Graduate Bulletin-School of Business and Management

Descriptive statistics, probability distributions, estimation and testing problems involving one or two populations, multiple regression analysis using statistical software, analysis of variance, analysis of contingency tables.

403. Introduction to Biostatistics. (3 s.h.)

Topics cover methods and concepts of Statistics 402, with special emphasis on applications in health and biological sciences. May be used to satisfy M.B.A. and M.S. requirement for Stat. 402.

478. Statistics for Experimenters. (3 s.h.)

Basic statistics for students studying a laboratory science. Data summarization, introduction to probability, probability distributions, estimation, tests of hypotheses, laboratory quality control, survey design and clinical trials, analysis of count data, principles of analysis of variance, and regression analysis. Students are encouraged to register for an optional one-credit statistical computing laboratory in which the MINITAB statistical software package will be used to explore and analyze data.

Note: You must complete the 400-level core for all M.B.A./M.S. programs before taking any 500-level courses.

501. Probability and Statistics Theory I. (3 s.h.)

Prerequisite: calculus.

Topics include basic probability theory and combinatorial problems, generating functions, random variables, probability distributions, law of large numbers, and limit theorems.

502. Probability and Statistics Theory II. (3 s.h.)

Prerequisite: calculus and Stat. 501.

A comprehensive development of the theory of statistics, including standard distributions, sampling distributions, general theory of estimation, testing of hypotheses, statistical decision theory, order statistics, linear statistical estimation.

503. Statistical Methods I. (3 s.h.)

Prerequisite: Stat. 402 or permission of instructor.

Introduction to frequently used methods. Includes probability, estimation, tests of hypothesis, survey sampling, linear regression data analysis, statistical computer packages.

504. Statistical Methods II. (3 s.h.)

Prerequisite: Stat. 503.

Introduction to multiple regression, analysis of variance, design of experiments, robust techniques, analysis of covariance, nonparametric analysis, and multivariate analysis. Statistical packages such as SAS and MINITAB are used.

509. Introduction to Stochastic Models. (3 s.h.)

Applicants of probability and simple stochastic processes, including Markov Chains and Poisson processes, to problems such as queueing, inventory, and reliability.

510. Measure Theory and Probability. (3 s.h.)

Prerequisite: advanced calculus.

Introduction to measure theory, probability theory, Lebesque integral, conditional probability.

511. Sampling Theory. (3 s.h.)

Prerequisite: Stat. 503 or permission of instructor.

Theory and application of sampling from finite populations. Topics include random, stratified, cluster, and systematic sampling; estimation of means and variances; optimal allocation of resources; problems of nonsampling errors; and ratio and regression estimation.

515. Matrix Theory for Statistics. (3 s.h.)

Prerequisite: undergraduate linear algebra or permission of instructor.

Vector spaces; linear independence of vectors and basis; matrices and algebraic operations on matrices; determinants; rank of a matrix; inverse of nonsingular matrices; linear equations and their solutions; generalized inverse of a matrix; eigen values and vectors of matrices; diagonalization theorems; quadratic forms and their reduction to sum of squares; Jacobians.

518. Time Series Analysis I. (3 s.h.)

Prerequisite: Stat. 502 or permission of instructor.

Theory and application of time series models illustrated with forecasting problems. Considers spectrum analysis, autocorrelation analysis, stationary and nonstationary linear processes, ARMA and ARIMA models, seasonal time series and related materials.

521. Linear Models I. (3 s.h.)

Prerequisite: Stat. 502 and Stat. 515 or permission of instructor.

Theory and analysis of the overparameterized and cell means forms of the linear statistical model that underlies designed experiments. Emphasis on the "messy data" situations having disproportionate cell sizes, empty cells and disconnectedness. Use of SAS-GLM software to analyze about 10 real data sets.

522. Design of Experiments I. (3 s.h.)

Prerequisites: Stat. 504 or permission of instructor.

Principles of experimental designs, completely randomized designs, multiple comparisons, randomized block design, latin square design, missing value problems, analysis of covariance, and factorial experiments.

531. Applied Linear Regression. (3 s.h.)

Prerequisite: Stat. 504.

Simple linear regression. Multiple regression. Weighted least squares, generalized least squares. Polynomial regression. Dummy variables, comparing regression lines. Subset selection procedures. Residual analysis, influential observations. Prediction.

533. Applied Multivariate Analysis I. (3 s.h.)

Prerequisites: Stat. 504, 515, or permission of instructor.

Multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector and dispersion matrix; correlation, partial correlation, and multiple correlation coefficients; Hotelling's T2; MANOVA; discriminant function; repeated measurements analysis; principal components and canonical correlation; factor analysis; and multidimensional scaling.

550. Probability and Statistics Theory for Business Applications. (3 s.h.)

Prerequisite: calculus.

Topics covered: probability, density functions, moments, transformation of variables, common distributions, estimation and tests of hypothesis with emphasis on business applications.

551. Regression, Time Series, and Forecasting for Business Applications. (3 s.h.)

Prerequisite: Stat. 402 or permission of instructor.

Application oriented. Standard statistical packages such as SAS, MINITAB, or SPSS are introduced and extensively used in the course. Topics include regression analysis, time series analysis, and forecasting.

554. Survey Techniques for Business Applications. (3 s.h.)

Prerequisite: Stat. 402 or permission of instructor.

Application oriented. A course dealing with statistical and nonstatistical aspects of organizing a sample survey. Included are discussions of objectives, measurement, sample selection, pilot testing, data collection, data editing, summarization and interpretation of results in addition to describing the various sampling schemes. Students may be required to plan and execute a survey.

571. Nonparametric Methods. (3 s.h.)

Prerequisite: Stat. 502 or permission of instructor.

A thorough course in nonparametric statistics. Estimation and testing of hypothesis when the function form of the population distribution function is not completely specified.

572. Categorical Data Analysis. (3 s.h.)

Prerequisite: Stat. 502 or permission of instructor.

Sampling models for discrete data; Fisher's exact test; Gart's test for residual effects; measures of association; log-linear models; iterative proportion fitting; collapsing multidimensional tables; conditional independence, and logistic regression.

575. Clinical Trials. (3 s.h.)

Prerequisite: Stat. 502 or permission of instructor.

Introduction to the special problems associated with medical trials on humans. Topics include the debate concerning randomization, use of historical data, ethics of experimentation, decision theoretic approach to clinical trials, and applications of sequential and adaptive sampling techniques. Discussion of research articles.

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