500
Introduction to statistical programming for managing and analyzing data, including programming logic, data manipulation, missing data handling, basic techniques for analyzing data and creating reports. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Applications of statistical analysis methods including the usage of computer software packages. Topics include simple and multiple regression, diagnostics, forecasting, and analysis of variance. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Probability concepts, conditional probability, independence, expectations, discrete and continuous distributions, sampling distributions, estimation, hypothesis testing, goodness of fit tests. Credit may not be earned in more than one of these courses:
STA 581,
STA 584. Prerequisite:
MTH 133 or graduate standing.
Credits
3(3-0)
Prerequisites
MTH 133 or graduate standing
Corequisites
None.
Randomized block designs, Latin square designs, factorial designs, fractional factorial designs, response surface methods, robust designs. Prerequisite:
STA 580.
Credits
3(3-0)
Prerequisites
STA 580
Corequisites
None.
Theory and applications of nonparametric methods. Topics include one, two, and several sample problems, rank correlation and regression, Kolmogorov-Smirnov tests and contingency tables. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Probability defined on finite and infinite samples spaces, conditional probability and independence, random variables, expectations, moment-generating functions, probability models, limit theorems. Prerequisite:
MTH 233.
Credits
3(3-0)
Prerequisites
MTH 233
Corequisites
None.
Introductory topics from mathematical theory of statistics: population distributions, sampling distributions, point and interval estimation, tests of hypotheses. Prerequisite:
STA 584.
Credits
3(3-0)
Prerequisites
STA 584
Corequisites
None.
Simple and advanced statistical techniques used in the analysis and interpretation of clinical research data. Emphasison statistical techniques commonly used in chronic disease analysis. Prerequisite: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Statistical theory and methods for optimizing quality and minimizing costs: classical and recently developed on-line methods and Taguchi's off-line quality and robust designs. Prerequisites:
STA 580.
Credits
3(3-0)
Prerequisites
STA 580
Corequisites
None.
Principles of sampling; simple random sampling; stratified random sampling; systematic sampling; cluster sampling; sample size determination; ratio and regression estimates; comparisons among the designs. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Introduction to basic time series forecasting techniques. Topics include forecasting, basic stochastic models, time series regression, stationary and nonstationary models. Prerequisite:
STA 580.
Credits
3(3-0)
Prerequisites
STA 580
Corequisites
None.
Linear models with autocorrelated errors, non-linear regression, logistic regression, multiway ANOVA, simultaneous comparison procedures, ANOVA diagnostics, analysis of covariance, unbalanced data and missing data analysis. Prerequisites:
MTH 223;
STA 580; or graduate standing.
Credits
3(3-0)
Prerequisites
MTH 223; STA 580; or graduate standing
Corequisites
None.
Data mining techniques for analyzing large and high dimensional data. Topics include data mining strategy, exploratory analysis, predictive modeling techniques, model assessment and comparison. Prerequisite:
STA 580 or graduate standing.
Credits
3(3-0)
Prerequisites
STA 580 or graduate standing
Corequisites
None.
Six Sigma problem solving strategy for continuous improvement. Topics include DMAIC and PDSA strategies and applications, tools and statistical techniques used in the strategies. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 282 or STA 382 or STA 392; or graduate standing
Corequisites
None.
Introduction to Bayesian analysis and applications. Topics include principles of Bayesian statistics, Bayesian linear models and generalized linear models. Prerequisites:
STA 580;
STA 581 or 584; or graduate standing.
Credits
3(3-0)
Prerequisites
STA 580; STA 581 or STA 584; or graduate standing
Corequisites
None.
Subject matter not included in regular courses. May be taken for credit more than once, total credit not to exceed 6 hours. Prerequisite: permission of the instructor.
Credits
1-6(Spec)
Prerequisites
permission of the instructor
Corequisites
None.
The in-depth study of a topic in statistics under the direction of a faculty member. May be taken for credit more than once, total credit not to exceed six hours. Prerequisite: Permission of instructor.
Credits
1-6(Spec)
Prerequisites
Permission of instructor
Corequisites
None.