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: MATH 152 or 172
Syllabus

604.  Special Problems in Statistical Computations and Analysis in R and SAS. (3-0). Credit 3. Computer algorithms for programming; statistical analysis, efficient uses of existing statistical computer programs, generation of random numbers and statistical variables, programming of simulation studies, selected topics in statistical analysis not covered in STAT 601.
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.
Syllabus  Textbook

608.  Least Squares and 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.
Syllabus  Textbook

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 or approval of instructor.

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: MATH 221 or 251 or 253.
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: MATH 423, STAT 642 or 652.
Syllabus  Textbook

641.  Statistical Methods 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 MATH 221 or Math 251 or Math 253.
Syllabus  Textbook

642.  Statistical Methods 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.
Syllabus

643 (689). Special Topics in Biostatistics and Data Analysis I. (3-0). Credit 3. This course will present a survey of many important topics in biostatistics, including correlated data analysis, survival analysis, and clinical trials, and epidemiology. The goal is for each student to gain an understanding of the statistical issues underlying these topics, as well as existing methods and their use in R. This will be a Masters-level course, suitable for students in their second year of studies. Students from disciplines other than statistics are encouraged to enroll. Whereas the existing two-semester STAT 643-644 biostatistics sequence focuses on the mathematical details of biostatistics methods, this course will be geared toward preparing Masters-level statisticians for real-world data analysis.Prerequisites: STAT 651, STAT652 and STAT 659.
Syllabus

644 (689).  Statistical Bioinformatics. (3-0). Credit 3. This course will present an overview of the biological concepts, technologies, and statistical callenges of high-throughput genomics / proteomics. Prerequisites: STAT 643.

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.

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.

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.
Syllabus

656.  Data Mining Using SAS Enterprise Miner. Credit 3. This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). Prerequisite: Knowledge of multiple linear regression and logistic regression. 
Syllabus

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 642 or 652, and Stat 604.
Syllabus

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 or equivalent.
Syllabus  Textbook

667.  Statistics for Advanced Placement Teachers. (3-0). Credit 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. Professional Internship. 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.Professional Internship. 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 and 659.

685.  Problems. 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. Biostatistics and Data Analysis. (3-0). Credit 3. This course will present a survey of many important topics in biostatistics, including correlated data analysis, survival analysis, and clinical trials, and epidemiology. The goal is for each student to gain an understanding of the statistical issues underlying these topics, as well as existing methods and their use in R. This will be a Masters-level course, suitable for students in their second year of studies. Students from disciplines other than statistics are encouraged to enroll. Whereas the existing two-semester STAT 643-644 biostatistics sequence focuses on the mathematical details of biostatistics methods, this course will be geared toward preparing Masters-level statisticians for real-world data analysis.Prerequisites: STAT 651, STAT 652 and STAT 659.

689. Statistical Bioinformatics. (3-0). Credit 3. This course will present an overview of the biological concepts, technologies, and statistical challenges of high-throughput genomics / proteomics. Course material will be drawn from journal articles, and students will be required to analyze published datasets.Prerequisites: STAT 689 Bio I.