Courses and Programs 1997-1999
Courses and Programs 1997-1999Stat 100. Orientation in Statistics. (1-0) Cr. R. F. Opportunities, challenges, and the scope of the curriculum in statistics. For students planning or considering a career in this area.
Stat 101. Principles of Statistics. (3-2) Cr. 4. F.S.SS. Prereq: 11/2 years of high school algebra. Statistical concepts in modern society; descriptive statistics and graphical displays of data; the normal distribution; data collection; elementary probability; elements of statistical inference; estimation and hypothesis testing; linear regression and correlation; contingency tables. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 227.
Stat 104. Introduction to Statistics. (2-2) Cr. 3. F.S.SS. Prereq: 11/2 years of high school algebra. Statistical concepts and their use in science; collecting, organizing and drawing conclusions from data; sampling and experimentation as ways of generating data; methods for describing and summarizing data and understanding relationships; statistical inference. For students in the agricultural and biological sciences. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 227.
Stat 105. Introduction to Statistics for Engineers. (3-0) Cr. 3. F.S. Prereq: Math 165 (or 165H). Statistical concepts with emphasis on engineering applications. Data collection; descriptive statistics; probability distributions and their properties; elements of statistical inference; regression; statistical quality control charts; use of statistical software; team project involving data collection, description and analysis. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 227.
Stat 201. Applied Regression Analysis for Business. (2-0) Cr. 2. F. Prereq: 101 or 104 or 105. Brief review of required descriptive and inferential statistics; statistical process monitoring and applications in quality control; use of computers to analyze data; simple linear regression analysis; multiple regression analysis; diagnostic checking and model building; application of regression techniques to analysis of variance and time series analysis. Credit for both 201 and 227 may not be applied toward graduation.
Stat 227. Introduction to Business Statistics. (4-2) Cr. 5. F.S.SS. Prereq: Math 150 or 165. Obtaining, presenting, and organizing statistical data; measures of location and dispersion; probability concepts; the normal distribution; sampling and sampling distributions; estimation and confidence intervals; statistical process monitoring and applications in quality control; use of computers to analyze data; simple linear regression analysis; multiple regression analysis. Credit for only one of the following courses may be applied toward graduation: 101, 104, 105, 227. Credit for both 201 and 227 may not be applied toward graduation.
Stat 231. Probability and Statistical Inference for Engineers. (4-0) Cr. 4. F.S. Prereq: Credit or enrollment in Math 265. Emphasis on engineering applications. Basic probability; random variables and probability distributions; joint and sampling distributions; propagation of error. Descriptive statistics; confidence intervals; hypothesis testing; simple linear regression; multiple linear regression; one way analysis of variance; use of statistical software.
Stat 305. Engineering Statistics. (3-0) Cr. 3. F.S. Prereq: Math 165 (or 165H). Statistics for engineering problem solving with emphasis on the design and analysis of experiments. Descriptive statistics; elementary probability distributions; principles of experimentation; confidence intervals and significance tests; one-, two-, and many-factor studies; regression analysis; use of statistical software; team project involving multi-factor experimentation and analysis.
Stat 328. Applied Business Statistics. (2-2) Cr. 3. F.S. Prereq: 201 or 227. Application of statistical methods to problems in business and economics; review of multiple regression; residual analysis; model building; analysis of variance; introduction to experimental design concepts; time series analysis and forecasting. Nonmajor graduate credit.
Stat 333. Probability and Statistics for Electrical and Computer Engineers. (3-0) Cr. 3. F.S. Prereq: Math 267. Accelerated and rigorous introduction to probability and statistics. Applications to areas of electrical and computer engineering such as systems, control, signal processing, digital and analog circuits, communications. Discrete and continuous random variables, associated probability models, extensions to random vectors and random processes. Applications to parameter estimation, confidence intervals, hypothesis testing, regression, analysis of variance, spectral estimation. Nonmajor graduate credit.
Stat 341. Introduction to the Theory of Probability and Statistics. (Same as Math 341.) (3-0) Cr. 3. F.S. Prereq: Math 265 (or 265H). Probability; distribution functions and their properties; classical discrete and continuous distributions; moment generating functions. Credit for both 341 and 447 may not be applied toward graduation.
Stat 342. Introduction to the Theory of Probability and Statistics. (Same as Math 342.) (3-0) Cr. 3. S. Prereq: 341, Math 307 or 317. Theory of estimation and tests of hypotheses; regression and correlation; linear model theory; enumerative data.
Stat 361. Quality Control. (Same as I E 361.) See Industrial Engineering. Nonmajor graduate credit.
Stat 398. Cooperative Education. Cr. R. F.S.SS. Prereq: Permission of department head. Off-campus work periods for undergraduate students in a field of statistics.
Stat 401. Statistical Methods for Research Workers. (3-2) Cr. 4. F.S.SS. Prereq: 101 or 104 or 105 or 201 or 227. Graduate students without an equivalent course should contact the department. Methods of analyzing and interpreting experimental and survey data. Statistical concepts and models; estimation; hypothesis tests with continuous and discrete data; simple and multiple linear regression and correlation; introduction to analysis of variance. Nonmajor graduate credit.
Stat 402. Statistical Design and the Analysis of Experiments. (3-0) Cr. 3. F.S. Prereq: 401. The role of statistics in research and the principles of experimental design. Experimental units, randomization, replication, blocking, subdividing and repeatedly measuring experimental units; factorial treatment designs and confounding; extensions of the analysis of variance to cover general crossed and nested classifications and models that include both classificatory and continuous factors. Nonmajor graduate credit.
Stat 403. Nonparametric Statistical Methods. (2-0) Cr. 2. Alt. F., offered 1998. Prereq: 231 or 328 or 401. Groeneveld. Analysis of data when the dependent variable has ordinal or nominal properties; statistical inference for ranked data; Mann-Whitney and Kruskal-Wallis procedures; rank correlation; efficiency of nonparametric procedures and robustness of comparable parametric procedures. Nonmajor graduate credit.
Stat 404. Statistics for the Social Sciences. (2-2) Cr. 3. F. Prereq: 401. Lorenz, Roberts. Applications of generalized linear regression models to social science data. Assumptions of regression; diagnostics and transformations; analysis of variance and covariance; path analysis. Nonmajor graduate credit.
Stat 407. Methods of Multivariate Analysis. (2-0) Cr. 2. F. Prereq: 401, knowledge of matrix algebra. Techniques of analyzing multivariate data including Hotelling's T2, multivariate analysis of variance, principal components, cluster analysis. Nonmajor graduate credit.
Stat 421. Survey Sampling Techniques. (2-2) Cr. 3. S. Prereq: 231 or 328 or 401. Methods of designing and analyzing survey investigations; simple random, stratified, and multistage sampling designs; methods of estimation including ratio and regression; construction and use of sample frames. Nonmajor graduate credit.
Stat 432. Applied Probability Models. (3-0) Cr. 3. Alt. F., offered 1997. Prereq: 231 or 341 or 447. Groeneveld. Probabilistic models in engineering and the physical sciences; probability; Markov chains; Poisson and renewal processes; applications to queuing, scheduling, control, and other quantitative problems. Nonmajor graduate credit.
Stat 436. Quantitative Genetics. (3-0) Cr. 3. S. Prereq: 401. Bailey. Introduction to Quantitative Genetics (QG). Basic concepts of population genetics as they relate to QG. Derivation, definition, and estimation of QG parameters. Genotype by environment interaction. Application of statistical models to the design, analysis, and interpretation of QG experiments. Genetic and statistical implications of natural and artificial selection procedures. Nonmajor graduate credit.
Stat 447. Statistical Theory for Research Workers. (4-0) Cr. 4. S.SS. Prereq: Math 151 and permission of instructor, or Math 265. Amemiya, Yang. Primarily for graduate students not majoring in statistics. Emphasis on aspects of the theory underlying statistical methods. Probability, population distributions and their properties, sampling distributions, point and interval estimation, tests of hypotheses, simple regression. Credit for both 341 and 447 may not be applied toward graduation. Nonmajor graduate credit
Stat 451. Applied Time Series. (3-0) Cr. 3. S. Prereq: 231 or 328 or 401. Meeker. Methods for analyzing data collected over time; review of multiple regression analysis. Elementary forecasting methods: moving averages and exponential smoothing. Autoregressive-moving average (Box-Jenkins) models: identification, estimation, diagnostic checking, and forecasting. Transfer function models and intervention analysis. Nonmajor graduate credit.
Stat 479. Computer Processing of Statistical Data. (3-0) Cr. 3. F. Prereq: 401. Marasinghe. Structure, content and programming aspects of a modern statistical package. Advanced techniques in the use of a statistical software system for data analysis. Introduction to graphical methods in statistics and a matrix programming language. Nonmajor graduate credit.
Stat 480. Statistical Applications of Digital Computers. (3-0) Cr. 3. S. Prereq: 101 or 104 or 105 or credit or enrollment in 401, 341, or 447; programming knowledge in FORTRAN or C. Kennedy, Marasinghe. Techniques of programming for statistical applications. Programming in algorithmic languages. Efficiency and numerical accuracy in algorithms. Introduction to Monte Carlo methods, matrix computations, and numerical methods in statistical computing. Nonmajor graduate credit.
Stat 490. Independent Study. Cr. var. Prereq: 10 credits in statistics. No more than 9 credits in Stat 490 may be counted toward graduation. H: Honors.
Stat 493. Workshop in Statistics. (1-0 or 2-0) Cr. 1 or 2. Off-campus, offered as demand warrants. Prereq: 101 or 104 or 227. Planning, executing, and interpreting experiments by understanding experimental design and utilizing the statistical concepts of linear models. Designed for master of agriculture program only. Nonmajor graduate credit.
Stat 495. Applied Statistics for Industry. (3-0) Cr. 3. F. Prereq: 101 or 104 or 105 or 201 or 227; Math 166 (or 166H). Graduate students without an equivalent course should consult the department. Statistical thinking applied to industrial processes. Assessing, monitoring and improving processes using statistical methods. Analytic/enumerative studies; graphical displays of data; process monitoring; control charts; capability analysis. Nonmajor graduate credit.
Stat 496. Applied Statistics for Industry. (3-0) Cr. 3. S. Prereq: 495. Statistical design and analysis of industrial experiments. Concepts of control, randomization and replication. Simple and multiple regression; factorial and fractional factorial experiments; reliability; analysis of lifetime data. Nonmajor graduate credit.
Stat 500. Statistical Methods. (3-2) Cr. 4. F. Prereq: 101 and credit or enrollment in 579. Hinz, Koehler. Introduction to methods and analyzing data from experiments and surveys. Methods of analysis of variance including cross classifications; correlation; multiple regression; covariance; contingency table analysis. Current computer software utilized in data analyses.
Stat 501. Multivariate Statistical Methods. (3-0) Cr. 3. S. Prereq: 500 or 402; 447 or 542; knowledge of matrix algebra. Cook, Koehler. Statistical methods for analyzing multivariate data: dynamic graphics, Hotelling's T2, multivariate analysis of variance, principal components, factor analysis, canonical correlations, cluster analysis, and classification methods.
Stat 505. Environmental Statistics. (2-2) Cr. 3. Alt. S., offered 1998. Prereq: 341 or 447; 401. Cressie. Basic ideas of statistical modeling for environmental applications; ecotoxicology; limits of detection; spatial statistics; geostatistics, kriging, spatial sampling; hierarchical modeling, components of variance, Bayesian methodology.
Stat 511. Theory and Application of Linear Models. (3-0) Cr. 3. S. Prereq: 500 or 402 or 404; 542 or 447; a course in matrix algebra. Stufken. Standard functional and classificatory models, matrix preliminaries, estimability, intermediate theory of least squares and of best linear unbiased estimation, analysis of variance and covariance, distribution of quadratic forms, variance components.
Stat 512. Design of Experiments. (3-0) Cr. 3. F. Prereq: 511. Stufken. Basic ideas of experimental design with applications; completely randomized, randomized block, and Latin Square designs; randomization analysis; factorial experiments, confounding, fractional replication; split-plot and incomplete block designs; basic ideas of optimal design.
Stat 513. Response Surface Methodology. (3-0) Cr. 3. Alt. S., offered 1998. Prereq: 402 or 512, knowledge of elementary matrix theory. Carriquiry, Stufken. Design criteria and optimality; determination of optimum operating conditions; exploration of response surfaces; robust estimation and transformations; mixture experiments; construction of optimal designs.
Stat 514. Scheduling and Inventory Theory. (Same as I E 514.) See Industrial Engineering.
Stat 515. Theory and Applications of Nonlinear Models. (3-0) Cr. 3. F. Prereq: 447 or 543, 511. Construction of nonlinear statistical models; nonlinear regression models with homogeneous and heterogeneous errors, generalized linear models, two-state random parameter models. Iterative methods for parameter estimation and likelihood-based inference. Assignments include derivation of theoretical results and development of computational algorithms for use with actual data.
Stat 521. Theory of Sample Surveys. (3-0) Cr. 3. S. Prereq: 401; 447 or 542. Breidt, Opsomer. Basic concepts and theory of designing sample surveys for finite populations. Simple random, systematic, stratified, cluster and multistage sampling. General unequal probability designs. Horvitz-Thompson estimation of totals and functions of totals: means, proportions, covariances, regression coefficients. Model-assisted ratio and regression estimation. Two-phase sampling. Variance estimation for complex designs. Nonsampling errors.
Stat 531. Quality Control and Engineering Statistics. (Same as I E 531.) (3-0) Cr. 3., Alt. S., offered 1999. Prereq: 401; 342 or 447. Vardeman. Statistical methods and theory applicable to problems of industrial process monitoring and improvement. Shewhart, CUSUM, and other control charts; feedback control; process capability studies; estimation of product and process characteristics; experimental design and analysis; acceptance sampling, continuous sampling and sequential sampling; economic and decision theoretic arguments in industrial statistics.
Stat 533. Reliability. (Same as I E 533.) (3-0) Cr. 3. Alt. S., offered 1998. Prereq: 342 or 432 or 447. Meeker. Probabilistic modeling and inference in reliability; analysis of systems; Bayesian aspects; product limit estimator, probability plotting, maximum likelihood estimation for censored data, accelerated failure time and proportional hazards regression models with applications to accelerated life testing; repairable system data; planning studies to obtain reliability data.
Stat 534. Ecological Statistics. (2-0) Cr. 2. Alt. S., offered 1999. Prereq: 447 or 542. Pollak. Models of population growth; growth of populations with two competing species; parasite-host and predator-prey relationships; elementary population genetics; selection, mutation, and migration; spatial patterns in populations with one or more species; diversity; information theory.
Stat 535. Biological Statistics. (Same as Tox 535.) (2-0) Cr. 2. Alt. S., offered 1999. Prereq: 401 or 500. Estimations from standard curves. Sigmoidal dose-response curves. Design and analysis of direct, parallel line, slope-ratio and quantal response assays. Immunoassays. Fitting the Michaelis-Menten equation and the examination of other biostatistical problems according to student interests.
Stat 536. Genetic Statistics. (Same as Gen 536.) (2-0) Cr. 2. Alt. F., offered 1997. Prereq: 401, 447; Gen 320 or Biol 301 and permission of instructor. Pollak. Probability applied to genetic systems; random mating; selection and mutation; theory of inbreeding; some effects of finite population size.
Stat 537. Genetic Statistics. (Same as Gen 537.) (2-0) Cr. 2. Alt. S., offered 1998. Prereq: 536. Pollak. Models for quantitative inheritance; partition of genotypic variance; covariances among relatives with random mating; experimental designs for evaluating parameters; phenotypic selection for quantitative traits; response to artificial selection.
Stat 538. Econometric Statistics. (Same as Econ 538.) (3-0) Cr. 3. F. Prereq: 447. Bilias, Fuller. Generalized linear regression, nonlinear regression, measurement error models. Simultaneous equation systems, regression equations with autoregressive errors, large sample theory.
Stat 539. Game Theory. (Same as Econ 539, I E 539.) (3-0) Cr. 3. F. Prereq: 341 or 432 or 447. H. T. David. Zero-sum and bi-matrix non-cooperative two person games; games of timing; relation to mathematical programming; cooperative n-person games.
Stat 542. Theory of Probability and Statistics. (3-0) Cr. 3. F. Prereq: 341; Math 414 or 465. Sample spaces, events, probability, expectation, moments, inequalities, conditional probability, common distributions, moment generating and characteristic functions, elementary limit theorems, order statistics, sampling distributions, multivariate normal distribution.
Stat 543. Theory of Probability and Statistics. (3-0) Cr. 3. S. Prereq: 542. Point estimation, sufficiency, completeness, exponential family, confidence intervals, Neyman-Pearson lemma, UMP tests, likelihood ratio tests, sequential testing, Bayes estimation, and nonparametric inference.
Stat 544. Bayesian Statistics. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 543. Specification of probability models; conjugate priors and noninformative priors; hierarchical models; techniques for obtaining posterior distributions; model checking; comparison of Bayesian and classical methods; empirical Bayes procedures; decision theory.
Stat 546. Theory of Nonparametric and Asymptotic Methods. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 542. Sukhatme. Introduction to nonparametric problems; tests based upon sample distribution functions, rank tests for location, scale and independence; local properties of rank tests; convergence of a sequence of random variables; limit theorems; asymptotic distributions of sample quantiles, U-statistics, rank statistics, chi-square and other goodness of fit test statistics; asymptotic efficiency of tests.
Stat 551. Time Series Analysis. (3-0) Cr. 3. F. Prereq: 447 or 542. Stationary and non-stationary time series; covariance and spectral properties of stationary time series; autoregressive moving average processes; best linear prediction; state space models and Kalman recursions; estimation techniques, model-building and diagnostics.
Stat 554. Introduction to Stochastic Processes. (Same as Math 554.) See Mathematics.
Stat 555. Theory of Stochastic Processes. (Same as Math 555.) See Mathematics.
Stat 557. Statistical Methods for Counts and Proportions. (3-0) Cr. 3. Alt. F., offered 1998. Prereq: 500 or 401; 542 or 447. Koehler. Statistical methods for analyzing categorical responses: chi-square tests, measures of association and relative risk, log-linear and logistic regression models, generalized linear models, methods for clustered responses and repeated measures. Asymptotic properties of estimators.
Stat 579. Introduction to Computer Hardware and Software Systems for Statistical Computing. (1-0) Cr. 1. F. Prereq: Graduate classification in statistics. Kennedy, Marasinghe. Designed to introduce students to the languages and conventions required for the use of the leading software systems in statistical computing. Offered on a satisfactory-fail grading basis only.
Stat 580. Statistical Computing. (3-0) Cr. 3. F. Prereq: 500, 542 and knowledge of a scientific programming language. Kennedy, Marasinghe. Seminumerical, numerical, and nonnumerical methods used in statistical computing. Application areas discussed include probability function approximation, simulation, and optimization methods in linear and nonlinear estimation.
Stat 581. Computational Methods in Statistics. (3-0) Cr. 3. Alt. SS., offered 1999. Prereq: 511, 543 and knowledge of a scientific programming language. Marasinghe. Applications of matrix computations, optimization methods, stochastic simulation, iterative techniques and selected numerical methods in statistical research. These include maximum likelihood estimation, Monte Carlo methods, non-linear regression and robust regression computations, resampling algorithms and other computer intensive methods. Programming assignments include applications of these algorithms to real or realistic problems.
Stat 590. Special Topics. Cr. var. A. Theory. B. Methods. C. Design of Experiments. D. Design of Surveys.
Stat 599. Creative Component.
Stat 601. Advanced Statistical Methods. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 500; Math 514. Kaiser, coordinator. Topics selected from areas of current development and importance in statistical methods. Recent topics have included construction of general models from specification of conditional distributions, Bayesian hierarchical models, Markov Chain Monte Carlo methods, and scatterplot smoothing. Assignments include solution of computationally intensive problems and written reports that cover all aspects of problem definition, model formulation, and synthesis of results. May be presented in several modules by various faculty.
Stat 606. Spatial Statistics. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 511, 543. Cressie. General spatial models; spatial data analysis; continuous spatial variation, geostatistics, kriging; lattice data, conditional models, joint models; image analysis; point patterns, randomness, clustering, random sets.
Stat 611. Advanced Linear Model Theory. (3-0) Cr. 3. F. Prereq: 511, 543, a course in matrix algebra. Advanced theory of least squares and best linear unbiased estimation, non-central chi-square and F distributions, distribution of linear and quadratic forms, F test, confidence regions, extensions of best linear unbiased estimation theory to mixed and random models and to non-standard settings, biased estimation, recursive estimation, inference for variance components.
Stat 612. Advanced Design of Experiments. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 512. Stufken. Design optimality criteria and optimal designs; Galois fields and finite geometries with applications to design construction; fractional factorial designs; theory of approximate designs and the equivalence theorem; changeover designs with applications.
Stat 621. Advanced Theory of Survey Sampling. (3-0) Cr. 3. Alt. S., offered 1999. Prereq: 521. Breidt. Advanced topics of current interest in design of surveys and analysis of survey data; unequal probability sampling with and without replacement; criteria for choice of survey strategies including sufficiency, likelihood, and admissibility; super population models and their role in choice of optimal strategies; review of recent literature.
Stat 642. Advanced Probability Theory. (3-0) Cr. 3. S. Prereq: 542, Math 514. Athreya, Lahiri. Probability spaces; Kolmogorov's existence theorem for stochastic processes; expectation and moments of random variables; Jensen's inequality and applications; Borel-Cantelli lemmas; weak and strong laws of large numbers; convergence of moments; weak convergence of probability distributions; characteristic functions; continuity theorem; Lindeberg-Feller central limit theorem and its ramifications; Poisson convergence; conditional expectation and probability; discrete time martingales.
Stat 643. Theory of Estimation and Testing of Hypotheses. (3-0) Cr. 3. F. Prereq: 543, 642. Cressie, Lahiri. Asymptotic theory of maximum likelihood estimation; elements of decision theory; sufficiency; unbiased estimation; Neyman-Pearson theory of testing hypotheses; UMP unbiased tests; Neyman structure; invariance.
Stat 645. Order Statistics. (3-0) Cr. 3. Alt. F., offered 1997. Prereq: 543. Distribution theory and moments of order statistics; estimation of location and scale parameters; censoring; robust estimation; treatment of outliers; asymptotic distributions of quantiles, extremes, and linear functions of order statistics.
Stat 647. Multivariate Analysis. (3-0) Cr. 3. F. Prereq: 543, knowledge of matrix algebra. Amemiya. Multivariate normal distribution, Wishart distribution, multiple, partial, and canonical correlations, inference for mean vector, multivariate regression, principal components, discriminant analysis, factor analysis, covariance structure analysis, latent variable modeling.
Stat 648. Seminar on Theory of Statistics and Probability. Cr. var. Prereq: 543.
Stat 651. Time Series. (3-0) Cr. 3. Alt. S., offered 1998. Prereq: 642. Fuller. Covariance and spectral representation of time series. Stationary and nonstationary autoregressive models. Fourier and periodogram analyses. Stochastic difference equations. Estimation and distribution theory.
Stat 680. Advanced Statistical Computing. (3-0) Cr. 3. S. Prereq: 580. Cook, Kennedy. Selected methods and algorithms in selected areas of statistical computing. Emphasis on the most recent advances in these and other areas supported by statistical computing.
Stat 699. Research.