Credits: 3.
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)
Credits: 3; Prereq: STA 2023 or the 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.
Credits: 3; Prereq: MAC 2311.
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)
Back to Top
Credits: 3; Prereq: MAC 2312.
Measurement of simple and compound interest, accumulated and present value. Annuities, yield rates, amortization schedules, sinking funds, bonds, securities and related funds.
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.
Credits: 3; Prereq: STA 4210.
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.
Credits: 3; Prereq: STA 4321 and either STA 2023 or STA 3032 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.
Back to Top
Credits: 3; Prereq: MAC 2313, and STA 2023 or STA 3032 with minimum grades of C, or instructor permission.
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 and moment generating functions. Multivariate probability distributions, independence, covariance. Distributions of functions of random variables, sampling distributions, central limit theorem.
STA 4322 Introduction to Statistics Theory
Credits: 3; Prereq: STA 4321 or the 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.
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.
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, log-linear models applications.
Back to Top
STA 4702 Multivariate Statistical Methods
Credits: 3; Prereq: (STA 3024 or STA 4210 or STA 4322 or STA 6127 or STA 6167) and either MAS 3114 or MAS 4105 or the equivalent.
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 canonical correlation. Multivariate classification techniques including discriminant and cluster analyses. Additional topics at the discretion of the instructor, time permitting.
STA 4712 Introduction to Survival Analysis
Credits: 3; Prereq: STA 4210.
Survival analysis data methods including Kaplan-Meier and Nelson estimators of the survival, accelerated failure time and proportional hazards models and frailty and recurrent event models.
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.
STA 4853 Introduction to Time Series and Forecasting
Credits: 3; Prereq: STA 4210 and STA 4321.
Stationarity, autocorrelation, ARMA models; frequency domain methods and the spectral density; forecasting methods; and computationally-oriented application to case studies.
Back to Top
Credits: 1 to 5; can be repeated with change in content up to 15 credits. Prereq: department permission.
Special topics designed to meet the needs and interests of individual students.
STA 4911 Undergraduate Research in Statistics
Credits: 0 to 3; can be repeated with change in content up to 6 credits.
Course provides firsthand, supervised research in Statistics. Projects may involve inquiry, design, investigation, scholarship, discovery or application in Statistics.
Credits: 3; can be repeated with change in content up to 15 credits. Prereq: department permission.
Variable topics designed to meet the students' needs and interests.
Credits: 1 to 3; Prereq: STA 4211 and undergraduate coordinator permission.
Supervised activity associated with planning and/or analyzing data from a research project. Supervision by a faculty member or delegated authority and a post-internship written report are required. (S-U)
Back to Top