Statistics (STAT)

Prerequisite: placement test. Applied approach to data description and statistical inference (hypothesis testing, estimation). Analysis of ratios, rates, and proportions. Computer applications. Laboratory. (Formerly 3470:260)

Prerequisite: placement test. Descriptive statistics, tabular and graphical data displays; probability, probability distributions. Introduction to statistical inference (hypothesis testing, estimation); one-sample parametric and nonparametric methods. Computer applications. (Formerly 3470:261)

Prerequisite: STAT 261 or equivalent. Parametric and nonparametric methods of statistical inference for paired data and two-sample problems; one-way ANOVA, simple linear regression and correlation. Computer applications. (Formerly 3470:262)

Prerequisite: Permission. Selected topics of interest in statistics. (Formerly 3470:289)

Prerequisites: STAT 250 or STAT 260 or STAT 262. This course provides practical statistical methods beyond the introductory course. The topics include design of experiments, data analysis, multiple regression and modern software use. (Formerly 3470:360)

Prerequisite: MATH 221. Introduction to probability, statistics, random variables, data descriptions, statistical inference, confidence intervals, hypothesis testing, design of experiments, and applications of statistics to engineering. (Formerly 3470:401)

Prerequisite: MATH 221. Introduction to probability, random variables and probability distributions, expected value, sums of random variables, Markov processes. (Formerly 3470:450)

Prerequisite: MATH 223. Sequential (part 1 of 2). Appropriate background is three semesters of calculus or equivalent. Elementary combinatorial probability theory, probability distributions (discrete and continuous), expectation and variance, bivariate and multivariate distributions, distributions of functions of random variables. (Formerly 3470:451)

Prerequisite: STAT 451. Sequential (2nd of 2 parts). Sampling distributions, point estimation and properties of point estimators, sufficiency, Rao-Blackwell method and MVUE, methods of obtaining point estimators, interval estimation, hypothesis testing, Neyman-Pearson theory of optimal tests (Formerly 3470:452)

Prerequisite: MATH 221. Applications of statistical theory to natural and physical sciences and engineering, including probability distributions, interval estimation, hypotheses testing (parametric and nonparametric), and simple linear regression and correlation. (Formerly 3470:461)

Prerequisite: STAT 262 or STAT 461. Applications of the techniques of regression and multifactor analysis of variance. (Formerly 3470:462)

Prerequisite: STAT 262 or STAT 461 or equivalent. Design and analysis of frequently used sample survey techniques. (Formerly 3470:465)

Prerequisites: [STAT 261 and STAT 262] or STAT 461. This course introduces the basic tasks of inferential statistics (estimation, hypothesis testing, regression, analysis of variance) in situations where the usual assumption of the data following a parametric distribution cannot be justified or verified. Topics include the one-sample location problem, the two-sample location problem, the two-sample dispersion problem, the case with 3 or more populations – one-way layout, the case with 3 or more populations – two-way layout, binary data and success probabilities, regression and correlation. (Formerly 3470:466)

Prerequisite: STAT 461. Selected topics in reliability modeling including parametric and nonparametric models, competing modes of failure, censored data and accelerated life models. (Formerly 3470:469)

Prerequisite: STAT 261 and STAT 262 or STAT 461, or equivalent. Biostatistics and Epidemiological methods for biological and medical studies, including ANOVA, analysis of repeated measures, disease-related measures, log-linear models, and clinical trials. (Formerly 3470:470)

Prerequisite: MATH 221 or equivalent. Pre/Corequisite: MATH 222 or equivalent. Interest theory and financial mathematics used in actuarial science. Topics include value of money, annuities, loans, bonds, cash flows and immunization, interest rate swaps. (Formerly 3470:471)

Prerequisite: STAT 451. Study of severity, frequency and aggregate models used in actuarial applications. Calibration and evaluation. credibility procedures, fundamental principles of pricing in short-term insurance coverage. (Formerly 3470:472)

Prerequisite: STAT 262 or STAT 461. Basic concepts in survival analysis, censoring and data truncation, estimation of survival models, nonparametric hazard and survival function estimation, comparing survival times between groups. (Formerly 3470:473)

Prerequisite: STAT 461 or equivalent. Course provides a solid foundation in the theory and applications of statistical techniques widely used in industry. (Formerly 3470:475)

Prerequisite: STAT 262 or STAT 461 or equivalent. Basic concepts in Bayesian theory, sampling methods, MCMC, and hierarchical modeling. Computer applications of Bayesian statistics to natural; and physical; sciences and engineering. (Formerly 3470:476)

Prerequisite: STAT 262, STAT 450, STAT 451, or STAT 461 . Stationarity. ARIMA modeling with seasonality. Parameter estimation, model diagnostics and forecasting. Regression with autocorrelated errors. Cointegration and multivariate ARMA models. Heterosecedasticity and long-memory models (Formerly 3470:477)

Prerequisite: STAT 262 or STAT 461. Students learn data organization and structures, design of statistical data bases, statistical software analysis, importing and exporting data between software, and missing data analysis. (Formerly 3470:480)

Prerequisite: STAT 262 or STAT 461 or equivalent. Topics include data management, random number generation, resampling methods, numerical optimization, Markov Chain Monte Carlo, smoothing methods, data mining: clustering and classification. (Formerly 3470:483)

Prerequisite: STAT 262 or STAT 461 or equivalent. Methodologies for statistical learning, including generalized logistic regression, ridge regression, neural networks, support vector machines, principal components analysis, and K-means and hierarchical clustering (Formerly 3470:484)

Prerequisite: STAT 262 or STAT 461. Selected topics in predictive modeling using CHAID, Classification and Regression Trees, Logistic Regression and Neural Networks (Formerly 3470:485)

Prerequisite: STAT 262 or STAT 461 or equivalent. Basic concepts of geostatistics, point pattern, area unit. Spatial-temporal modeling in high dimensional data. Computer applications to natural and physical sciences and engineering. (Formerly 3470:486)

(May be repeated for a total of six credits) Prerequisite: Permission. Selected topics in advanced statistics, including quality control, reliability, sampling techniques, decision theory, advanced inference, stochastic processes and others. (Formerly 3470:489)

(May be repeated with change of topic) Group studies of special topics in statistics. May not be used to meet undergraduate or graduate major requirements in mathematics and statistics. May be used for elective credit only. (Formerly 3470:491)

Prerequisites: STAT 462 and STAT 480, or permission or instructor. This course provides exposure to a variety of advanced statistical methods (beyond the ones taught in our undergraduate curriculum) for handling the challenges of high-dimensional high-throughput data, along with their software implementation and applications. Topics include multiple hypothesis testing and multiplicity adjustment, curse of dimensionality, sparsity, high-dimensional data visualization, dimension reduction methods, model selection and estimator selection, machine learning methods, and aggregation of estimators and classifiers. (Formerly 3470:494)

Prerequisite: STAT 462 or STAT 480 or permission. Students will learn about various aspects of statistical consulting and will work on current projects of the Center for Statistical Consulting. May be repeated for a total of 4 credits. (Formerly 3470:495)

Prerequisites: STAT 462 and STAT 480, or permission of instructor. This course provides exposure to a variety of advanced statistical methods (beyond the ones taught in our undergraduate curriculum) for handling the challenges of modern-day data analysis, along with their software implementation and applications. Topics include distribution-free statistical methods, modern regression methods (robust, penalized, nonparametric), generalized linear models, random effects models, generalized linear mixed models, generalized additive models, some machine learning methods, some data mining methods, and an introduction to biostatistics. (Formerly 3470:496)

(May be repeated for a total of four credits) Prerequisites: senior standing and permission. Directed studies in statistics designed as introduction to research problems under guidance of selected faculty member. (Formerly 3470:497)

Prerequisite: STAT 489 (honors) and senior in the Honors Program. Directed study for a senior student in the University Honors Program who has completed STAT 489 (honors). An introduction to research problems in Statistics under the guidance of selected faculty. (Formerly 3470:498)