STA-STATISTICS

STA 1013 Seeing Through Statistics . . . . . 3(3,0,0)
Covers some fundamental concepts of statistics, including descriptive measures, randomness, estimation of proportions, central tendency, rare event principle, association vs. causation, and risks. Intended for students who are not planning to take further courses in statistics. Credit toward Gordon Rule and graduation may be earned in STA 1013 or STA 2023 but not both. (Gordon Rule Course: Applied Math) and (General Studies Course: MAT/MO) Prerequisite: MAC 1105 or equivalent.

STA 2023 Elements of Statistics . . . . . 3(3,0,0)
Fundamental statistical concepts. Probability, inference, estimation, hypothesis testing. (Gordon Rule Course: Applied Math) and (General Studies Course: MAT/MO) Prerequisite: MAC 1105.

STA 3134 Quantitative Methods for Business . . . . . 3(3,0,0)
Linear programming, simple regression analysis, introduction to time series. (Gordon Rule: Applied Math) Prerequisite: STA 2023; MAC 1105 or MAC 1140.

STA 3162C Applied Statistics . . . . . 4(3,2,0)
Inferential statistics from an applied point of view. Probability and sampling distributions, confidence intervals and hypothesis testing, ANOVA, correlation, simple and multiple linear regression. SAS computer techniques. Lab required. (Gordon Rule Course: Applied Math) Prerequisite: MAC 2311.

STA 4173 Biostatistics . . . . . 3(3,0,0)
A second course in statistics for students in the Biological Sciences. Topics covered include analysis of variance, regression analysis, nonparametric statistics, contingency tables. (Gordon Rule Course: Applied Math) Prerequisite: STA 2023 or equivalent.

STA 4222 Design of Sample Surveys . . . . . 3(3,0,0)
Basic designs in sample surveys; simple and stratified sampling. Cluster and multi stage sampling. Methods of estimation. (Gordon Rule Course: Applied Math) Prerequisite: STA 2023 or STA 4321.

STA 4321 Introduction to Mathematical Statistics I . . . . . 3(3,0,0)
Probability, distributions of random variables, conditional probability and stochastic independence, distribution of functions of random variables, limiting distributions, multivariate probability distributions. (Gordon Rule Course: Applied Math). Prerequisite: MAC 2313 or equivalent.

STA 4322 Introduction to Mathematical Statistics II . . . . . 3(3,0,0)
Point and interval estimates, measures of quality of estimates, Bayesian estimates, robust estimation, statistical hypothesis testing, including goodness of fit, contingency tables and ANOVA, SPR test, the Cramer-Rao inequality, multiple comparisons, completeness, distributions of quadratic forms, multivariate normal distributions. Offered concurrently with STA 5326; graduate students will be assigned additional work. (Gordon Rule Course: Applied Math) Prerequisite: STA 4321.

STA 5136 Applied Statistics for the M.B.A. . . . . . 3(3,0,0)
Simple and multiple regression, introduction to ANOVA, Bayes decision theory, topics in time series analysis, index numbers. Prerequisite: STA 2023 or equivalent.

STA 5166 Special Topics in Statistics . . . . . 3(3,0,0)
Introduction to one- and two-way ANOVA; nonparametric methods, correlation and linear regression analysis. Introduction to SAS. Prerequisite: STA 2023 or equivalent.

STA 5206 Analysis of Variance . . . . . 3(3,0,0)
Statistical methods useful in design and analysis of experiments in physical, biological and social sciences. Analysis of variance including randomized blocks. Latin square, factorial arrangements, regression. Prerequisite: STA 2023 or STA 3162C.

STA 5207 Applied Regression Analysis . . . . . 3(3,0,0)
Regression analysis, simple and multiple; procedures for selection of a best set of regressors. Prerequisite: STA 2023 or STA 3162C

STA 5326 Mathematical Statistics . . . . . 3(3,0,0)
Point and interval estimates, measures of quality of estimates, Bayesian estimates, robust estimation, statistical hypothesis testing, including goodness of fit, contingency tables and ANOVA, SPR test, the Cramer-Rao inequality, multiple comparisons, completeness, distributions of quadratic forms, multivariate normal distributions. Offered concurrently with STA 4322; graduate students will be assigned additional work. Prerequisite: STA 4321.

STA 5825 Probabilistic Methods in System Analysis . . . . . 3(3,0,0)
Introduction to stochastic processes and the response of linear dynamic systems to such processes. Topics covered are single variate and multivariate stochastic processes, the time and ensemble properties of stochastic processes, analysis of processes by correlation techniques and the stochastic response characteristics of linear systems. Multivariate estimation theory is also considered. Prerequisite: STA 4321.

STA 6246 Design and Analysis of Experiments . . . . . 3(3,0,0)
Further concepts in design and analysis of planned experiments with emphasis on confounding and fractional replications of factorial experiments; composite designs; incomplete block designs; estimation of variance components. Prerequisite: STA 5206.

STA 6507 Nonparametric Statistics . . . . . 3(3,0,0)
Extensive coverage of goodness-of-fit tests, location problems, association analysis and general nonparametric topics. Prerequisite: STA 4321 and STA 3162C or STA 2023.

STA 6607 Operations Research I . . . . . 3(3,0,0)
Mathematical probability models and distributions; linear programming models; the simplex method; duality and sensitivity analysis; inventory models; queuing theory; simulation. Prerequisite: STA 4321 and MAS 3105 or MAS 5107.

STA 6608 Operations Research II . . . . . 3(3,0,0)
Decision theory and games, PERT/CPM, Markovian decision process, integer programming, dynamic programming, reliability and maintenance. Prerequisite: STA 6607.

STA 6666 Statistical Quality Control I . . . . . 3(3,0,0)
Procedures used in acceptance sampling and statistical process control are based on concepts and theory from probability and statistics. Introduces the applications of these procedures, investigates them from the standpoint of their statistical properties and develops the methodology for construction, evaluation and comparison of procedures. Prerequisite: STA 2023 or STA 3162C and STA 4321.

STA 6667 Statistical Quality Control II . . . . . 3(3,0,0)
Develops the methodology for construction, evaluation, and comparison of advanced process control procedures using Markov chains and probability theory. Computer simulations will be used in individual projects to study the performance of specific process control procedures from the current literature. Prerequisite: STA 6666.

STA 6707 Multivariate Methods . . . . . 3(3,0,0)
Multivariate extensions of Chi-Square and t-tests: discrimination and classification procedures: applications to diagnostic problems in biological, medical, anthropological and social research: multivariate analysis of variance: factor analysis and principle components analysis. Prerequisites: STA 4321, STA 5206, STA 5207.

STA 6826 Stochastic Systems . . . . . 3(3,0,0)
General theory of uncertainties in linear systems, estimation and control as applied to linear stochastic systems. Introduction to Kalman filtering and smoothing techniques. Prerequisite: STA 5825.

STA 6857 Time Series . . . . . 3(3,0,0)
Box-Jenkins procedure applied to identification, estimation and verification of the time series processes. Prerequisite: MAA 4212 and STA 5207.

STA 6930 Proseminar in Statistics . . . . . 1(0,0,1)
Each M.A. candidate (except those who choose the thesis option), shall, under the direction of a project advisor, independently investigate a topic or topics in mathematics/statistics or mathematics education through the study of journal articles or other appropriate sources. The candidate shall submit a formal written report and make an oral presentation of the results of his/her investigations. The goal of the proseminar is to provide students an opportunity to integrate the total experience gained during their graduate training. Graded on satisfactory/unsatisfactory basis only. MA candidacy and permission of the instructor.

STA 6971 Thesis . . . . . 1-6(VARIABLE)
Graded on satisfactory/unsatisfactory basis only. Permission of instructor is required.