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Undergraduate Courses

201. (MATH 1342, 1442) Elementary Statistical Inference. (3-0). Credit 3. I, II
Data collection, tabulation and presentation; elementary description of the tools of statistical inference; probability, sampling and hypothesis testing; applications of statistical techniques to practical problems. May not be taken for credit after any other course in statistics or INFO303 has been taken.

211. Principles of Statistics I. (3-0). Credit 3. I, II, S
Introduction to probability and probability distributions; sampling and descriptive measures; inference and hypothesis testing; linear regression, analysis of variance. Prerequisite: MATH 152 or 172.

212. Principles of Statistics II. (3-0). Credit 3. I, II
Continuation of STAT 211. Design of experiments, model building, multiple regression, nonparametric techniques, contingency tables and short introductions to response surfaces, decision theory and time series data. Prerequisite: STAT 211.

301. Introduction to Biometry. (3-0). Credit 3. I, II
Intended for students in animal sciences. Introduces fundamental concepts of biometry including measures of location and variation, probability, tests of significance, regression, correlation and analysis of variance which are used in advanced courses and are being widely applied to animal-oriented industry. Credit will not be allowed for more than one of STAT 301, 302 or 303. Prerequisite: MATH 141 or 166 or equivalent.

302. Statistical Methods. (3-0). Credit 3. I, II, S
Intended for undergraduate students in the biological sciences and agriculture (except agricultural economics). Introduction to concepts of random sampling and statistical inference; estimation and testing hypotheses of means and variances; analysis of variance; regression analysis; chi-square tests. Credit will not be allowed for more than one of STAT 301, 302 or 303. Prerequisite: MATH 141 or 166 or equivalent.

303. Statistical Methods. (3-0). Credit 3. I, II, S
Intended for undergraduate students in the social sciences. Introduction to concepts of random sampling and statistical inference, estimation and testing hypotheses of means and variances, analysis of variance, regression analysis, chi-square tests. Credit will not be allowed for more than one of STAT 301, 302 or 303. Prerequisite: MATH 141 or 166 or equivalent.

307. Sample Survey Techniques. (3-0). Credit 3. I, II, S
Concepts of population and sample; the organization of a sample survey; questionnaire design. Basic survey designs and computation of estimates and variances. Prerequisites: STAT 301 or 302 or 303 or INFO 303.

407. Principles of Sample Surveys. (3-0). Credit 3. I
Principles of sample surveys and survey design; techniques for variance reduction; simple, stratified and multi-stage sampling; ratio and regression estimates; post-stratification; equal and unequal probability sampling. Prerequisite: STAT 212.

408. Introduction to Linear Models. (3-0). Credit 3. II
Introduction to the formulation of linear models and the estimation of the parameters of such models, with primary emphasis on least squares. Application to multiple regression and curve fitting. Prerequisites: STAT 212; MATH 304.

414. Mathematical Statistics I. (3-0). Credit 3. I
Introduction to the mathematical theory of statistics, including random variables and their distributions, expectation and variance, point estimation, confidence intervals and hypothesis testing. Prerequisite: MATH221, 251 or 253.

415. Mathematical Statistics II. (3-0). Credit 3. II
Continuation of the mathematical theory of statistics, including sampling and limiting distributions, principles for statistical inference and inference for bivariate and categorical data. Prerequisite: STAT 414.

485. Directed Studies. Credit 1 to 6. I, II, S
Special problems in statistics not covered by another course in the curriculum. Work may be in either theory or methodology. Prerequisite: Approval of instructor.

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Summary Information for All Courses

Course Descriptions from Undergraduate Catalog

Graduate Courses

601. Statistical Analysis. (3-2). Credit 4.
For students in engineering, physical and mathematical sciences. Introduction to probability, probability distributions and statistical inference; hypotheses testing; introduction to methods of analysis such as tests of independence, regression, analysis of variance with some consideration of planned experimentation. Prerequisite: MATH 152 or 172.

604. Special Problems in Statistical Computations and Analysis. (3-0). Credit 3.
Computer algorithms for programming; statistical analysis, efficient uses of existing statistical computer programs, generation of random numbers and statistical variables, programming of simulation studies, selected topics in statistical analysis not covered in STAT 601. Prerequisite: STAT 601 or concurrent enrollment in STAT 610 and 641.

605. Advanced Topics in Computational Statistics. (3-0). Credit 3.
Algorithms in constrained and unconstrained optimization; time series analysis; multivariate analysis; use and development of modern graphical exploratory data analysis; methods for interfacing programs with existing computer environments. Prerequisite: STAT 612.

607. Sampling. (3-0). Credit 3.
Planning, execution and analysis of sampling from finite populations; simple, stratified, multistage and systematic sampling; ratio estimates. Prerequisite: STAT 601 or 652 or concurrent enrollment in STAT 641.

608. Regression Analysis. (3-0). Credit 3.
Multiple, curvilinear, nonlinear, robust, logistic and principal components regression analysis; regression diagnostics, transformations, analysis of covariance. Prerequisite: STAT 601 or 641.

610. Theory of Statistics – Distribution Theory. (3-0). Credit 3.
Brief introduction to probability theory; distributions and expectations of random variables, transformations of random variables and order statistics; generating functions and basic limit concepts. Prerequisite: MATH 409 or concurrent enrollment in MATH 409.

611. Theory of Statistics – Inference. (3-0). Credit 3.
Theory of estimation and hypothesis testing; point estimation, interval estimation, sufficient statistics, decision theory, most powerful tests, likelihood ratio tests, chi-square tests. Prerequisite: STAT 610 or equivalent.

612. Theory of Linear Models. (3-0). Credit 3.
Theory of least squares, theory of general linear hypotheses and associated small sample distribution theory, analysis of multiple classifications. Prerequisites: STAT 611 or equivalent; MATH 423.

613. Advanced Theory of Statistical Inference. (3-0). Credit 3.
General theory of estimation and sufficiency including maximum likelihood and minimum variance estimation; Neyman-Pearson theory of testing hypotheses; elements of decision theory. Prerequisites: STAT 612; MATH 409.

614. Statistical Applications in Probability. (3-0). Credit 3.
Probability measures; Lebesque-Stieltjes integration, sigma fields, random variables, expectation, moment inequalities, independence, convergence of random variables and sample moments, characteristics functions, convergence of distributions, the central limit theorem and the delta method. Prerequisite: STAT 610 or its equivalent.

615. Stochastic Processes. (3-0). Credit 3.
Survey of the theory of Poisson processes, discrete and continuous time Markov chains, renewal processes, birth and death processes, diffusion processes and covariance stationary processes. Prerequisites: STAT 611; MATH 409.

616. Multivariate Analysis. (3-0). Credit 3.
Multivariate normal distributions and multivariate generalizations of classical test criteria, Hotelling’s T2, discriminant analysis and elements of factor and canonical analysis. Prerequisites: STAT 611 and 612.

618. Statistical Aspects of Machine Learning and Data Mining. Credit 3.
This course will examine the statistical aspects of techniques used to examine data streams which are large scale, dynamic, and heterogeneous. This course will examine the underlying statistical properties of classification; trees; bagging and boosting methods; neural networks; support vector machines; cluster analysis; and independent component analysis. Prerequisites: STAT 610, 611, and 613.

620. Statistical Large Sample Theory. (3-0). Credit 3.
Transformations of statistics; statistical functionals including influence curves and M, L and R estimators; u-statistics; asymptotic properties of estimators; asymptotic properties of tests; order of stochastic convergence; Edgeworth expansions and the bootstrap. Prerequisites: STAT 614.

621. Advanced Stochastic Processes. (3-0). Credit 3.
This is a second course in stochastic processes, at the non-measure theoretic level. Topics will include various types of continuous time processes such as discrete Markov processes, Brownian motion and diffusions. Prerequisite: STAT 614.

623. Statistical Methods for Chemistry. (3-0). Credit 3.
Chemometrics topics of process optimization, precision and accuracy; curve fitting; chi-squared tests; multivariate calibration; errors in calibration standards; statistics of instrumentation. Prerequisite: STAT 601, 641 or 652 or approval of instructor.

626. Methods in Time Series Analysis. (3-0). Credit 3.
Introduction to statistical time series analysis; autocorrelation and spectral characteristics of univariate, autoregressive, moving average models; identification, estimation and forecasting. Prerequisite: STAT 601 or 642 or approval of instructor.

627. Nonparametric Function Estimation. (3-0). Credit 3.
Nonparametric function estimation; kernel, local polynomials, Fourier series and spline methods; automated smoothing methods including cross-validation; large sample distributional properties of estimators; recent advances in function estimation. Prerequisite: STAT 611.

630. Overview of Mathematical Statistics. (3-0). Credit 3.
Basic probability theory including distributions of random variables and expectations. Introduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratio tests. Introduction to Bayesian methods. Prerequisites: MATH 221, 251, and 253.   Textbook

631. Statistical Methods in Finance. Credit 3.
Regression and the capital asset pricing model, statistics for portfolio analysis, resampling, time series models, volatility models, option pricing and Monte Carlo methods, copulas, extreme value theory, value at risk, spline smoothing of term structure. Prerequisites: STAT 610, 611, 608.

632. Statistical Decision Theory. (3-0). Credit 3.
Fundamentals of Bayesian inference, single and multi-parameter models, Bayesian regression and linear models, posterior simulation, MCMC, hierarchical models. Prerequisite: STAT 613.

633. Advanced Bayesian Modeling and Computation. Credit 3.
Bayesian methods in their research; methodology, and applications of Bayesian methods in bioinformatics, biostatistics, signal processing, machine learning, and related fields. Prerequisite: STAT 608, 613, 632.

636. Methods in Multivariate Analysis. (3-0). Credit 3.
Multivariate extensions of the chi-square and t-tests, discrimination and classification procedures; applications to diagnostic problems in biological, medical, anthropological and social research; multivariate analysis of variance, principal component and factor analysis, canonical correlations. Prerequisites: MATH 423 and STAT 653 or approval of instructor. Cross-listed with INFO 657. Textbook

638. Introduction to Applied Bayesian Methods. (3-0). Credit 3.

Students will learn how uncertainty regarding parameters can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution Course will emphasize modeling and computations under the Bayesian paradigm. Topics include: prior distributions, Bayes Theorem, conjugate and non-conjugate models, posterior simulation via the Gibbs sampler and MCMC, hierarchical modeling. Prerequisite: STAT 604, 608, or 630.

641. The Methods of Statistics I. (3-0). Credit 3.
An application of the various disciplines in statistics to data analysis, introduction to statistical software; demonstration of interplay between probability models and statistical inference. Prerequisite: Concurrent enrollment in STAT 610 or approval of instructor. Textbook

642. The Methods of Statistics II. (3-0). Credit 3.
Design and analysis of experiments; scientific method; graphical displays; analysis of nonconventional designs and experiments involving categorical data. Prerequisite: STAT 641.

643. Biostatistics I. (3-0). Credit 3.
Bio-assay for quantitative and quantal responses: statistical analysis of contingency, including effect estimates, matched samples and misclassification. Prerequisites: STAT 608, 630, and 642 or STAT 610.

644. Biostatistics II. (3-0). Credit 3.
Generalized linear models; survival analysis with emphasis on nonparametric models and methods. Prerequisite: STAT 643 or approval of instructor.

645. Applied Biostatistics and Data Analysis. Credit 3.
Survey of crucial topics in biostatistics; application of regression in biostatistics; analysis of correlated data; logistic and Poisson regression for binary or count data; survival analysis for censored outcomes; design and analysis of clinical trials; sample size calculation by simulation; bootstrap techniques for assessing statistical significance; data analysis using R. Prerequisites: STAT 651, 652, and 659, or equivalent or prior approval of instructor.

646. Statistical Bioinformatics. Credit 3.
An overview of relevant biological concepts and technologies of genomic/proteomic applications; methods to handle, visualize, analyze, and interpret genomic/proteomic data; exploratory data analysis for genomic/proteomic data; data preprocessing and normalization; hypotheses testing; classification and prediction techniques for using genomic/proteomic data to predict disease status. Prerequisites: STAT 604, 651, 652 or equivalent or prior approval of instructor.

647. Spatial Statistics. (3-0). Credit 3.
Spatial correlation and its effects; spatial prediction (kriging); spatial regression; analysis of point patterns (tests for randomness and modelling patterns); subsampling methods for spatial data. Prerequisite: STAT 601 or 611 or equivalent.

648. Applied Statistics and Data Analysis. Credit 3.
Background to conduct research in the development of new methodology in applied statistics. Topics covered will include: exploratory data analysis; sampling; testing; smoothing; classification; time series; and spatial data analysis. Prerequisite: Approval of instructor.

651. Statistics in Research I. (3-0). Credit 3.
For graduate students in other disciplines; non-calculus exposition of the concepts, methods and usage of statistical data analysis; T-tests, analysis of variance and linear regression. Prerequisite: MATH 102 or equivalent.

652. Statistics in Research II. (3-0). Credit 3.
Continuation of STAT 651. Concepts of experimental design, individual treatment comparisons, randomized blocks and factorial experiments, multiple regression, Chi-squared tests and a brief introduction to covariance, non-parametric methods and sample surveys. Prerequisite: STAT 651.

653. Statistics in Research III. (3-0). Credit 3.
Advanced topics in ANOVA; analysis of covariance; and regression analysis including analysis of messy data; non-linear regression; logistic and weighted regression; diagnostics and model building; emphasis on concepts; computing and interpretation. Prerequisite; STAT 652.

656. Applied Analytics Using SAS Enterprise Miner. Credit 3.
Introduction to data mining and will demonstrate the procedures; Optimal prediction decisions; comparing and deploying predictive models; neural networks; constructing and adjusting tree models; the construction and evaluation of multi-stage models. Prerequisite: STAT 657.

657. Advanced Programming Using SAS. (3-0). Credit 3.
Programming with SAS/IML, programming in SAS Data step, advanced use of various SAS procedures. Prerequisites: STAT 604 and 642.

658. Transportation Statistics. (3-0). Credit 3.
Design of experiments, estimation, hypothesis testing, modeling, and data mining for transportation specialists. Prerequisite: STAT 211 or STAT 651.

659. Applied Categorical Data Analysis. (3-0). Credit 3.
Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as SAS, GLIM, SPSSX. Prerequisite: STAT 601, 641 or 652 or equivalent. Textbook

661. Statistical Genetics I. (3-0). Credit 3.
Basic concepts in human genetics, sampling designs, gene frequency estimation, Hardy-Weinberg equilibrium, linkage disequilibrium, association and transmission disequilibrium test studies, linkage and pedigree analysis, segregation analysis, polygenic models, DNA sequence analysis. Prerequisites: STAT 610 and 611.

665. Statistical Applications of Wavelets. (3-0). Credit 3.
This is a course on the use of wavelet methods in statistics. The course introduces wavelet theory, provides an overview of wavelet-based statistical methods. Topics include smoothing of noisy signals, estimation of function data and representation of stochastic processes. Some emphasis is given to Bayesian procedures. Prerequisite: STAT 611 or approval by the instructor.

667. Statistics for Advanced Placement Teachers. (3-0). Credit 3.
Review of the fundamental concepts and techniques of statistics; topics included in Advanced Placement Statistics; exploring data, planning surveys and experiments, exploring models, statistical inference. Prerequisite: Approval of instructor.

671. Methods of Statistical Data Modeling I. (3-0). Credit 3.
Introduction to new methods of statistical analysis, especially statistical data modeling, exploratory data analysis, adaptive and robust estimation. Prerequisite: STAT 611 or equivalent.

673. Time Series Analysis I. (3-0). Credit 3.
Introduction to diverse modes of analysis now available to solve for univariate time series; basic problems of parameter estimation, spectral analysis, forecasting and model identification. Prerequisite: STAT 611 or equivalent.

674. Time Series Analysis II. Credit 3.
Continuation of STAT 673. Multiple time series, ARMA models, test of hypotheses, estimation of spectral density matrix, transfer function and forecasting. Prerequisites: STAT 673.

681. Seminar. (1-0). Credit 1.
Oral presentations of special topics and current research in statistics. May be repeated for credit. Prerequisite: Graduate classification in statistics.

684. Professional Internship. Credit 1 to 3.
Practicum in statistical consulting for students in PhD program. Students will be assigned consulting problems brought to the Department of Statistics by researchers in other disciplines. Prerequisite: STAT 642 or its equivalent.

685. Directed Studies. Credit 1 to 6.
Individual instruction in selected fields in statistics; investigation of special topics not within scope of thesis research and not covered by other formal courses. Prerequisites: Graduate classification and approval of department head.

689. Special Topics in... Credit 1 to 4.
Selected topics in an identified area of statistics. Open to non-majors. May be repeated for credit. Prerequisite: Approval of instructor.

691. Research. Credit 1 or more.
Research for thesis or dissertation. Prerequisite: Graduate classification.

See Also...

Summary Information for All Courses

Course Descriptions from Graduate Catalog