# Courses

#### Statistics (FSBM)

0403. Introduction to Biostatistics (3 s.h.)

Topics cover statistical methods and concepts with special emphasis on applications in health and biological sciences.

0500. Quantitative Methods for Business (3 s.h.)

This course is designed to introduce you to contemporary elementary applied statistics and to provide you with an appreciation for the uses of statistics in business, economics, everyday life, as well as hands-on capabilities needed in your later coursework and

professional employment.

0501. 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.

0502. Probability and Statistics Theory II (3 s.h.)

Prerequisite: 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.

0503. Statistical Methods I. (3 s.h.)

Prerequisite: Basic statistics for permission of instructor

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

##### 0504. 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 S+ are used.

##### 0509. Introduction to Stochastic Models (3 s.h.)

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

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0510. Measure Theory and Probability (3 s.h.)

Prerequisite: advanced calculus.

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

0511. 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.

##### 0515. 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.

0518. 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.

##### 0521. 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.

0522. Design of Experiments I. (3 s.h.)

Prerequisite: 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.

0531. 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, influentialobservations. Prediction.

##### 0533. Applied Multivariate Analysis I. (3 s.h.)

Prerequisite: Stat. 504 and 515, or permission of instructor.

Multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector anddispersion 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.

0550. 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.

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0551. Regression, Time Series, and Forecasting for Business Applications (3 s.h.)

Prerequisite: Stat. 500 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.

##### 0554. Survey Techniques for Business Applications (3 s.h.)

Prerequisite: Stat. 500 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.

0555. Statistical Methods for Business Research I (3 s.h.)

Prerequisite: Statistics 500, CIS 401, or equivalent. Students must pass a placement examination to enter the course..

Doctoral level, one-year sequence of courses for those students in Business Administration program. Covers a variety of advanced statistical methods useful in business research, including the logistic regression, path analysis, factor analysis, discriminant analysis, log-linear models, and clustering techniques. Emphasis on rationales, assumptions, techniques, and interpretation of results from computer packages. Relevant mathematical results will be presented, but proofs or abstract arguments shall be avoided. The students are required to work with SAS (or equivalent packages) throughout the year. Experience in working with large databases will be acquired through an arrangement of working with each student's own field adviser.

0556. Statistical Methods for Business Research II. (3 s.h.)

Prerequisite: Statistics 555 or equivalent. Students must pass a placement examination to enter the course.

Continuation of Stat. 0555.

0571. 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.

0572. 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.

0575. 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.

0578. Introduction of Biometry (3 s.h.)

Prerequisites: Stat. 502 or 504 or permission of instructor.

Presents the theory of biometry and explores its many applications. Topics include common survival distributions, modes of censoring, model testing, life table analysis, Kaplan-Meier estimation procedures and bioassay.

##### 0580. Pharmaceutical Statistics I. (3 s.h.)

Prerequisite: Stat. 501-502, or equivalent.

Introduction to certain statistical problems associated with pharmaceutical drug development. Topics include the bioequivalence problems, population PK/PD modeling, and analysis of combination drug trials. Research articles of current interest will be discussed.

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0581. Statistical Computing (3 s.h.)

Prerequisites: Stat. 504 and CIS 401 or permission of instructor.

Use of computers in the solution of statistical problems. Topics include: floating point architecture, random number generation, design of statistical software, computational linear algebra, numerical integration, optimization methods.

##### 0590. Advanced SAS Programming (3 s.h.)

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0598. Independent Study (1-6 s.h.)

Prerequisite: approval of the department.

Special study in a particular aspect of statistics under thedirect supervision of an appropriate graduate faculty member. No more than six semester hours of independent study may be counted toward degree requirements.

0599. Independent Study (1-6 s.h.)

Prerequisite: approval of the department.

Special study in a particular aspect of statistics under thedirect supervision of an appropriate graduate faculty member. No more than six semester hours of independent study may be counted toward degree requirements.

##### 0601. Advanced Statistical Inference I. (3 s.h.)

Prerequisite: Stat. 502.

Random variables, inequalities, convergence types, Central Limit Theorem, integration and expectation, conditional probability and expectation, UMVU estimator, Bayes and Minimax estimators.

0602. Advanced Statistical Inference II (3 s.h.)

Prerequisite: Stat. 601.

Hypothesis testing and confidence sets, Neyman-Pearson Lemma, similar and unbiased tests, likelihood ratio tests.

0618. Time Series Analysis II. (3 s.h.)

Prerequisite: Stat. 518 or permission of instructor.

Advanced topics on modern time series analysis from the time domain and/or frequency domain perspective. Emphasis will be placed on the analysis of multiple time series.

##### 0621. Linear Models II. (3 s.h.)

Prerequisite: Stat. 521 or permission of instructor.

Continuation of Stat. 521, principally devoted to analysis of mixed models and inference concerning variance components. Topics covered include calculation of expected mean squares, estimation by Henderson's Methods, ML, REML, MINQUE, MIVQUE. Dispersion Mean Model. Computational algorithms and software.

0622. Design of Experiments II. (3 s.h.)

Prerequisite: Stat. 522 or permission of instructor.

Covers symmetric and asymmetrical factorial experiments, fractional replication, split plot design, balanced and partially balanced incomplete block designs without and with recovery of interblock information and lattice designs.

0633. Multivariate Analysis II (3 s.h.)

Prerequisite: Stat. 502 and 533 or permission of instructor.

A study of specialized topics in multivariate analysis.

0701. Seminar in New Topics in Statistics (3 s.h.)

Prerequisite: permission of instructor.

Special topics in Statistics

0702. Seminar in New Topics in Statistics (3 s.h.)

Prerequisite: permission of instructor.

Special topics in Statistics

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0799. Directed Study in Statistics (variable credit)

Prerequisite: departmental permission.

Preparation for preliminary examinations.

0800. Statistical Aanalysis for Management(3 s.h.)

In this course, you'll learn how to use statistics to help solve business problems throughout an enterprise. You'll examine case examples of statistical analysis in areas such as marketing, finance and management. You'll learn descriptive and inferential techniques such as regression analysis and how to analyze data and reach decisions, using statistical computer software and Excel.

##### 0802. Quantitative Techniques for Management(3 s.h.)

Prerequisite: Limited to students matriculated in the Executive M.B.A. program.

In this course you'll apply advanced quantitative techniques for managerial decision-making such as forecasting, linear programming, simulation, decision analysis, Markov chains and game theory. You'll use customized software and Excel to analyze these models extensively and apply them to decisions regarding resource allocation and other managerial problems.

0896. Directed Study in Statistics (variable credit)

Prerequisite: departmental permission.

##### 0899. Directed Study in Statistics (variable credit)

Prerequisite: departmental permission.

##### 0999. Dissertation Research (1-12 s.h.)

Prerequisite: departmental approval and elevation to candidacy.