601.  Statistical Analysis. (3-2) Credit 4. For students in engineering, physical and mathematical sciences. Introduction to probability, probability distributions and statistical inference; hypotheses testing; introduction to methods of analysis such as tests of independence, regression, analysis of variance with some consideration of planned experimentation. Prerequisite: Two semesters of calculus. Multiple integration and elementary linear algebra are helpful.
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604.  Topics in Statistical Computations. (3-0). Credit 3. Efficient uses of existing statistical computer programs (SAS, R, etc.); generation of random numbers; using and creating functions and subroutines; statistical graphics; programming of simulation studies; and data management issues.
Summer 2009 Syllabus

607.  Sampling. (3-0). Credit 3. Planning, execution, and analysis of sampling from finite populations; simple, stratified, multistage, and systematic sampling; ratio estimates. Prerequisite: STAT 601 or 652 or concurrent enrollment in STAT 641.
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608. Regression Analysis. (3-0). Credit 3. Multiple, curvilinear, nonlinear, robust, logistic and principal components regression analysis; regression diagnostics, transformations, analysis of covariance. Prerequisite: STAT 601 or 641.
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626.  Methods in Time Series Analysis. (3-0). Credit 3. Introduction to statistical time series analysis; autocorrelation and spectral characteristics of univariate, autoregressive, moving average models; identification, estimation and forecasting. Prerequisite: STAT 601 or 642 and a working knowledge of complex numbers and trigonometry.
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630.  Overview of Mathematical Statistics. (3-0). Credit 3. Basic probability theory including distributions of random variables and their expectations. Introduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratio tests. Introduction to Bayesian methods. Prerequisite: Three semesters of calculus, including multiple integration and a basic understanding of limits.
Syllabus  Textbook

636.  Methods in Multivariate Analysis. (3-0). Credit 3. Multivariate extensions of the chi-square and t-tests, discrimination and classification procedures. Applications to diagnostic problems in biological, medical, anthropological, and social research; multivariate analysis of variance, principal component and factor analysis, canonical correlations. Prerequisites: STAT 642 or 652 (Applied Regression and Analysis of Variance Methods).
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641.  The Methods of Statistics I. (3-0). Credit 3. An application of the various disciplines in statistics to data analysis, introduction to statistical software; demonstration of interplay between probability models and statistical inference. Prerequisites: Two semesters of Calculus, STAT 604, and STAT 630 or concurrent enrollment in STAT 630.
Syllabus  Textbook

642.  The Methods of Statistics II. (3-0). Credit 3. Design and analysis of experiments; scientific method; graphical displays; analysis of non-conventional designs and experiments involving categorical data. Prerequisites: STAT 641.
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651.  Statistics in Research I. (3-0). Credit 3. For graduate students in other disciplines. A non-calculus exposition of the concepts, methods, and usage of statistical data analysis. T-tests, analysis of variance, and linear regression. Prerequisite: MATH 102 or equivalent.
Syllabus Textbook

652.  Statistics in Research II. (3-0). Credit 3. Continuation of STAT 651. Concepts of experimental design, individual treatment comparisons, randomized blocks and factorial analysis, multiple regression, chi-square tests and a brief introduction to covariance, non-parametric methods, and sample surveys. Prerequisite: STAT 651.
Syllabus Textbook

653.  Statistics in Research III. (3-0). Credit 3. Currently listed as STAT 689. The analysis of messy and complex data sets using analysis of variance, analysis of covariance and regression analysis. Transformations; regression diagnostics; nonlinear, robust, logistic and principal components regression; structural equations. Prerequisite: STAT 642 or 652.
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657.  Advanced Programming Using SAS (3-0). Credit 3. Programming with SAS/IML, programming in SAS Data Step, advanced use of various SAS procedures. Prerequisite: STAT 604 and STAT 630 or 652.
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659. Applied Categorical Data Analysis. (3-0). Credit 3. Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as SAS, GLIM, SPSSX. Prerequisite: STAT 601 or 641 or 652 (Methods, multiple regression, analysis of variance including factorial experiments).
Syllabus  Textbook

667.  Statistics for Advanced Placement Teachers. Credit 1-3. Review of the fundamental concepts and techniques of statistics; topics included in Advanced Placement Statistics; exploring data, planning surveys and experiments, exploring models, statistical inference. Prerequisite: Approval of instructor.

681.  Seminar. Credit 1. Oral presentations of special topics and current research in statistics. Prerequisite: Graduate classification in statistics.

684 Sections 700 and 720. Consulting I. Credit 1. Practicum in statistical consulting. Students will watch practice sessions and read relevant papers. Prerequisite: STAT 641 and 642.

684 Sections 701 and 721. Consulting II. Credit 1. Practicum in statistical consulting. Students will be assigned consulting problems brought to the Department of Statistics by researchers in other disciplines. Prerequisite: STAT 608, 641, 642, 659 and 684 I.

685. Directed Studies. Credit 1 to 6. Individual instruction in selected fields in statistics; investigation of special topics not within scope of thesis research and not covered by other formal courses. Prerequisites: Graduate classification; approval of instructor.

689 Bayes. Introduction to Applied Bayesian Methods. (3-0). Credit 3. The objective of this applied master's level course is to introduce students to the Bayesian paradigm for data analysis. Students learn how uncertainty regarding parameters can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution. Students are exposed to foundational principles, but the course emphasizes modeling and computations under the Bayesian paradigm.. Prerequisites: STAT 604, STAT 608 and STAT 630.
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