# Office of the University Registrar

Course Descriptions

College of Liberal Arts and Sciences

STA 2023 Introduction to Statistics 1
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) (MR)

STA 2122 Statistics for Social Science
Credits: 3.
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 use of statistics in social sciences and media.

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) (MR)

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) (MR)

STA 4173 Biostatistics
Credits: 3.
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) (MR)

STA 4183 Theory of Interest
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.

STA 4210 Regression Analysis
Credits: 3; Prereq: STA 2023, 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) (MR)

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. (MR)

STA 4222 Sample Survey Design
Credits: 3; Prereq: STA 2023 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) (MR)

STA 4321 Mathematical Statistics 1
Credits: 3; Prereq: grade of "C" or better in MAC 2313, and STA 2023 or STA 3032, or permission of instructor.
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, sampling distributions, central limit theorem. (M) (MR)

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) (MR)

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 4211 or STA 4322 or STA 6127 or STA 6167.
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. (MR)

STA 4853 Introduction to Time Series and Forecasting
Credits: 3; Prereq or coreq: STA 4322.
Stationarity, autocorrelation, ARMA models. Frequency domain methods, the spectral density. Forecasting methods. Computationally-oriented, application to case studies.

STA 4905 Individual Work
Credits: 1 to 5; can be repeated with change in content up to 15 credits. Prereq: permission of department.
Special topics designed to meet the needs and interests of individual students. (MR)

STA 4930 Special Topics
Credits: 3; can be repeated with change in content up to 15 credits. Prereq: permission of department chair.
Rotating topics designed to meet the needs and interests of individual students. (MR)

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

General Education Categories

• Composition (C)
• Mathematical Sciences (M)
• Humanities (H)
• Social and Behavioral Sciences (S)
• Physical (P) and Biological (B) Sciences
• International and Diversity focus (I)

Symbols Used in Course Descriptions

• (WR) indicates the course satisfies the writing requirement.
The Schedule of Courses lists the amount of writing credit per course section.
• (MR) indicates the course satisfies the math requirement.
• †† indicates the course may be taken on an S-U basis.