
Research for Master's Thesis
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SPN 700 - Research for Master's Thesis
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STA 401 /501: Probability
3.00 Credits
Miami University-Hamilton
Development of probability theory with emphasis on how probability relates to statistical inference. Topics include review of probability basics, counting rules, Bayes Theorem, distribution function, expectation and variance of random variables and functions of random variables, moment generating function, moments, probability models for special random variables, joint distributions, maximum likelihood estimation, unbiasedness, distributions of functions of random variables, chi-square distribution, students t distribution, F distribution, and sampling distributions of the sample mean and variance. Prerequisite: STA 261, 301, or 368 or equivalent. Pre- or Corequisite Calculus II. Note: STA 501 may not be counted toward graduate degree programs in mathematics or statistics.
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STA 401 /501 - Probability
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STA 402 /502: Statistical Programming
3.00 Credits
Miami University-Hamilton
Introduction to the use of computers to process and analyze data. Techniques and strategies for managing, manipulating, and analyzing data are discussed. Emphasis is on the use of the SAS system. Statistical computing topics, such as random number generation, randomization tests, and Monte Carlo simulation, will be used to illustrate these programming ideas. Prerequisite: STA 401/501 or STA 671 or DSC 305 or permission of instructor.
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STA 402 /502 - Statistical Programming
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STA 462 /562: Inferential Statistics
3.00 Credits
Miami University-Hamilton
A study of estimation and hypothesis testing including a development of related probability ideas. Topics include derivation of the distribution of functions of random variables, point estimation methods, properties of point estimators, derivation of confidence interval formulas, and derivation of test statistics and critical regions for testing hypotheses. Prerequisite: STA 401/501 and Calculus III.
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STA 462 /562 - Inferential Statistics
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STA 463 /563: Regression Analysis
4.00 Credits
Miami University-Hamilton
Linear regression model, theory of least squares, statistical inference procedures, general linear hypothesis, partial F tests, residual analysis, regression diagnostics, comparison of several regressions, model adequacy, and use of statistical computer packages. Prerequisite: STA 401/501 and MTH 222 or 231.
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STA 463 /563 - Regression Analysis
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STA 466 /566: Experimental Design Methods
4.00 Credits
Miami University-Hamilton
Experimental design concepts; completely randomized, randomized block, and Latin square designs; planned and multiple comparisons; analysis of variance and covariance; factorial and split-plot experiments; nested designs and variance components; fixed, random, and mixed effects models. Emphasis on applications and computer usage. Prerequisite: STA 463/563 or DSC 305.
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STA 466 /566 - Experimental Design Methods
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STA 467 /567: Multivariate Analysis
3.00 Credits
Miami University-Hamilton
Multivariate normal distribution, partial and multiple correlations, Hotelling's T-squared, estimation and tests of hypotheses for multivariate populations. Prerequisite: STA 401/501 and MTH 222.
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STA 467 /567 - Multivariate Analysis
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STA 471 /571: Probability and Statistics Problems Seminar
1.00 Credits
Miami University-Hamilton
Solution and discussion of challenging probability problems such as those found on the first actuarial exam. Prerequisite: STA 401/501.
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STA 471 /571 - Probability and Statistics Problems Seminar
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STA 473 /573: Applied Multiple Regression
1.00 Credits
Miami University-Hamilton
Service course. Linear regression model and assumptions, statistical inferences associated with regression, multiple correlation, curvilinear regression, selection of 'best' regression function, regression approach to single-factor analysis of variance. Extensive use of computer library programs. Offered in five-week sprint mode. Prerequisite: previous course in statistics.
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STA 473 /573 - Applied Multiple Regression
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