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Department of Statistics


After each course title the number of semester hours is given in parentheses, followed by the semester in which the course is offered (I for Fall, II for Spring, S for Summer).

Undergraduate Courses

STAT 100. Statistical Literacy in the Age of Information. (3) I, II.
This course is intended for majors in non-quantitative fields. Focus will be on the development of an awareness of statistics at the conceptual and interpretative level, in the context of everyday life. Data awareness and quality, sampling, scientific investigation, decision making, and the study of relationships are included. Emphasis will be on the development of critical thinking through in-class experiments and activities, discussions, analyses of real data sets, written reports, and collaborative learning. Computing activities will be included where appropriate; no previous computing experience required. Pr.: MATH 100. Cannot be taken for credit if credit has been received for any other statistics course.
STAT 325. Elements of Statistics. (3) I, II, S.
A basic first course in probability and statistics; frequency distributions; averages and measures of variation; probability; simple confidence intervals and tests of significance appropriate to binomial and normal populations; correlation and regression, including confidence intervals and tests of significance for bivariate populations. Pr.: MATH 100.
STAT 340. Biometrics I. (3) I, II.
A basic first course in probability and statistics with textbook, examples, and problems aimed toward the biological sciences. Frequency distributions, averages, measures of variation, probability, confidence intervals; tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation. Pr.: MATH 100. Cannot be taken for credit if credit has been received for STAT 325, or 350.
STAT 341. Biometrics II. (3) II.
Analysis and interpretation of biological data using analysis of variance, analysis of covariance, and multiple regression. Negative binomial distribution and its applications. Pr.: STAT 325, 340, or 350.
STAT 350. Business and Economic Statistics I. (3) I, II, S.
A basic first course in probability and statistics with textbook, examples, and problems pointed toward business administration and economics. Frequency distributions, averages, index numbers, time series, measures of variation, probability, confidence intervals, tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation. Pr.: MATH 100. Cannot be taken for credit if credit has been received for STAT 325, or 340.
STAT 351. Business and Economic Statistics II. (3) I, II, S.
Continuation of STAT 350 including study of index numbers, time series, business cycles, seasonal variation, multiple regression and correlation, forecasting; some nonparametric methods applicable in business and economic studies. Pr.: STAT 325, 340, or 350.
STAT 399. Honors Seminar in Statistics. (3)
Selected topics. May be used to satisfy quantitative requirements for BS degree. Open only to students in the honors program.
STAT 410. Probabilistic Systems Modeling. (3) II.
Descriptive statistics and graphical methods; basic probability; probability distributions; several random variable; Poisson processes; computer simulation of random phenomena; confidence interval estimation; hypothesis testing. Pr.: MATH 221 and CIS 300.
STAT 490. Statistics for Engineers. (1) I, II.
First course in statistics with examples and problems toward engineering. Distributions, means, measures of variation, confidence intervals, graphical display of data, simple regression and correlation, philosophy of experimentation. Must be taken conc. with a laboratory course in engineering which uses statistics.
STAT 499. Honors Project (3) I, II, S.
Open only to Arts and Science students who are active members of the University Honors Program.
STAT 510. Introductory Probability and Statistics I. (3) I, II.
Descriptive statistics, probability concepts and laws, sample spaces; random variables; binomial, uniform, normal, and Poisson; two-dimensional variates; expected values; confidence intervals; binomial parameter, median, normal mean, and variance; testing simple hypotheses using CIs and X2 goodness of fit. Numerous applications. Pr.: MATH 221.
STAT 511. Introductory Probability and Statistics II. (3) II.
Law of Large Numbers, Chebycheff's Inequality; continuation of study of continuous variates; uniform, exponential, gamma, and beta distribution; Central Limit Theorem; distributions from normal sampling; introduction to statistical inference. Pr.: STAT 510.

Undergraduate and Graduate Courses

STAT 701. Fundamental Methods of Biostatistics. (3) I, II, S.
A course emphasizing concepts and practice of statistical data analysis for health sciences. Basic techniques of descriptive and inferential statistical methods applied to health related surveys and designed experiments. Populations and samples, parameters and statistics; sampling distributions for hypothesis testing and confidence intervals for means and proportions involving one sample, paired samples and multiple independent stamples; odds ratios, risk ratios, simple linear regression. Use of statisitical software to facilitate the collection, manipulation, analysis and interpretation of health related data. Pr.: One previous statistics course.
STAT 703. Introduction to Statistical Methods for the Sciences. (3) I, II, S.
Statistical concepts and methods basic to experimental research in the natural sciences; hypothetical populations; estimation of parameters; confidence intervals; parametric and nonparametric tests of hypotheses; linear regression; correlation; one-way analysis of variance; t-test; chi-square test. Pr.: One previous statistics course.
STAT 705. Regression and Analysis of Variance. (3) I, II, S.
Simple and multiple linear regression, analysis of covariance, correlation analysis, one-, two- and three-way analysis of variance; multiple comparisons; applications including use of computers; blocking and random effects. Pr.: One previous statistics course.
STAT 706. Basic Elements of Statistical Theory. (3) I.
The mathematical representation of frequency distributions, their properties, and the theory of estimation and hypothesis testing. Elementary mathematical functions illustrate theory. Pr.: MATH 205, 210, or 220 and STAT 325 or equiv.
STAT 710. Sample Survey Methods. (3) I, in even years.
Design, conduct, and interpretation of sample surveys. Pr.: STAT 510 or 770.
STAT 713. Applied Linear Statistical Models. (3) I.
Matrix-based regression and analysis of variance procedures at a mathematical level appropriate for a first-year graduate statistics major. Topics include simple linear regression, linear models in matrix form, multiple linear regression, model building and diagnostics, analysis of covariance, multiple comparison methods, contrasts, multifactor studies, blocking, subsampling, and split-plot designs. Pr.: Prior knowledge of matrix or linear algebra and one prior course in statistics. A student may not receive credit for both STAT 705 and STAT 713.
STAT 716. Nonparametric Statistics. (3) I, in odd years.
Hypothesis testing when form of population sampled is unknown: rank, sign, chi-square, and slippage tests; Kolmogorov and Smirnov type tests; confidence intervals and bands. Pr.: STAT 705 or 713.
STAT 717. Categorical Data Analysis. (3) II.
Analysis of categorical count and proportion data. Topics include tests of association in two-way tables; measures of association; Cochran-Mantel-Haenzel tests for 3-way tables; generalized linear models; logistic regression; loglinear models. Pr.: STAT 705 or STAT 713.
STAT 720. Design of Experiments. (3) II, S.
Planning experiments so as to minimize error variance and avoid bias; Latin squares; split-plot designs; switch-back or reversal designs; incomplete block designs; efficiency. Pr.: STAT 705 or STAT 713.
STAT 722. Experimental Designs for Product Development and Quality Improvement. (3) I.
A study of statistically designed experiments which have proven to be useful in product development and quality improvement. Topics include randomization, blocking, factorial treatment structures, factional factorial designs, screening designs, and response surface methods. Pr.: STAT 511 or STAT 705 or STAT 713.
STAT 725. Introduction to SAS Computing. (1) I.
Topics may include basic environment and syntax, reading and importing data from files, data manipulation basic graphics, and built-in and user-defined functions. Pr.: One graduate-level course in statistics.
STAT 726. Introduction to R Computing. (1). I.
Topics may include basic environment and syntax, reading and importing data from files, data manipulation basic graphics, and built-in and user-defined functions. Pr.: One graduate-level course in statistics.
STAT 730. Multivariate Statistical Methods. (3) II.
Multivariate analysis of variance and covariance; classification and discrimination; principal components and introductory factor analysis; canonical correlation; digital computing procedures applied to data from natural and social sciences. Pr.: STAT 705 or STAT 713.
STAT 736. Bioassay. (2) II, in odd years.
Direct assays; quantitative dose-response models; parallel line assays; slope ratio assays; experimental designs for bioassay; covariance adjustment; weighted estimates; assays based on quantal responses. Pr.: STAT 705 or STAT 713.
STAT 745. Statistical Graphics. (3) II, in even years.
Visual display of quantitative information. Statistical graphics topics to include visual perception, basic graphics construction, quantitative univariate to multivariate statistical graphics, trellis displays, introduction to smoothing and graphics, introduction to density estimation and graphics, and categorical graphics. Modern graphics software will be used. Pr.: STAT 705 or equivalent.
STAT 770. Theory of Statistics I. (3) I.
Probability models, concepts of probability, random discrete variables, moments and moment generating functions, bivariate distributions, continuous random variables, sampling, Central Limit Theorem, characteristic functions. More emphasis on rigor and proofs than in STAT 510 and 511. Pr.: MATH 222.
STAT 771. Theory of Statistics II. (3) II.
Introduction to multivariate distributions; sampling distributions, derivation, and use; estimation of parameters, testing hypothesis; multiple regression and correlation; simple experimental designs; introduction to nonparametric statistics; discrimination. Pr.: STAT 770.
STAT 799. Topics in Statistics. (Var.) I, II, S.
Pr.: Consent of instructor.

Graduate Courses

STAT 810. Seminar in Probability and Statistics. (1) I, II.
Discussion and lectures on topics in probability and statistics; one seminar talk by each student registered for credit. Pr.: Graduate standing and at least two graduate courses in statistics.
STAT 818. Theory of Life-Data Analysis. (3) I, in even years.
A study of models and inferential procedures important to life-data analysis. Comparison of estimators (MLE, BLUE, etc.). Pivotal quantities. Design and regression models for non-normal distributions. Analysis of censored data. Pr.: STAT 705 or 713 and STAT 771.
STAT 825. Numerical Methods of Statistics. (3) II, in odd years.
Topics may include efficient programming echniques, generating data from non-standard distributions, simulation techniques,resampling methods, optimization techniques, smoothing, and imputation. Pr.: STAT 725, STAT 726, STAT 771.
STAT 842. Probability for Statistical Inference. (3) I.
Probability spaces and random elements, distributions, generating and characteristic functions, conditional expectation, convergence modes and stochastic orders, continuous mapping theorems, central limit theory and accuracy, laws of large numbers, asymptotic expansions for approximating functions of random variables and distributions. Pr.: STAT 770 & 771, or equivalent; MATH 633 or equivalent, or concurrent enrollment in MATH 633.
STAT 843. Statistical Inference. (3) II.
Distributions (commonly used univariate and multivariate distributions, including exponential families of distributions and properties), order statistics and distributional properties, (asymptotic) unbiased estimation and the information inequality, likelihood inference for parametric statistical models (including the multi-parameter case, regular and non-regular cases), confidence sets, functional parameters and statistical functionals, density estimation and nonparametric function estimation, permutation methods. Pr.: STAT 842; MATH 634 or equivalent, or concurrent enrollment in MATH 634.
STAT 850. Stochastic Processes (3) II, even years.
Normal processes and covariance stationary processes; Poisson processes; renewal counting processes; Markov chains; Brownian motion; applications to science and engineering. Pr.: STAT 770.
STAT 860. Linear Models I. (3) I.
Subspaces, projections, and generalized inverses; multivariate normal distribution, distribution of quadratic forms; optimal estimation and hypothesis testing procedures for the general linear model; application to regression models, correlation model. Pr.: STAT 713, 771.
STAT 861. Linear Models II. (3) II.
Continued application of optimal inference procedures for the general linear model to multifactor analysis of variance, experimental design models, analysis of covariance, split-plot models, repeated measures models, mixed models, and variance component models; multiple comparison procedures. Pr.: STAT 860.
STAT 870. Analysis of Messy Data. (3) I.
Design structures; treatment structures; equal and unequal variances; multiple comparisons; unequal subclass numbers; missing cells; interpretation of interaction; variance components; mixed models; split-plot and repeated measures; analysis of covariance; cross-over designs. Pr.: STAT 720.
STAT 880. Time Series Analysis. (3) I, in odd years.
Autocorrelation function; spectral density; autoregressive integrated moving average processes; seasonal time series; transfer function model; intervention analysis; regression model with time series error. Pr.: STAT 713, 771.
STAT 898. Master's Report. (2) I, II, S.
Pr.: Consent of instructor.
STAT 899. Master's Thesis Research. (Var.) I, II, S.
Pr.: Consent of instructor.
STAT 903. Statistical Methods for Spatial Data. (3) II, odd years.
Statistical models and methods for analyzing data that are collected at different spatial locations, and perhaps at different times. Spatial prediction and Kriging for continuous spatial data, along with variogram models and estimation for spatial correlation. Spectral analysis for spatial data. Spatial models for lattice data and inference for lattice models. Models and model fitting for spatial point patterns. Classical approaches as well as newly developed methodological and computational research in spatial statistics will be covered with computer-aided applications. Pr.: STAT 771, plus one introductory course in statistical computing (e.g. STAT 726 or equivalent background).
STAT 904. Resampling Methods. (3) II, even years.
Application, theory, and computational aspects of resampling methods. Topics include parametric and nonparametric bootstrap methods, the jackknife, and randomization/permutation methods; techniques for estimation, bias correction, confidence intervals, and hypothesis testing; applications to linear and nonlinear models; different test statistics for randomization inferences such as mean differences, rank based statistics, t-statistics, and moderated t-statistics for high-dimensional settings; implementation of methods using statistical software; simulation designs for comparing methods. Pr.: STAT 713, 771.
STAT 905. High-Dimensional Data and Statistical Learning. (3) I, even years.
Statistical methods for the analysis of large scale data. Data mining, supervised and unsupervised statistical learning techniques for prediction and pattern recognition. Methods for model selection, multiple testing control, and estimation in high-dimensions. Applications in various fields, including the sciences and engineering using computer software. Pr.: STAT 713 and 771, plus one introductory course in statistical computing (e.g. STAT 726 or equivalent background).
STAT 907. Bayesian Statistical Inference. (3) I, odd years.
Principles of Bayesian inference. Methods of Bayesian data analysis with applications in the sciences. Hierarchical and non-hierarchical models, including linear and generalized linear models. Model checking, Model selection, Model comparison. Bayesian computation including Markov Chain Monte Carlo algorithms. Applications in the sciences utilizing computer software. Pr.: STAT 720 and 771, plus one introductory course in statistical computing (e.g. STAT 725 or 726 or equivalent background).
STAT 940. Advanced Statistical Methods. (3) I, even years.
Generalized linear models and generalized mixed models. Statistical models based on the exponential family of distributions. Applications to non-normal and discrete data, including binary, Poisson and gamma regression, and log-linear models. Topics include likelihood-based estimation and testing, model-fitting, residual analyses, over-dispersed models, quasi-likelihood, large sample properties, and the use of computer packages. Also, methods for longitudinal repeated measures data that will include inference for continuous and discrete data. Inferential objectives include prediction of response and estimation of correlation/covariance structures. Nonparametric and semiparametric methods covered as time permits. Pr.: STAT 861, plus one introductory course in statistical computing (e.g. STAT 725 or 726 or equivalent background).
STAT 941. Advanced Statistical Inference. (3) II, even years.
Foundations and methods of modern statistical inference including asymptotic theory in parametric models (including local asymptotic normailty and contiguity), efficiency of estimators and tests, Bayes procedures, rank, sign and permutation statistics, U-, M-, L-, R-estimates, chi-square tests, empirical processes and the functional delta method, quantiles and order statistics, inference for nonparametric and semiparametric models. Pr.: STAT 843.
STAT 945. Problems in Statistical Consulting. (1) I, II.
Principles and practices of statistical consulting. Supervised experience in consultation and consequent research concerning applied statistics and probability associated with on-campus investigations. Pr.: STAT 720; restricted to majors.
STAT 950. Advanced Studies in Probability and Statistics. (Var.) I, II, S.
Theoretical studies of advanced topics in probability, decision theory, Markov processes, experimental design, stochastic processes, or advanced topics. May be repeated. Pr.: Instructor consent.
STAT 999. Research in Statistics. (Var.) I, II, S.
Pr.: Consent of instructor.