Courses
The following are undergraduate and graduate courses in statistics offered through the Department of Information Systems, Statistics, and Management Science.
Undergraduate Courses
- ST 260 Statistical Data Analysis
- ST 450 Statistical Methods in Research I
- ST 451 Statistical Methods in Research II
- ST 454 Mathematical Statistics I
- ST 455 Mathematical Statistics II
- ST 465 Sampling Techniques
- ST 475 Statistical Quality Control
Graduate Courses
- ST 509 Statistics for Business Applications
- ST 521 Statistical Data Management
- ST 522 Advanced Statistical Data Management
- ST 531 Knowledge Discovery and Data Mining I
- ST 532 Advanced Data Mining
- ST 550 Statistical Methods for Applied Research I
- ST 551 Statistical Methods for Applied Research II
- ST 552 Applied Regression Analysis
- ST 553 Applied Multivariate Analysis
- ST 554 Mathematical Statistics I
- ST 555 Mathematical Statistics II
- ST 560 Statistical Methods in Research I
- ST 561 Applied Design of Experiments
- ST 565 Sampling Techniques
- ST 570 Time Series Analysis
- ST 575 Statistical Quality Control
- ST 580 Analysis of Categorical-Level Data
- ST 603 Advanced Inference
- ST 610 Linear Models
- ST 615 Theory of Regression
- ST 635 Nonparametric Statistics
- ST 640 Statistical Computing
- ST 675 Advanced Statistical Quality Control
ST 260 Statistical Data Analysis.
Introduction to the use of basic statistical concepts in business applications.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Barrett |
 |
Fall 2007 |
002 |
Barrett |
|
Fall 2007
|
003 |
Mansfield |
|
Fall 2007 |
901 |
Mansfield |
|
Fall 2007 |
991 |
Barrett |
|
Fall 2007 |
992 |
Goertz |
|
ST 450 Statistical Methods in Research I.
Development of fundamental concepts of organizing, exploring, and summarizing data; probability; common probability distributions; sampling and sampling distributions; estimation and hypothesis testing for means, proportions, and variances using parametric and nonparametric procedures; power analysis; goodness of fit; contingency tables. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on methods and on selecting proper statistical techniques for analyzing real situations.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Mansfield |
|
ST 451 Statistical Methods in Research II.
Analysis of variance and design of experiments, including randomization, replication, and blocking; multiple comparisons; correlation; simple and multiple regression techniques, including variable selection, detection of outliers, and model diagnostics. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on appropriate analysis of data in real situations.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Barrett |
|
ST 454 Mathematical Statistics I.
Fundamental concepts and theory of probability. Sample spaces, random variables, probability distributions, moments and moment-generating functions, and sampling distributions.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Adams |
|
ST 455 Mathematical Statistics II.
Theory of point and interval estimation, hypothesis testing, chi square tests, correlation, regression, and analysis of variance. Includes some applications.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
|
|
Fall 2007 |
002 |
|
|
Fall 2007 |
003 |
|
ST 465 Sampling Techniques.
Planning, execution, and evaluation of sample surveys. Simple, random, stratified, and cluster sampling; multistage and systematic sampling; questionnaire design; cost functions; and optimal designs. Teams plan, perform, and analyze actual sample surveys.
Semester |
Section |
Instructor |
Syllabus |
|
ST 475 Statistical Quality Control.
Statistical methods useful in control of quality of manufactured products. Topics include Shewhart and cumulative sum control charts; process capability analysis; and acceptance sampling procedures by attributes and variables. Emphasis is on understanding, design, implementation, and interpretation of these techniques.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Chakraborti |
|
ST 509 Statistics for Business Applications.
A broad elementary introduction to statistical and probabilistic methods useful for managerial decision making. The course requires three hours of lecture and one hour of laboratory work per week. The laboratory is used to expose the student to computer software applications.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Gray |
|
ST 521 Statistical Data Management.
Introduction to the management of data using SAS. The collection and management of data from business or scientific research projects are emphasized.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Casselman |
|
ST 522 Advanced Statistical Data Management.
This course provides students with insight and understanding into the advanced aspects of data management. Emphasis will be placed on computer techniques for the preparing and cleaning of data from scientific research projects as well as for business-oriented projects in order to conduct advanced level analyses. Techniques for detecting, quantifying, and correcting data quality will be covered.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Casselman |
|
ST 531 Knowledge Discovery and Data Mining I.
Data mining is the process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns of data. Techniques for accomplishing these tasks in a business setting will be discussed.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Hardin |
|
ST 532 Advanced Data Mining.
A detailed study of data mining techniques including logistic regression, neural networks, decision trees, general classifier theory, and unsupervised learning methods. Mathematical details and computer techniques are examined. The SAS programming language and SAS's Enterprise Miner will be used to accomplish these tasks. Other packages may also be used.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Hardin |
|
ST 550 Statistical Methods for Applied Research I.
Development of fundamental concepts of organizing, exploring, and summarizing data; probability; common probability distributions; sampling and sampling distributions; estimation and hypothesis testing for means, proportions, and variances using parametric and nonparametric procedures; power analysis; goodness of fit; contingency tables. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on methods and on selecting proper statistical techniques for analyzing real situations.
Semester |
Section |
Instructor |
Syllabus |
Fall 2006 |
001 |
Barrett |
|
ST 551 Statistical Methods for Applied Research II.
Analysis of variance and design of experiments, including randomization, replication, and blocking; multiple comparisons; correlation; simple and multiple regression techniques including variable selection, detection of outliers, and model diagnostics. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on appropriate analysis of data in real situations.
ST 552 Applied Research Analysis.
Modeling issues for multiple linear regression are discussed in the context of data analysis. These include the use of residual plots, transformations, hypothesis tests, outlier diagnostics, analysis of covariance, variable selection techniques, weighted least squares and colinearity. The uses of multiple logistic regression are similarly discussed for dealing with binary-valued dependent variables.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Barrett |
|
Fall 2007 |
001 |
Davis |
|
ST 553 Applied Multivariate Analysis.
Methods and business applications of multivariate analysis, discriminant analysis, canonical correlation, factor analysis, cluster analysis, and principal components.
Semester |
Section |
Instructor |
Syllabus |
Fall 2006 |
001 |
Barrett |
|
ST 554 Mathematical Statistics I.
Distributions of random variables, moments of random variables, probability distributions, joint distributions, and change of variable techniques.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Adams |
|
ST 555 Mathematical Statistics II.
Theory of order statistics, point estimation, interval estimation, and hypothesis testing.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Chakraborti |
|
ST 560 Statistical Methods in Research I.
Statistical methods for summarizing data; probability; common probability distributions; sampling and sampling distributions; estimation and hypothesis testing for means, proportions, and variances using parametric and nonparametric procedures; power analysis; goodness of fit; contingency tables; and simple regression and one-way analysis of variance.
Semester |
Section |
Instructor |
Syllabus |
Fall 2006 |
001 |
Adams |
|
ST 561 Applied Design of Experiments.
An introduction to the design and analysis of experiments. Topics include factorial, fractional factorial, block, incomplete block, and nested designs. Other methods discussed include Taguchi Methods, response surface methods, and analysis of covariance.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Adams |
|
Fall 2007 |
001 |
Adams |
|
ST 565 Sampling Techniques.
Planning, execution, and evaluation of sample surveys. Simple, random, stratified, and cluster sampling; multistage and systematic sampling; questionnaire design; cost functions; and optimal designs. Teams will plan, perform, and analyze actual sample surveys.
ST 570 Time Series Analysis.
Modeling of both stationary and non-stationary time series. Autoregressive (AR) processes and moving average (MATH) processes, as well as mixed (ARMA) processes, are discussed, along with model identification and estimation and forecasting procedures. Computer software is used.
ST 575 Statistical Quality Control.
Statistical methods useful in control and improvement of manufactured products, including statistical process control with variables and attribute control charts, and process improvement with designed experiments. Emphasis is placed on design, implementation, and interpretation of the techniques.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Adams |
|
ST 580 Analysis of Categorical-level Data.
Logit and probit models, including dichotomous and multichotomous response functions; discrete choice models; log-linear models for multi-way contingency tables; procedures for analyzing ordinal-level data.
ST 603 Advanced Inference.
A continuation of ST 555, with emphasis on the general theory of estimation and hypothesis testing and large sample distribution theory.
Semester |
Section |
Instructor |
Syllabus |
Fall 2007 |
001 |
Chakraborti |
|
ST 610 Linear Models.
Gauss-Markov Theorem, solution of linear systems of less than full rank, generalized inverse of matrices, distributions of quadratic forms, and theory for estimation and inference for the general linear model.
Semester |
Section |
Instructor |
Syllabus |
Fall 2006 |
001 |
Conerly |
|
ST 615 Theory of Regression.
Theory of the general linear regression models and inference procedures, variable selection procedures, and alternate estimation methods including principal components regression, robust regression methods, ridge regression, and nonlinear regression.
Semester |
Section |
Instructor |
Syllabus |
Spring 2007 |
001 |
Gray |
|
ST 635 Nonparametric Statistics.
Theory and applications of various nonparametric statistical methods are covered for one-sample, two-sample, and multi-sample problems. Goodness of fit techniques such as Chi-square and the kolmogorov-Smirnov test are covered along with graphical analysis based on P-P and Q-Q plots. Computer software such as MINITAB, SAS, and STATXACT are used.
ST 640 Statistical Computing.
Topics include a survey of current statistical software, numerical methods for statistical computations, nonlinear optimization, statistical simulation, and recent advances in computer-intensive statistical methods.
ST 675 Advanced Statistical Quality Control.
Theoretical approaches to statistical process control procedures and the design of experiments for quality improvement.
*The Department of Information Systems, Statistics, and Management Science also offers courses of independent study, selected topics in statistics, internships, and research in statistics for students who would like to specialize or conduct advanced research in a particular field.