STATISTICS (STA–Arts and Science; Department of Mathematics and Statistics)

Note: A service course is for specific non-mathematics and non-statistics majors. Mathematics and statistics majors should not take service courses.

401/501 Probability (3)

Development of probability theory with emphasis on how probability relates to statistical inference. Topics include review of probability basics, counting rules, Bayes Theorem, distribution function, expectation and variance of random variables and functions of random variables, moment generating function, moments, probability models for special random variables, joint distributions, maximum likelihood estimation, unbiasedness, distributions of functions of random variables, chi-square distribution, students t distribution, F distribution, and sampling distributions of the sample mean and variance. Prerequisite: STA 261, 301, or 368 or equivalent. Pre- or Corequisite Calculus II.

Note: STA 501 may not be counted toward graduate degree programs in mathematics or statistics.

402/502 Statistical Programming (3)

Introduction to the use of computers to process and analyze data. Techniques and strategies for managing, manipulating, and analyzing data are discussed. Emphasis is on the use of the SAS system. Statistical computing topics, such as random number generation, randomization tests, and Monte Carlo simulation, will be used to illustrate these programming ideas. Prerequisite: STA 401/501 or STA 671 or DSC 305 or permission of instructor.

462/562 Inferential Statistics (3)

A study of estimation and hypothesis testing including a development of related probability ideas. Topics include derivation of the distribution of functions of random variables, point estimation methods, properties of point estimators, derivation of confidence interval formulas, and derivation of test statistics and critical regions for testing hypotheses. Prerequisite: STA 401/501 and Calculus III.

463/563 Regression Analysis (4)

Linear regression model, theory of least squares, statistical inference procedures, general linear hypothesis, partial F tests, residual analysis, regression diagnostics, comparison of several regressions, model adequacy, and use of statistical computer packages. Prerequisite: STA 401/501 and MTH 222 or 231.

466/566 Experimental Design Methods (4)

Experimental design concepts; completely randomized, randomized block, and Latin square designs; planned and multiple comparisons; analysis of variance and covariance; factorial and split-plot experiments; nested designs and variance components; fixed, random, and mixed effects models. Emphasis on applications and computer usage. Prerequisite: STA 463/563 or DSC 305.

467/567 Multivariate Analysis (3)

Multivariate normal distribution, partial and multiple correlations, Hotelling’s T-squared, estimation and tests of hypotheses for multivariate populations. Prerequisite: STA 401/501 and MTH 222.

471/571 Probability and Statistics Problems Seminar (1)

Solution and discussion of challenging probability problems such as those found on the first actuarial exam. Prerequisite: STA 401/501.

473/573 Applied Multiple Regression (1)

Service course. Linear regression model and assumptions, statistical inferences associated with regression, multiple correlation, curvilinear regression, selection of ‘best’ regression function, regression approach to single-factor analysis of variance. Extensive use of computer library programs. Offered in five-week sprint mode. Prerequisite: previous course in statistics.

476/576 Experimental Designs (1)

Service course. Planned and unplanned comparisons; completely randomized, randomized block, Latin square designs; factorial, nested experiments; analysis of covariance. Offered in five-week sprint mode. Prerequisite: STA 473/573.

483/583 Analysis of Forecasting Systems (3)

Introduction to quantitative prediction techniques using historical time series. Involves extensive use of interactive computing facilities in developing forecasting models and considers problems in design and updating of computerized forecasting systems. Cross-listed with CSA 483/583. Prerequisite: STA 401/501 or permission of instructor. Credit not awarded for both STA 483/583 and DSC 444.

484/584 Analysis of Categorical Data (3)

Introduction to statistical procedures used in analyzing categorical data. Chi-square tests, log-linear models, measures of association. Prerequisite: STA 401/501.

600 Topics in Advanced Statistics (1-4; maximum 10)

Prerequisite: permission of department chair.

609 Probability and Statistics for Secondary School Teachers (3)

For high school teachers. Selection of topics, with emphasis on developing good intuition as well as good understanding of the logic of the subject. Emphasis upon applications. For students in mathematics and statistics programs, credit may only be applied to Master of Arts in Teaching. Prerequisite: Licensure in secondary school mathematics or permission of instructor. Summer only.

650 Topics in Statistics (1-4; maximum 8)

Topics selected from an area of statistics. Prerequisite: permission of instructor. Offered infrequently.

660 Practicum in Data Analysis (3)

Supervised practice in consulting and statistical data analysis including use of computer programs. Maximum of six hours may be applied toward a degree in mathematics or statistics. Offered credit/no-credit basis only. Prerequisite: STA 666.

663 An Introduction to Applied Probability (3)

Random walks and ruin problems, branching processes, Markov chains, Poisson processes, birth and death processes, plus topics chosen from renewal theory, queuing theory, and Markov processes. Prerequisite: STA 401/501.

664/665 Theory of Statistics (3,3)

Topics from distribution theory, theory of estimation, theory of tests of hypothesis. Prerequisite: none.

666 General Linear Models (3)

The theory of linear models used in regression and experimental design. Topics will include: multivariate normal distributions, quadratic form theory, general linear model theory and inference for both full and less than full rank models, estimability and estimable functions. Prerequisite: STA 463/563.

667 An Introduction to Multivariate Statistical Analysis (3)

Study of multivariate normal distribution, estimation and tests of hypotheses for multivariate populations, principal components, factor analysis, discriminant analysis. Prerequisite: STA 462/56

668 Sampling Theory and Techniques (3)

Introduction to sampling theory and applications, with topics including simple random samples, sampling for proportions, systematic samples, stratified samples, cluster samples, regression and ratio estimation, and sampling errors. Prerequisite: STA 462/562 or permission of instructor.

669 Nonparametric Statistics (3)

Introduction to theory and methods of nonparametric statistics including sign test, runs test, Mann Whitney test, asymptotic relative efficiency, etc. Prerequisite: STA 462/562.

671 Environmental Statistics (3)

Service course. Descriptive statistics, probability models, sampling distributions, estimation, hypothesis testing, regression and correlation analysis, elements of experimental design, and analysis of variance. Prerequisite: graduate standing or permission of instructor.

680 Internship in Statistics (1-6; maximum 12)

Intern experience for advanced graduate students in statistics while working for appropriate industry or agency. Students must have faculty sponsor for internship. Offered on credit/no-credit basis only. Prerequisite: STA 660 and approval of department chair.

684 Categorical Data Analysis (3)

Introduction to analysis of contingency tables. Topics include: Log-linear and related modeling procedures; measures of association, sensitivity, and agreement; goodness of fit; partitioning Chi-square; collapsing multidimensional tables; sampling models for discrete data. Prerequisite: STA 462/562 or permission of instructor.

685 Biostatistics (3)

Introduction to statistical techniques used in biostatistics focusing on analysis of survival and lifetime data. Topics include nonparametric and parametric methods for estimation and comparison of survival distributions. Additional material chosen from clinical trials design and analysis, dose-response models, and risk estimation models. Prerequisite: STA 462/562 or permission of instructor.

686 Quality Control and Industrial Statistics (3)

Introduction to theory and application of statistical procedures used in industry. Topics include quality control, control charts, acceptance sampling, process optimization techniques, evolutionary operations, response surface methodology, canonical and ridge analysis, method of steepest ascent, and first and second order models. Prerequisite: STA 463/563 or permission of instructor.

700 Research for Master’s Thesis (1-12; minimum 6, maximum 12)