604.  Special computer algorithms for programming; statistical analysis, efficient uses of existing statistical computer programs, generation of random numbers and statistical variables, programming of simulation studies. Prerequisite: MATH 221 or 251 or 253

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

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

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.  Biostatistics I. (3-0). Credit 3. Bio-assay for quantitative and quantal responses; statistical analysis of contingency, including effect estimates, matched samples and misclassification. Prerequisites: STAT 608, STAT642 and STAT 659.

644.  Biostatistics II. (3-0). Credit 3. Generalized linear models; survival analysis with emphasis on semi-parametric models and methods. 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 652.

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 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.  Professional Internship. Credit 1 to 3. Practicum in statistical consulting for students in Ph.D. program. Students will be assigned consulting problems brought to the Department of Statistics by researchers in other disciplines. Prerequisite: STAT 641 and 642.

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.  Special Topics in Statistics. Credit 1 to 4. Selected topics in an identified area of statistics. Open to non-majors. May be repeated for credit. Prerequisite: Approval of instructor.