# STA: Statistics Courses

### Courses

STA 2023 Elements of Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: 35 ALEKS Proctored test OR MAC 1105* OR MAC 1105C* OR 26 SAT15 Math Sub OR MAT 1033* OR MGF 1106* OR MGF 1107* OR 22 ACT Math OR 520 SAT Math OR 123 PERT Math

STA2023 covers descriptive statistics, elementary probability theory, and basic statistical procedures, estimation, and inference. In addition to provide basic concepts in the mentioned areas it prepares the student for other more advanced statistical courses that are necessary for research.
Meets General Education requirement in Mathematics.
Meets Gordon Rule Applied Mathematics Requirement.

STA 2360 Introduction to Data Science

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This is a first course in data science at the undergraduate level. In addition to data ethics, the data science cycle will be covered, including data wrangling, exploratory data analysis, data visualization, predictive modeling, and communicating results. An emphasis will be placed on conducting reproducible research ready for dissemination. This course will provide an overview of common topics in data science. No prior programming or statistics experience is necessary for this course.
Meets General Education requirement in Mathematics.
Meets Gordon Rule Applied Mathematics Requirement.

STA 3162C Applied Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

4 sh (may not be repeated for credit)

Prerequisite: MAC 2311 OR STA 2023

Inferential statistics from an applied point of view. Probability and sampling distributions, confidence intervals and hypothesis testing, ANOVA, correlation, simple and multiple linear regressions. SAS computer techniques. Lab required.
Meets Gordon Rule Applied Mathematics Requirement.

STA 3905 Directed Study

College of Sci and Engineering, Department of Mathematics & Statistics

1-12 sh (may be repeated indefinitely for credit)

STA 4051 Nonparametric Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 2023 OR MAC 2311 OR MAC 2233

The nonparametric or distribution-free methods can be useful in cases such as (i) no assumptions about the underlying population distribution is made, (ii) the data can be categorical or ranked, such as good or bad. This course provides an introduction of some key concepts of nonparametric statistics. Students will learn Why, When, and How to apply nonparametric techniques. This course covers several nonparametric tests as it is described below in Topics.

STA 4121 Statistics for Data Science I

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Statistics for Data Science I is the first course in statistics for students in data science. This 8-week asynchronous online course builds the fundamentals of statistics necessary for students to perform and interpret appropriate hypothesis tests using softwares based on the data and research questions at hand. Offered concurrently with STA 5126. Graduate students will be assigned additional work including professional reports.

STA 4173 Biostatistics

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 2023

A second course in statistics for students in the Biological Sciences. Topics covered include analysis of variance, regression analysis, nonparametric statistics, contingency tables. Offered concurrently with STA 5176; graduate students will be assigned additional work.
Meets Gordon Rule Applied Mathematics Requirement.

STA 4222 Sampling Theory

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: MAC 2311 OR STA 2023

A first course in sampling methods with application to survey sampling and field sampling. Topics include simple random sampling, stratified sampling, cluster sampling, systematic sampling, and adaptive sampling and corresponding estimates for these sampling designs.

STA 4231 Statistics for Data Science II

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 4121 OR STA 4173 OR STA 3162C

Statistics for Data Science II is a second course in statistics for students in data science. This course covers the application of regression analysis techniques using softwares for statistical analysis. Broadly, students will learn how to construct statistical models and disseminate results to a wide audience. There will be a focus on choosing the appropriate modeling strategy for the data and research questions at hand. Offered concurrently with STA 5232. Graduate students will be assigned additional work including conclusions based on statistical inference.

STA 4234 Regression Analysis

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 2023 OR STA 3162C

Simple Linear Regression, Multiple Linear Regression, Model Adequacy Checking, Transformations and Weighting to Correct Model Inadequacies, Diagnostics for Leverage and Influence, Polynomial Regression Models, Indicator Variables, Multicollinearity, Variable Selection and Model Building, Validation of Regression Models, Introduction to Logistic Regression.

STA 4321 Introduction to Mathematical Statistics I

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: MAC 2312

Probability, conditional probability, distributions of random variables, distribution of functions of random variables, limiting distributions, multivariate probability distributions. Offered concurrently with MAP 5XX1 (Introduction to Mathematical Statistics I); graduate students will be assigned additional work.
Meets Gordon Rule Applied Mathematics Requirement.

STA 4905 Directed Study

College of Sci and Engineering, Department of Mathematics & Statistics

1-12 sh (may be repeated indefinitely for credit)

STA 5108 MathStat Tools

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

MathStat Tools will cover computer-oriented statistics projects
using various software programs. This course provides students with a fundamental hands-on experience with SAS, R, Matlab, and Latex. Topics include data manipulation and
management, statistical and mathematical functions, and common statistical procedures
and techniques. Successful completing assignments require a mix of computing and statistics/mathematics.

STA 5126 Statistics for Data Science I

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Statistics for Data Science I is the first course in statistics for students in data science. This 8-week asynchronous online course builds the fundamentals of statistics necessary for students to perform and interpret appropriate hypothesis tests using softwares based on the data and research questions at hand. Offered concurrently with STA 4121. Graduate students will be assigned additional work including constructing professional reports.

STA 5176 Statistical Modeling

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course will provide further examination of statistics and data analysis beyond an introductory course. Topics covered include data visualization, point, and interval estimation, hypothesis testing of means, variances, and proportions, and linear and logistic regressions. Emphasis will be placed on conducting reproducible research.

STA 5232 Statistics for Data Science II

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 5126 OR STA 5176

Statistics for Data Science II is a second course in statistics for students in data science. This course covers the application of regression analysis techniques using softwares for statistical analysis. Broadly, students will learn how to construct statistical models and disseminate predictions and results to a wide audience. There will be a focus on choosing the appropriate modeling strategy for the data and research questions at hand. Offered concurrently with STA 4231. Graduate students will be assigned additional work including conclusions based on statistical inference.

STA 5326 Statistical Inference

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course is an advanced course in mathematical statistics. It is more theoretical than an applied statistics course and takes a mathematical approach to problem solving. Some theorems will be proved. There will be some "real world" applications of the theory.

STA 5905 Directed Study

College of Sci and Engineering, Department of Mathematics & Statistics

1-12 sh (may be repeated indefinitely for credit)

STA 6235 Modeling in Regression

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 5176

Several advanced topics in regression are covered, such as nonlinear regression, influence diagnostics, Eigensystem analysis of X'X matrix, logistic regression, ridge regression, robust regression, and generalized linear models.

STA 6246 Design and Analysis of Experiments

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Further concepts in design and analysis of planned experiments with emphasis on confounding and fractional replications of factorial experiments; composite designs; incomplete block designs; estimation of variance components.

STA 6257 Advanced Statistical Modeling

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course will cover advanced statistical models, enabling students to model various discrete and continuous outcomes. The focus will be determined by instructor and may include such analyses as generalized linear analysis, nonlinear regression analysis, or spatial cluster analysis. In addition to advanced models, the course will include model constructions, model fi
t, interpretation of results, and dissemination of results.

STA 6507 Nonparametric Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Extensive coverage of goodness-of-fit tests, location problems, association analysis and general nonparametric topics.

STA 6666 Statistical Quality Control I

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Procedures used in acceptance sampling and statistical process control are based on concepts and theory from probability and statistics. Introduces the applications of these procedures, investigates them from the standpoint of their statistical properties and develops the methodology for construction, evaluation and comparison of procedures.

STA 6707 Multivariate Methods

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course provides some of the concepts and methods of Multivariate analysis in order to describe and analyze multivariate data. Students will be introduced to multivariate extensions of Chi-Square and t-tests; discrimination and classification procedures; applications to diagnostic problems in biological, medical, anthropological and social research; multivariate analysis of variance; factor analysis and principal components analysis.

STA 6856 Time Series Analysis

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Time series data are time-oriented data that can be used to forecast future values or to analyze data. This course provides students with a fundamental understanding of the nature and basic processes used to analyze such data. This course also introduces the theory and practice of time series analysis, with an emphasis on practical skills. Successful completion of assignments requires a mix of computing and statistics/mathematics.

STA 6905 Directed Study

College of Sci and Engineering, Department of Mathematics & Statistics

1-12 sh (may be repeated indefinitely for credit)

STA 6912 Statistics Research 1

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course gives students the opportunity to engage in group and independent research projects. Research topics and materials vary according to instructor. Technical reports and oral presentations are expected of each student. Students must have completed 15 hours of graduate course work in the program and have maintained at least a 3.0 GPA. Students must also commit to both fall and spring sections of the course.

STA 6913 Statistics Research 2

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

Prerequisite: STA 6912

This course gives students the opportunity to engage in group and independent research projects. Research topics and materials vary according to instructor. Technical reports and oral presentations are expected of each student.

STA 6930 Proseminar in Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

1 sh (may not be repeated for credit)

Each M.A. candidate (except those who choose the thesis option), shall, under the direction of a project advisor, independently investigate a topic or topics in mathematics/statistics or mathematics education through the study of journal articles or other appropriate sources. The candidate shall submit a formal written report and make an oral presentation of the results of his/her investigations. The goal of the proseminar is to provide students an opportunity to integrate the total experience gained during their graduate training. Graded on satisfactory / unsatisfactory basis only. MA candidacy and permission is required.

STA 6950 Capstone Projects in Statistics

College of Sci and Engineering, Department of Mathematics & Statistics

3 sh (may not be repeated for credit)

This course will give students the opportunity to engage in group and independent research projects. Research topics and materials vary according to the instructor with the thrust being applied or theoretical Statistics. Technical reports and oral presentations will be expected of each student.

STA 6971 Thesis

College of Sci and Engineering, Department of Mathematics & Statistics

1-6 sh (may be repeated for up to 8 sh of credit)

Graded on satisfactory / unsatisfactory basis only. Permission is required.