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Course Criteria
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3.00 Credits
Regression analysis including simple linear regression, multiple regression, model checking and analysis of residuals, correlation and prediction, analysis of variance, completely randomized designs, randomized block designs, factorial designs, interaction and covariance analysis.
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3.00 Credits
Basic statistical analysis for students in quantitative disciplines other than statistics. Topics include principles of sampling and descriptive statistics, elementary probability and probability distributions, discrete and continuous random variables, normal distribution, sampling distributions, statistical inference for one and two samples, simple linear regression, basic nonparametrics, and chi-squared tests.
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3.00 Credits
Linear regression, analysis of variance, and related methodology for students in quantitative disciplines other than statistics. Topics include multiple regression; associated estimation and inference methods; model building, selection, and diagnostics; the analysis of variance; completely randomized and block designs; the analysis of covariance, and relevant statistical computing packages.
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3.00 Credits
Topics include multiple linear regression via its matrix representation, least squares estimation, methods of inference, model building and selection, regression diagnostics, completely randomized and block designs, Latin square designs, orthogonal arrays, factorial designs, logistic regression for binary data, and relevant statistical computing packages.
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3.00 Credits
Statistical logic and proofs, convergence, expectations, and matrix manipulations. Topics of this course are chosen to strengthen beginning statistics graduate students' analytical skills.
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3.00 Credits
Probability laws, distribution functions and expectations, random variables and statistical distributions, joint distributions and conditional expectations, order statistics, Markov chains, Poisson process, Brownian motion, renewal theory, queueing theory.
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3.00 Credits
Principles of data reduction, completeness, ancillarity. Point estimation; methods of estimation. Evaluation of estimators; C-R lower bound, efficiency, hypothesis testing: N-P tests, UMP tests, interval estimation; coverage probabilities and confidence sets.
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1.00 - 9.00 Credits
Research while enrolled for a master's degree under the direction of faculty members.
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1.00 - 9.00 Credits
Thesis writing under the direction of the major professor.
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1.00 - 5.00 Credits
Provides graduate teaching assistants with knowledge of pedagogical approaches and available support systems for teaching statistics courses. Special sections are reserved for international students, with focus on use of language, pedagogy, and cultural aspects of teaching in this country.
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