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USA Undergraduate/Graduate Bulletin 2014-2015

STATISTICS (ST)

ST 150 Contemporary Mathematics 1 cr

and Statistics Seminar  

This course gives an overview of modern mathematics and statistics from the point of view of the practitioners. The course is designed for majors in mathematics and statistics at all levels as well as those students who are considering mathematics and statistics as a major or minor area of study. Topics usually included are elements of geometry, algebra, analysis, methods of statistical inference, the role of the computer in the analytical sciences; these topics vary from semester to semester. This course cannot be taken for credit simultaneously with MA 150, but may be repeated in different semesters.
NOTE: May be offered for Honors Credit.

ST 210 Statistical Reasoning and Applications (C) 3 cr

An introduction to modern statistics designed to provide the student with a solid foundation in statistical concepts, reasoning and applications. Emphasis given to problem identification, methodology selection and interpretation of results. Analysis of data accomplished by extensive use of statistical computer software, thereby minimizing manual computation. Coverage includes descriptive statistics, probability models, estimation, and hypothesis testing. Pre-requisite: High School level algebra is recommended. Computer Lab fee.

NOTE: ST 210 is intended for students in all disciplines except Engineering and Computer Science. Credit for both ST 210 and ST 315 not allowed.
NOTE: May be offered for Honors Credit.
ST 305 Applied Statistics for Health Sciences (C) 3 cr
An introduction to statistical reasoning and data analysis for the health sciences. Coverage includes descriptive statistics, methods of data collection, estimation, hypothesis testing, non-parametric statistics, ANOVA, repeated measures, correlation and other measures of association. Critique of selected research articles and case studies incorporating research and evidence-based practice will be adopted to connect statistics to daily work in healthcare field. Statistical computer software (e.g. Minitab) will be extensively used for data analysis. Pre-requisite: MA 110. computer lab fee.
Note: This course is offered only as a fully online course and only for health sciences students.

ST 310 Statistical Research Techniques 3 cr

Continuation of ST 210 providing a more rigorous treatment of methodologies introduced in ST 210. Additional coverage will be given to experimental design, analysis of variance (ANOVA), regression, model building, nonparametric techniques, contingency table analysis, sampling and survey methods, time series analysis and statistical simulations. Statistical computer software will be extensively used for data analysis. Pre-requisite: C or better in ST 210. Computer Lab fee.
NOTE: Credit for only ONE course from ST 310, ST 315 and ST 320 is allowed.

ST 315 Applied Probability and Statistics 3 cr

Concepts of probability theory, discrete and continuous probability distributions including gamma, beta, exponential and Weibull, descriptive statistics, sampling, estimation, confidence intervals, testing of hypothesis, ANOVA and multiple comparisons, linear and multiple regression, correlation, nonparametric analysis, contingency table analysis, computer-assisted data analysis using appropriate statistical software. Pre-requisite: C or better in MA 125. Computer Lab fee.

ST 320 Applied Statistical Analysis 4 cr

Descriptive statistics, probability distributions, sampling, estimation, confidence intervals and hypothesis testing, experimental designs, ANOVA and multiple comparisons, linear and multiple regression, correlation, nonparametric analysis, goodness of fit, contingency table analysis, quality control, acceptance sampling, computer-assisted data analysis using appropriate statistical software. Pre-requisite: MA 125. Computer Lab fee.
NOTE: ST 315 and ST 320 are intended for students in Engineering, Computer Science, and Mathematics. ST 315 covers additional probability distributions while ST 320 additionally covers concepts of quality control and acceptance sampling. Students in these disciplines should consult with their academic advisor for appropriate choice between ST 315 and ST 320.
NOTE: Credit for only ONE course from ST 310, ST 315 and ST 320 is allowed.

ST 335 Applied Regression Analysis 3 cr

Simple, polynomial and multiple linear regression; residual and lack-of-fit analysis; simple, multiple, partial and multiple-partial correlation analysis; model building algorithms, dummy variables; analysis of covariance; model comparisons; analysis of experimental designs including messy data; nonlinear regression models; computer-assisted data analysis using appropriate statistical software. Pre-requisite: C or better in ST 210 or ST 315 or ST 320. Computer Lab fee.

ST 340

Design and Analysis of Experiments 3 cr

Principles, constructions, and analysis of experimental designs to include completely randomized, randomized complete block, Latin square and split plot designs, factorial experiments, designs with nested and/or crossed factors, multifactor experiments with randomization restrictions, transformations, incomplete block designs, multiple comparisons including contrasts, confounding, fractional replication, computer-assisted data analysis. Pre-requisite: C or better in ST 210 or ST 315 or ST 320. Computer Lab fee.

ST 345 Sampling and Survey Techniques 3 cr

Sampling concepts and designs for survey investigations; sampling methodologies including applications of simple random, stratified, one-and two-stage cluster, and systematic sampling; sample size determination; ratio and regression estimation; population size estimation; random response modeling; acceptance sampling including applications of single and multiple 2-class attribute sampling plans; computer-assisted data analysis using appropriate statistical software. Pre-requisite: C or better in ST 210 or ST 315 or ST320. Computer Lab fee.

ST 350 Applied Time Series Analysis 3 cr

Fundamentals concepts; classical regression models as forecasting models, exponential smoothings, stationary and nonstationary models, additive and multiplicative decompositions, moving average, autoregressive, ARMA and ARIMA processes, estimation in MA, AR, ARMA and ARIMA processes. Box-Jenkins methodology, computer aided modeling, and applications. Pre-requisite: ST 310 or ST 315 or ST 320 or ST 335. Computer Lab fee.

ST 355 Nonparametric Statistical Methods 3 cr

Distribution-free analysis of location and scale measures, non-parametric treatment of fundamental statistical designs, nonparametric comparison procedures, association and contingency table analysis, nonparametric goodness-of-fit procedures, and tests for randomness, nonparametric regression and other measures of association, computer intensive statistical methods. Pre-requisite: ST 210 or ST 315 or ST 320. Computer Lab fee.

ST 415 Statistical Quality Control and Reliability 3 cr

Probability distributions in quality control, inferences about process quality, control charts for attributes and variables, process capability analysis, economic design of control charts, custom charts, acceptance sampling by attributes and variables, six sigma concepts, reliability concepts, censoring, definitions and properties of survival distributions, methods of estimating and comparing reliability distributions, Kaplan-Meier estimation, burn-in models with a major emphasis on computer-assisted data analysis. Pre-requisite: Any 300 level ST course. Computer Lab fee.

ST 425 Applied Linear Models 3 cr

Some results of matrix algebra, multivariate normal distributions, distributions of quadratic forms, general linear models, design models with one factor and two factors including interaction, component-of-variance models, and computing techniques. Pre-requisite: MA 237 and ST 335 or ST 340. Computer Lab fee.

ST 450 Categorical Data Analysis 3 cr

Analysis of two-way, three-way and higher dimensional contingency tables using log-linear models, measures of association for nominal and ordinal tables, multiple-factor models, multiple response models, logistic regression, and weighted least squares. Pre-requisite: Any 300 level ST course. Computer Lab fee.

ST 460 Multivariate Statistical Analysis 3 cr

Multivariate normal distribution, sampling distribution, hypothesis testing, principal components and introduction to factor analysis, canonical correlation analysis, discriminant and classification analysis, and MANOVA. Pre-requisite: Any 300 level ST course. Computer Lab fee.

ST 470 Theory of Statistics 3 cr

A comprehensive introduction to the mathematical foundations of statistics. Sufficient statistics and information. Parameter estimation, maximum likelihood and moment estimation, optimality properties of estimators and confidence intervals. Hypothesis testing, likelihood ratio tests and power functions. Credit for both ST 470 and MA 551 is not allowed. Pre-requisite: MA 451 or MA 550.
ST 475 Statistical Computing and Graphics 3 cr
Introduction to computer-assisted data analysis with statistical computer software, including SAS, R/S-Plus. Coverage includes basics of SAS, common SAS statistical procedures, high-dimensional data visualization, some elements of statistical computing such as numerical computation, semi-numerical computation, symbolic and graphical computation, and special topics selected by instructor. (Credit for both ST 475 and ST 575 is not allowed. Pre-requisite: ST 210 or ST 315 or permission of instructor.)

ST 480 Statistical Practicum (W) 1 cr

Relates to the student's classroom studies with actual statistical problems encountered in practice. Working with the departmental statistical consultant, the student will participate in providing statistical assistance to research faculty in applied fields. Pre-requisite: C or better in EH 102 or EH 105 and approval of department chair. Computer Lab fee.

ST 490 Special Topics 1-3 cr

Selected topics in advanced undergraduate applied statistics. This course may be repeated for a maximum of six credits.

ST 494 Directed Studies 1-3 cr

Directed study. May be repeated for a maximum of six credits. Pre-requisite: Permission of the department chair.

ST 499 Honors Senior Project 3-6 cr

With the guidance and advice of a faculty mentor, Honors Students will identify, and carry out a research project in Statistics. The outcome of the research project will include a formal presentation at the annual Honors Student Colloquium. The senior project will be judged and graded by three members of the faculty, chaired by the faculty mentor.

ST 540 Statistics in Research I 3 cr

A service course for graduate students in disciplines other than mathematics and statistics. A non-calculus exposition in support of application. Coverage includes descriptive statistics, probability and probability distributions, sampling, estimation, tests of significance, analysis of variance, correlation, linear, polynomial, and multiple linear regression including residual and lack of fit analysis, nonparametric procedures, contingency table analysis, and computer-assisted data analysis using appropriate computer software. Computer lab fee.

ST 545 Statistics in Research II 3 cr

Continuation of ST 540. Coverage includes regression analysis through matrices, multiple, partial and multiple-partial correlation analysis, model building algorithms, non-linear regression, analysis of covariance, completely randomized, randomized complete block, and factorial experimentation for equal and unequal cell replication, logistic regression, resampling, basic multivariate techniques, and computer assisted data analysis. pre-requisite: C or better in ST 540. Computer lab fee.

ST 550 Environmental Statistics 3 cr

Sampling environmental populations, parametric and nonparametric estimation; applications of lognormal, Weibull, gamma and beta distributions; locating hot spots; censored data; outlier detection; trend analysis, seasonality; estimation of animal abundance. Pre-requisite: ST 540. Computer lab fee.
ST 575 Statistical Computing and Graphics 3 cr
Introduction to computer assisted data analysis with statistical computer software, including SAS, R/S-Plus. Coverage includes basics of SAS, common SAS statistical procedures, high-dimensional data visualization, some elements of statistical computing such as numerical computation, semi-numerical computation, symbolic and graphical computation, and special topics selected by instructor. (Credit for both ST 475 and ST 575 is not allowed. Pre-requisite: ST 210 or ST 315 or permission of instructor).
Department of Mathematics and Statistics

College of Arts and Sciences

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Date last changed: April 7, 2014 12:01 PM
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