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Statistics (STA) Course Descriptions

Department of Management Science and Statistics, College of Business


STA 1043  Introduction to Quantitative Reasoning [TCCN: MATH 1442.]
(3-0) 3 hours credit. Prerequisite: Satisfactory performance on placement examination.
Intended primarily for liberal arts majors, this course provides an overview of statistical methods useful for judgment and decision making under conditions of uncertainty. The emphasis of the course will be on using quantitative reasoning to gain insight and draw conclusions from observations. The common pitfalls of statistical studies and common myths about the fallacies of inference will be discussed. Topics may include data analysis, inference, correlation, and regression. (Formerly titled “Introduction to Statistical Reasoning.”)

STA 1053  Basic Statistics [TCCN: MATH 1342.]
(3-0) 3 hours credit. Prerequisite: Satisfactory performance on placement examination.
Descriptive statistics; histograms; measures of location and dispersion; elementary probability theory; random variables; discrete and continuous distributions; interval estimation and hypothesis testing; simple linear regression and correlation; one-way analysis of variance, and applications of the chi-square distribution. May be applied toward the core curriculum requirement in Mathematics.

STA 1403  Probability and Statistics for the Biosciences [TCCN: MATH 2342.]
(3-0) 3 hours credit. Prerequisite: A grade of “C-” or better in MAT 1193 or an equivalent.
Probability and statistics from a dynamical perspective, using discrete-time dynamical systems and differential equations to model fundamental stochastic processes such as Markov chains and the Poisson processes important in biomedical applications. Specific topics to be covered include probability theory, conditional probability, Markov chains, Poisson processes, random variables, descriptive statistics, covariance and correlations, the binomial distribution, parameter estimation, hypothesis testing and regression. (Formerly STA 1404. Credit cannot be earned for both STA 1403 and STA 1404.)

STA 2303  Applied Probability and Statistics for Engineers
(3-0) 3 hours credit. Prerequisite: MAT 1224.
Fundamental concepts of probability and statistics with practical applications to engineering problems. Emphasis on statistical distribution models used in reliability and risk analysis of engineering design; probabilistic reasoning; Bayes’ theorem; bivariate and multivariate distributions and their applications.

STA 3003  Applied Statistics
(3-0) 3 hours credit. Prerequisite: Completion of or concurrent enrollment in MAT 1033, MAT 1093, MAT 1203, MAT 1214, STA 3023, or an equivalent.
Introduction to the Scientific Method; principles of sampling and experimentation; scales of measurement, exploratory data analysis; introduction to basic probability; models for discrete and continuous data; simple simulations and inferences based on resampling; fundamentals of hypothesis testing and confidence intervals; introduction to analysis of variance and linear regression model. The course will emphasize data analysis and interpretation and effective communication of results through reports or presentations.

STA 3013  Multivariate Analysis
(3-0) 3 hours credit. Prerequisite: STA 3003, STA 3513, or an equivalent.
This course emphasizes application of statistics in organizations. Topics include, but are not limited to the multivariate normal distribution, tests on means, discriminant analysis, cluster analysis, principal components, and factor analysis. Use of software packages will be emphasized. Open to students of all disciplines.

STA 3023  Statistical Mathematics
(3-0) 3 hours credit. Prerequisite: MAT 1093 or an equivalent course or satisfactory performance on a placement examination. Concepts include sequences, series, convergence, limit, continuity, derivative, optimization, the fundamental theorem of calculus, methods of integration, Taylor expansions, function of several variables, partial derivatives, and multivariate transformations. Other topics include vector and matrix algebra, determinants, inverse matrix, eigenvalues and eigenvectors.

STA 3313  Experiments and Sampling
(3-0) 3 hours credit. Prerequisite: One of the following: MS 1023, STA 1043, STA 1053, STA 2303, STA 3003, or an equivalent.
Research techniques for collecting quantitative data: sample surveys, designed experiments, simulations, and observational studies; development of survey and experimental protocols; measuring and controlling sources of measurement error.

STA 3433  Applied Nonparametric Statistics
(3-0) 3 hours credit. Prerequisite: One of the following: MS 3313, STA 2303, STA 3003, or STA 3513.
Tests of location, goodness-of-fit tests, rank tests, tests based on nominal and ordinal data for both related and independent samples, and measures of association.

STA 3513  Probability and Statistics
(3-0) 3 hours credit. Prerequisites: STA 3003 and one of the following: STA 3023 or MAT 1224.
Axiomatic probability; random variables; discrete and continuous distributions; bivariate and multivariate distributions and their applications; mixture distributions; moments and generating functions, bivariate transformations.

STA 3523  Mathematical Statistics
(3-0) 3 hours credit. Prerequisite: STA 3513 or an equivalent.
Sampling distributions and the Central Limit Theorem; order statistics; estimation including method of moments and maximum likelihood; properties of estimators; hypothesis testing including likelihood ratio tests; introduction to ANOVA and regression.

STA 3533  Probability and Random Processes
(3-0) 3 hours credit. Prerequisites: EE 3423 and EGR 2323.
Probability, random variables, distribution and density functions, limit theorems, random processes, correlation functions, power spectra, and response of linear systems to random inputs.

STA 3813  Discrete Data Analysis
(3-0) 3 hours credit. Prerequisite: STA 3003 or STA 3513.
Introduction to methods for analyzing discrete (categorical) data. Course emphasizes the uses and interpretations of the methods rather than the underlying theory. Topics include Two-way and Three-Way Contingency Tables, Partial Association, Cochran-Mantel-Haenszel Method, Generalized Linear models, Model Inference and Model Checking, Logistic Regression, Loglinear Models, and Models for Matched Pairs.

STA 4133  Introduction to Programming and Data Management in SAS
(3-0) 3 hours credit. Prerequisite: Completion of a programming course or consent of instructor, Department Chair and Dean.
This course introduces essential programming concepts using SAS software, with a focus on data management and the preparation of data for statistical analysis. Topics include reading raw data, creating temporary and permanent datasets, manipulating datasets, summarizing data, and displaying data using tables, charts and plots. (Formerly titled “Statistical Computing Packages.”)

STA 4143  Data Mining
(3-0) 3 hours credit. Prerequisite: STA 4133 or equivalent.
Acquisition, organization, exploration, and interpretation of large data collections. Data cleaning, representation and dimensionality, multivariate visualization, clustering, classification, and association rule development. A variety of commercial and research software packages will be used.

STA 4233  Statistical Applications Using SAS Software
(3-0) 3 hours credit. Prerequisites: STA 4133 or approval of instructor; and one of the following: MS 3313, STA 3003, STA 3513, or STA 3523.
Analysis of datasets using the statistical software package SAS. Methods for analyzing continuous and categorical data will be introduced, using procedures from Base SAS, SAS/GRAPH and SAS/STAT software. Techniques for efficient programming will be stressed. Examples will be drawn from regression analysis, analysis of variance, categorical analysis, multivariate methods, simulation, and resampling.

STA 4643  Introduction to Stochastic Processes
(3-0) 3 hours credit. Prerequisite: STA 3513.
Probability models, Poisson processes, finite Markov chains, including transition probabilities, classification of states, limit theorems, queuing theory, and birth and death processes.

STA 4713  Applied Regression Analysis
(3-0) 3 hours credit. Prerequisite: MS 3313 or STA 3003.
An introduction to regression analysis, with emphasis on practical aspects, fitting a straight line, examination of residuals, matrix treatment of regression analysis, fitting and evaluation of general linear models, and nonlinear regression.

STA 4723  Introduction to the Design of Experiments
(3-0) 3 hours credit. Prerequisite: MS 3313 or STA 3003.
General concepts in the design and analysis of experiments. Emphasis will be placed on both the experimental designs and analysis and tests of the validity of assumptions. Topics covered include completely randomized designs, randomized block designs, complete factorials, fractional factorials, and covariance analysis. The use of computer software packages will be stressed.

STA 4753  Time-Series Analysis
(3-0) 3 hours credit. Prerequisite: STA 3513 or STA 3533, or an equivalent.
Development of descriptive and predictive models for time-series phenomena. A variety of modeling approaches will be discussed: decomposition, moving averages, time-series regression, ARIMA, and forecasting errors and confidence intervals.

STA 4803  Statistical Quality Control
(3-0) 3 hours credit. Prerequisite: STA 2303, STA 3003, STA 3513, or an equivalent.
Statistical methods are introduced in terms of problems that arise in manufacturing and their applications to the control of manufacturing processes. Topics include control charts and acceptance sampling plans. (Same as MAT 4803. Credit cannot be earned for both STA 4803 and MAT 4803.)

STA 4903  Applied Survival Analysis
(3-0) 3 hours credit. Prerequisite: STA 3523 or an equivalent.
Measures of survival, hazard function, mean residual life function, common failure distributions, procedures for selecting an appropriate model, the proportional hazards model. Emphasis on application and data analysis using SAS.

STA 4911,3  Independent Study
1 or 3 hours credit. Prerequisites: A 3.0 College of Business grade point average, permission in writing (form available) from the instructor, the student’s advisor, the Department Chair, and the Dean of the College in which the course is offered.
Independent reading, research, discussion, and/or writing under the direction of a faculty member. May be repeated for credit, but not more than 6 semester credit hours, regardless of discipline, will apply to a bachelor’s degree.

STA 4933  Internship in Statistics
3 hours credit. Prerequisites: Permission in writing from the instructor, the Department Chair, and the Dean of the College of Business; and a 2.5 UTSA grade point average. See the College of Business Undergraduate Advising Center for required forms and additional requirements.
Supervised full- or part-time work experience in statistics. Offers opportunities for applying statistics in private businesses or public agencies. May be repeated for credit, but not more than 6 semester credit hours will apply to a bachelor's degree.

STA 4953  Special Studies in Statistics
(3-0) 3 hours credit. Prerequisites: Consent of instructor, Department Chair and Dean.
An organized course offering the opportunity for specialized study not normally or not often available as part of the regular course offerings. Special Studies may be repeated for credit when the topics vary, but not more than 6 semester credit hours, regardless of discipline, will apply to a bachelor’s degree.

STA 4961  Actuarial Science Examination Preparation
(1-0) 1 hour credit.
An organized course offering specialized study for Actuarial Science Examinations. Topics covered include General Probability, Random Variables and Probability Distributions, Multivariate Distributions, and Risk Management and Insurance. May be repeated twice for credit.

STA 4993  Honors Thesis
3 hours credit. Prerequisites: STA 3523 and consent of instructor, Department Chair and Dean. Enrollment limited to students applying for Honors in Management Science and Statistics (see page 55).
Supervised research and preparation of an honors thesis. May be repeated once for credit with advisor’s approval.


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