STAT — Statistics: Catalog Entries

6 catalog course(s) on file. Undergraduate courses are numbered 100–499; graduate courses 500–699. See the department page for faculty and program information.

STAT 101 — Introduction to Statistics(3 cr.)

Introduction to descriptive statistics, probability, and inferential reasoning for students across disciplines. Topics include hypothesis testing, confidence intervals, and basic regression. Real-world datasets are used throughout the course.

View Fall 2026 sections of this course

STAT 210 — Probability and Statistics I(3 cr.)

Rigorous introduction to probability theory, random variables, and distributions. Students derive properties of common distributions and apply them to statistical inference. Serves as a foundation for further study in statistics.

Prerequisite(s): STAT 101

View Fall 2026 sections of this course

STAT 220 — Statistical Methods(3 cr.)

Applied study of regression, analysis of variance, and categorical data analysis. Students use statistical software to analyze datasets from multiple disciplines. Emphasis is placed on interpreting and communicating statistical results.

Prerequisite(s): STAT 101

View Fall 2026 sections of this course

STAT 310 — Regression Analysis(3 cr.)

In-depth study of simple and multiple linear regression, including model diagnostics and variable selection. Students apply regression techniques to real datasets using statistical software. Extensions to logistic and nonlinear regression are introduced.

Prerequisite(s): STAT 210

View Fall 2026 sections of this course

STAT 340 — Design of Experiments(3 cr.)

Study of principles for designing and analyzing controlled experiments, including randomization, blocking, and factorial designs. Students design an original experiment as a course project. Applications are drawn from industry and scientific research.

Prerequisite(s): STAT 220

View Fall 2026 sections of this course

STAT 420 — Applied Multivariate Statistics(3 cr.)

Study of statistical methods for analyzing multiple correlated variables, including principal component analysis and cluster analysis. Students apply these techniques to complex real-world datasets. Emphasis is placed on interpretation over derivation.

Prerequisite(s): STAT 310

View Fall 2026 sections of this course