Statistics (STA)                      Course Schedules

The complete description of Statistics is found in the Department of Mathematics in the College of Science & Technology.

Distance Learning Courses: Courses in the department approved for offering in a distance learning format include: STA 282.

STA 282 Introduction to Statistics 3(3-0) F, Sp
Descriptive statistics, probability, sampling distributions, statistical inference, regression. Course does not count on major, minor in mathematics. Credit may not be earned in both STA 282 and STA 382. Prerequisite: successful completion of MTH 105; or passing another math class 100-level or higher; or 50% or better on the Basic Mathematics Placement Test; or a score of 11 or above on the Elementary Algebra portion of the ACT; or a score of 10 or above on the Intermediate Algebra portion of the ACT.

STA 382 Elementary Statistical Analysis 3(3-0) F, Sp
An introduction to statistical analysis. Topics will include descriptive statistics, probability, sampling distributions, statistical inference, and regression. Greater emphasis than in STA 282 will be placed on probability theory and probability distribution. Credit may not be earned in both STA 282 and STA 382. Prerequisite: MTH 130.

STA 575 Introduction to Statistical Packages 3(3-0)
Introduction to statistical packages for data management and data analysis. SPSS, SAS and S-PLUS are introduced. Prerequisites: STA 282, STA 382 or permission of instructor.

STA 580 Applied Statistical Methods I 3(3-0)
Applications of statistical methods including the usage of computer packages. Topics include forecasting, simple and multiple regression, and analysis of variance. Prerequisites: STA 282, STA 382. 

STA 582 Experimental Designs 3(3-0)
Randomized block designs, Latin square designs, factorial designs, fractional factorial designs, response surface methods. Prerequisite: STA 580 or equivalent.

STA 583 Nonparametric Statistics 3(3-0)
Theory and applications of nonparametric methods. Topics include one-, two-, and several-sample problems, rank correlation and regression, Kolmogorov-Simirnov tests, and contingency tables. Prerequisite: STA 382.

STA 584 Mathematical Statistics I 3(3-0) F
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.

STA 585 Mathematical Statistics II 3(3-0) Sp
Introductory topics from mathematical theory of statistics: population distributions, sampling distributions, point and interval estimation, tests of hypotheses. Prerequisite: STA 584.

STA 586 Clinical Trials and Survival Analysis I 3(3-0)
Simple and advanced statistical techniques used in the analysis and interpretation of clinical research data. Emphasis on statistical techniques commonly used in chronic disease analysis. Prerequisites: STA 382 or equivalent.

STA 587 Statistical Theory and Methods for Quality Improvement 3(3-0)
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.

STA 588 Sampling Techniques 3(3-0)
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 382 or equivalent.

STA 589 Time Series Forecasting 3(3-0)
Introduction to basis timer series forecasting techniques. Topics include forecasting, Box-Jenkins models, time series regression, exponential smoothing, and transfer function models Prerequisites: STA 580, or permission of instructor.

STA 590 Applied Statistical Methods II 3(3-0) Sp
Multiway ANOVA, multiple comparison procedures, analysis of covariance, repeated measures analysis, unbalanced data and missing data analysis. Prerequisites: STA 580 and MTH 223.

STA 591 Statistical Methods for Data Mining 3(3-0)
Introduction to statistical techniques for data mining, including an overview of data mining and its applications, commonly used data mining techniques such as clustering, classification, association and predictive modeling techniques. Prerequisites: STA 580.

STA 596 Special Topics In Statistics 1-6(Spec)
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.

STA 597 Independent Study 1-6(Spec)
Open to students with permission of instructor. May be taken for credit more than once, total credit not to exceed 6 hours. Prerequisite: permission of department chairperson and instructor.

For 600 and 700 level course descriptions, consult the current Graduate Bulletin.

STA 680 Statistical Data Analysis and Consulting 3(3-0)

STA 682 Linear Models 3(3-0)

STA 684 Theory of Statistical Inference 3(3-0)

STA 686 Multivariate Analysis 3(3-0)

STA 782 Generalized Linear Models 3(3-0)

STA 784 Theory of Estimation 3(3-0)

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