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.

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.

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.

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

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.

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

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.

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

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

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.

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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.

Acquaints the student with applications of statistical theory. Techniques and solutions for a broad spectrum of real-world problems. Statistical computing emphasized.

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.

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.

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

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

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.

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.

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.

A study of specialized topics in multivariate analysis.

Presents the theory of biometry and explores its many applications. Topics are taken from clinical trials, bioassay and survival analysis. Topics discussed include the importance of randomization, parallel assays, censored data, proportional hazard models, and rank tests.

Special topics in Statistics

Special topics in Statistics

Preparation for preliminary examinations.

Introduces descriptive statistical tools which are commonlyused in business and decision making. Basic probability concepts are covered and are employed using statistical inference methods. Estimation, testing of hypotheses and applications. Statistical package "Minitab" will be used as a tool for not only computations but also for understanding statistical concepts. The course will be rigorous in statistical concepts.

Study advanced statistical techniques for managerial decision making, including the theory and application of regression, time series and forecasting, categorical data analysis, and quality improvement techniques. Statistical packages, business data sets, and case studies are used.