The University of Tennessee                                          
Department of Statistics, Operations, and Management Science  

 Statistics Graduate Courses

Offered via Distance Education

The UTK Department of Statistics offers several graduate-level courses which may be taken via distance mode, using UT Outreach & Continuing Education’s award-winning Web-based Cyberclass technology. 

Cyberclasses are live, synchronous classes. Students follow course material as the teacher lectures from and annotates Power Point slide presentations. Cyberclasses employ two-way audio and encourage student  participation. Statistical software can be demonstrated live, and sessions are recorded for playback at the student's convenience.  For additional information about the Cyberclass technology, please see http://anywhere.tennessee.edu/de/tech.

All distance courses are taught by the regular faculty of the Department of Statistics, and content is identical to that delivered in on-campus versions of the courses.  All distance courses require admission to The Graduate School and carry 3 hours of graduate credit.

Distance courses may be used to earn the Graduate Certificate in Applied Statistical Strategies.  Please see www.bus.utk.edu/stat/certificate for information regarding the Certificate program.

Distance Courses Currently Offered

	For those with limited background in math, stat, and  computing
	Statistics 531: Survey of Statistical Methods I  Univariate and bivariate data collection and organization, statistical estimation and hypothesis testing; analysis of relationships for categorical and numerical data, including Chi-square tests and simple linear and quadratic regression. Use of computing facilities required (SPSS statistical software). Prereq: 1 yr. college mathematics.
	Statistics 532: Survey of Statistical Methods II Multiple linear regression, including use of dummy variables; single and multiple factor analysis of variance and covariance; issues in experimental design and analysis.  Use of computing facilities required.  Prereq: 531.
	For more technically oriented students
	Statistics 571: Statistical Methods Applied statistical methods: estimation, tests of hypotheses, analysis of variance, nonparametric methods. Prereq: One year of calculus and one course in statistics. Uses JMP statistical software.
	Statistics 572: Applied Regression Analysis Simple linear regression. Matrix approach to multiple linear regression. Partial and sequential sums of squares, interaction and confounding, use of dummy variables, model selection. Leverage, influence and collinearity. Autocorrelated errors. Generalized linear models, maximum likelihood estimation, logistic regression, analysis of deviance. Nonlinear models, inference, ill-conditioning. Robust regression, M-estimators, iteratively reweighted least squares. Nonparametric regression, kernel, splines, testing lack of fit. Prereq: 571 and matrix algebra.
	Statistics 573: Design of Experiments One-way ANOVA, multiple range tests, equal and unequal variances, transformations; factorial experiments, completely randomized designs, analysis of covariance, split-plot and nested designs, fractional factorials, sequential designs. Prereq: 571.
	Stat 574:  Data Mining Understanding and application of data mining methods. Data preparation; exploratory data analysis and visualization; cluster analysis; logistic regression; decision trees; neural networks; association rules; model assessment; and other topics. Applications to real world data. Use of standard computer packages. Prereq: Stat 532 or Stat 538 or Stat 571, or consent of instructor.
	Stat 567: Analysis of Lifetime Data  Statistical analysis of lifetime data. Methods of analysis for complete and censored data. Lifetime data regression. Planning and analysis of life tests. Prereq: 563 or Mathematics 425 or consent of instructor.
	Stat 579: Applied Multivariate Methods Multivariate techniques: Hotelling's T-sq., MANOVA, discriminant analysis, canonical correlation, principal component analysis, and factor analysis.  Computer oriented approach: analysis and interpretation.  Knowledge of basic matrices and SAS essential.  Prereq: 538 or knowledge of regression and analysis of variance.