Note: Service courses do not count toward majors in the Department of Mathematics and Statistics. They may or may not count toward majors in other departments. Look carefully at your major requirements and at the mathematics and statistics placement guide elsewhere in this Bulletin.
Introduces students to the multidisciplinary nature of environmental science and the solution of environmental problems. This course does not meet any CAS requirements. Cross-listed with BOT/CHM/GEO/GLG/MBI/MTH/ZOO 175.
Service course. Descriptive statistics, basic probability, random variables, binomial and normal probability distributions, tests of hypotheses, regression and correlation, analysis of variance. Emphasis on applications. Prerequisite: MTH 102 or 104 or 121 or three years of college preparatory mathematics or permission of department chair. Note: STA 261.S is designed for social science majors. V. CAS-E.
Note: Credit for graduation will not normally be given for more than one of DSC 205, STA 261, STA 301, or STA 368.
Introduction to the principles and methodologies of environmental science. Topics include contamination of earth systems and pollution mitigation; use, abuse and conservation of natural resources; land use, conservation and preservation, planning and management and the value of biodiversity and wilderness. This course does not meet any CAS requirements. Cross-listed with BOT/CHM/GEO/GLG/MBI/ZOO 275.
A first course in applied statistics including an introduction to probability, the development of estimation and hypothesis testing, and a focus on statistical methods and applications. Includes introduction to probability of events, random variable, binomial and normal distributions, mathematical expectation, sampling distributions, estimation, and hypothesis testing. Statistical methods include one and two sample procedures for means and proportions, chi-square tests, analysis of variance, and linear regression. Prerequisite: MTH 141, Calculus I or II.
Applied study of statistical techniques useful in estimating parameters of a population whose underlying distribution is unknown. Chi-square, runs, and association tests covered. Cross-listed with DSC 333. (For majors in the department, this course counts only toward the B.S. in Statistics.) Prerequisite: DSC 205 or STA 301 or STA 363 or STA 368.
Service course. Applications of statistics using regression and design of experiments techniques. Regression topics include simple linear regression, correlation, multiple regression and selection of the best model. Design topics include the completely randomized design, multiple comparisons, blocking and factorials. Prerequisite: STA 261 or STA 301 or STA 368 or DSC 205 or permission of instructor.
Statistical procedures used in quality control. Control charts, acceptance sampling, industrial applications. Cross-listed with DSC 365. (For majors in the department, this course counts only toward B.S. in Statistics.) Prerequisite: DSC 205 or STA 301 or 363 or 368.
Service course. Beginning course in statistics with emphasis on methods and applications. Probability, random variables, binomial and normal probability distributions, sampling distributions, statistical inference procedures, linear regression, analysis of variance and other data analysis methods. Prerequisite: Calculus I or II.
Note: Students with majors other than engineering should take STA 301 rather than STA 368. Engineering majors should check the degree requirements for their major to determine whether to take STA 301 or 368.
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 Co-requisite Calculus II.
Note: STA 501 may not be counted toward graduate degree programs in mathematics or statistics.
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 permission of instructor.
Survey sampling with applications to problems of business research. Simple random sampling, systematic sampling, stratified random sampling, ratio estimation, and cluster sampling. (For majors in the department, this course counts only toward B.S. in statistics.) Prerequisite: DSC 305 or STA 363 or STA 401 or permission of instructor. Cross-listed with DSC 432/532.
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.
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.
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. Credit does not count toward a graduate degree in mathematics and statistics. Prerequisite: STA 463/563 or DSC 305.
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.
Solution and discussion of challenging probability and statistics problems such as those found on the second actuarial exam. Introduction to at least squares estimation, order statistics, and Bayesian statistics. Prerequisite: STA 401/501 and completion or concurrent registration in STA 462/562. Note: does not count toward requirements of minor or major programs in the department (undergraduate or graduate).
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.
The use of statistical data analysis to solve a variety of projects. Emphasis on integrating a broad spectrum of statistical methodology, presentation of results both oral and written, use of statistical computing packages to analyze and display data, and an introduction to the statistical literature. A term project involving student teams combines elements of all of the above. Prerequisite: STA 463/563 or 363, or DSC 305.
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.
Departmental honors may be taken for a minimum of four semester hours and a maximum total of six semester hours in one or more semesters of student's senior year.
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.
Introduction to statistical procedures used in analyzing categorical data. Chi-square tests, log-linear models, measures of association. Prerequisite: STA 401/501.
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.
Topics selected from an area of statistics. Prerequisite: permission of instructor. Offered infrequently.
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.
Random walks and ruin problems, branching processes, Markov chains, Poisson processes, birth and death processes, plus topics chosen from renewal theory, queueing theory, and Markov processes. Prerequisite: STA 401/501.
Topics from distribution theory, theory of estimation, theory of tests of hypothesis. Prerequisite: (664) MTH 441/541 and STA 462/562; (665) STA 664.
Completely randomized, randomized block and Latin square designs; analysis of variance and covariance, planned orthogonal and multiple comparisons; response curve analysis, factorial and split-plot experiments; confounding and fractional factorials, nested and cross classification data; subsampling and variance components; fixed, random, and mixed effects models, complex experimental designs. Prerequisite: STA 463/563.
Study of multivariate normal distribution, estimation and tests of hypotheses for multivariate populations, principal components, factor analysis, discriminant analysis. Prerequisite: STA 462/562.
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.
Introduction to theory and methods of nonparametric statistics including sign test, runs test, Mann Whitney test, asymptotic relative efficiency, etc. Prerequisite: STA 462/562.
Service course. Descriptive statistics, probability models, sampling distributions, estimation, hypothesis testing, regression and correlation analysis, elements of experimental design, and analysis of variance. Prerequisite: previous course in statistics or graduate standing or permission of instructor.
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.
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.
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.
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.
Required of all newly appointed graduate assistants. Deals with practical problems encountered in teaching algebra, trigonometry, statistics, and calculus. Credit does not count toward a graduate degree in mathematics or statistics. Offered on credit/no-credit basis only. Prerequisite: graduate standing and teaching responsibilities in mathematics or statistics. Summer only.