2003 - 2004
Casella, G., Chair; Agresti, A.G.; Booth, J.G.; Brank, E.; Carter, R.L.; Chang, M.N.; Cornell, J.G.; Daniels, M.J.; Garvan, C.; Ghosh, M.; Hobert, J.P.; Khuri, A.I.; Littell, R.C.; Marks, R.G.; Martin, F.G.; Meece, M.; Mukherjee, B.; Portier, K.M.; Presnell, B.D.; Randles, R.H.; Rao, P.V.; Ripol, M.; Rosalsky, A.J.; Scheaffer, R.L.; Schoolfield, C.; Shuster, J.J.; Stevens, G.R.; Tian, L.; Trindade, A.A.; Wackerly, D.D.; Winner, L.; Wu, R.; Wu, S. S.; Yang, M.C.K. Undergraduate Coordinator: D.D. Wackerly Graduate Coordinator: J.P. Hobert
STA 2023 Introduction to Statistics 1.
Graphical and numerical descriptive measures. Simple linear regression. Basic probability concepts, random variables, sampling distributions, central limit theorem. Large and small sample confidence intervals and significance tests for parameters associated with a single population and for comparison of two populations. Use of statistical computer software and computer applets to analyze data and explore new concepts. (M) GR-M†
STA 2122 Statistics for the Social Sciences.
Basic statistical concepts presented in a conceptual fashion, emphasizing data collection and analysis rather than theory. Topics include exploratory data analysis, design of surveys and experiments, introduction to estimation and significance tests and the use of statistics in the social sciences and the media. (M) GR-M†
STA 3024 Introduction to Statistics 2.
Credits: 3; Prereq: STA 2023 or equivalent.
An introduction to the analysis of variance. Nonparametric statistical methods and applications. Analysis of count data: chi-square and contingency tables. Simple and multiple linear regression methods with applications. (M) GR-M†
STA 3032 Engineering Statistics.
Credits: 3; Prereq: MAC 2311.
A survey of the basic concepts in probability and statistics with engineering applications. Topics include probability, discrete and continuous random variables, estimation, hypothesis testing, and linear and multiple regression. (M) GR-M†
STA 4033 Mathematical Statistics with Computer Applications.
Credits: 2; Prereq: STA 2023 or STA 3032, MAC 2312, CIS 3020 or equivalent.
Computer simulations on simple statistical techniques such as histograms, z-tests and t-tests, analyzing large data sets by regression, contingency tables, non-parametric and simple multivariate procedures. (M) GR-M†
STA 4170 Introduction to Statistical Methods in Pharmacy.
Introduces statistical design and analysis techniques needed to perform pharmaceutical research and evaluate articles in medical literature. Designing epidemiologic and clinical studies, evaluating diagnostic testing procedures, interpreting the use of rates in medical literature, and using frequently used statistical methods of data analysis. Emphasis will be on concepts and their application to critical appraisal of statistical contents in medical literature. (M) GR-M†
STA 4173 Biometry.
Credits: 3; Prereq: STA 4210 or STA 4322 or equivalent.
Specialized statistical methods in biological medical sciences. Contents include analysis of rates and proportions, statistical methods in biological assay, analysis of survival data, planning and designing of clinical trials. (M) GR-M†
STA 4210 Regression Analysis.
Credits: 3; Prereq: STA 2023 or STA 3032 or STA 4322.
Simple linear regression and multiple linear regression models. Inference about model parameters and predictions, diagnostic and remedial measures about the model, independent variable selection, multicolinearity, autocorrelation, and nonlinear regression. SAS implementation of the above topics. (M) GR-M†
STA 4211 Design of Experiments.
Credits: 3; Prereq: STA 4210.
An introduction to the basic principles of experimental design: analysis of variance for experiments with a single factor; randomized blocks and Latin square designs: multiple comparison of treatment means; factorial and nested designs; analysis of covariance; response surface methodology. GR-M†
STA 4222 Sample Survey Design.
Credits: 3; Prereq: STA 2023 or STA 2122 or STA 4322.
An introduction to the design of sample surveys and the analysis of survey data, the course emphasizes practical applications of survey methodology. Topics include sources of errors in surveys, questionnaire construction, simple random, stratified, systematic and cluster sampling, ratio and regression estimation, and a selection of special topics such as applications to quality control and environmental science. (M) GR-M†
STA 4321 Mathematical Statistics 1.
Credits: 3; Prereq: MAC 2313 or equivalent.
Introduction to the theory of probability, counting rules, conditional probability, independence, additive and multiplicative laws, Bayes Rule. Discrete and continuous random variables, their distributions, moments, moment generating functions. Multivariate probability distributions, independence, covariance. Distributions of functions of random variables. (M) GR-M†
STA 4322 Mathematical Statistics 2.
Credits: 3; Prereq: STA 4321 or equivalent.
Sampling distributions, central limit theorem, estimation, properties of point estimators, confidence intervals, hypothesis testing, common large sample tests, normal theory small sample tests, uniformly most powerful and likelihood ratio tests, linear models and least squares, correlation. Introduction to analysis of variance. (M) GR-M†
STA 4502 Nonparametric Statistical Methods.
Credits: 3; Prereq: STA 2023 or STA 3032 or STA 4210 or STA 4322.
Introduction to nonparametric statistics, including one- and two-sample testing and estimation methods, one- and two-way layout models and correlation and regression models.
STA 4504 Categorical Data Analysis.
Credits: 3; Prereq: STA 3024 or STA 3032 or STA 4210 or STA 4322.
Description and inference using proportions and odds ratios, multi-way contingency tables, logistic regression and other generalized linear models, loglinear models applications.
STA 4664 Industrial Statistics.
Credits: 3; Prereq: STA 3032 or a 4000 or higher level STA course.
Philosophy and tools of total quality management, design of experiments for process optimization via response surface methods including factorial, fractional factorial, Plackett-Burman and central composite designs; control chart methods, including Shewhart and cusum charts.
STA 4702 Multivariate Statistical Methods.
Credits: 3; Prereq: STA 3024 or STA 4322 or STA 6127 or STA 6167 or STA 4211.
Review of matrix theory, univariate normal, t, chi-squared and F distributions and multivariate normal distribution. Inference about multivariate means including Hotelling’s T2, multivariate analysis of variance, multivariate regression and multivariate repeated measures. Inference about covariance structure including principal components, factor analysis and cannonical correlation. Multivariate classification techniques including discriminant and cluster analyses. Additional topics at the discretion of the instructor, time permitting.
STA 4821 Stochastic Processes.
Credits: 3; Prereq: STA 4321 or equivalent.
Theoretical development of elementary stochastic processes, including Poisson processes and their generalizations, Markov chains, birth and death processes, branching processes, renewal processes, queuing processes and genetic and ecological processes. GR-M†
STA 4905 Individual Work.
Credits: 1 to 5; Prereq: permission of department. May be repeated with change of content up to a maximum of 15 credits
Special topics designed to meet the needs and interests of individual students. GR-M†
STA 4930 Special Topics.
Credits: 3; Permission of the department chairperson. May be repeated with change of content up to a maximum of 15 credits.
Rotating topics designed to meet the needs and interests of individual students. GR-M†
STA 4940 Internship.
Credits: 1 to 3; Prereq: STA 4211.
Supervised activity associated with planning and/or analyzing data from a research project. Permission of the undergraduate coordinator required. Supervision by a faculty member or delegated authority and a post-internship written report are required. S/U
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