A technique-oriented approach to data analysis using statistical techniques. Graphical and numerical summaries of data, multivariable thinking, confidence interval estimation, regression and correlation, testing hypotheses including chi-square tests and one-way analysis of variance. A statistical software package will provide computational assistance. Fulfills Foundations - Mathematical Sciences. Offered every semester. Prerequisite: MTH 108 and MTH 109 (or MTH 110) or equivalent.
Winter 2024 - Online Spring/Summer 2024 - Online Fall 2024 - Hybrid Winter 2025 - Online
Project-oriented introduction to major statistical techniques using a statistical package such as SAS or SPSS. Hypothesis testing, t-test, multivariate regression, analysis of variance, analysis of covariance, chi-square tests, and nonparametric statistics. Offered every semester. Prerequisite: STA 215 or STA 312.
Winter 2024 - Online Spring/Summer 2024 - Online Fall 2024 - Hybrid Winter 2025 - Hybrid
An introduction to applications and the conceptual framework for predictive analytics and modeling, using a statistical programming language such as R. Topics include preparing data for predictive modeling, exploratory data analysis and visualizations, multiple linear regression, logistic regression for classification, and methods for model selection and evaluation. Offered winter semester. Prerequisite: STA 215 or STA 312.
Winter 2024 - Online Winter 2025 - Online
An examination of statistics reported in the media. Students will read news stories and published research to critically evaluate the conclusions made, recognizing when assertions are and are not supported by evidence. Common fallacies and misconceptions will be covered. Part of the Information, Innovation, or Technology Issue. Offered fall and winter semesters. Prerequisites: One of STA 215, or STA 220, or STA 312; and junior standing.
Winter 2024 - Online Spring/Summer 2024 - Online Fall 2024 - Online Winter 2025 - Online
An introduction to statistical programming and graphics using the object-oriented statistical language R. Skills in writing R code to perform statistical analyses, graphics and simulations are developed. Emphasis will be on solving real problems with hands-on work including randomization statistics, time series, data mining and big data analysis. Cross-listed with STA 518. Offered fall semester. Prerequisites: (STA 215 or STA 220 or STA 312) and (STA 216 or CIS 162).
Spring/Summer 2024 - Online
Internship in a statistical situation with individual faculty supervision to allow students to apply academic knowledge to actual and professional experiences. Offered fall and winter semesters. Prerequisites: Junior standing and permission of the instructor. Graded credit/no-credit.
Fall 2024 - Online
Independent research in an area of interest to the students, supervised by a member of the statistics faculty. Hours, credits, topics, and time to be arranged by the student in conference with professor. Approval of the department required. Offered fall and winter semesters.
An introduction to statistical programming and graphics using the object-oriented statistical language R. Skills in writing R code to perform statistical analyses; graphics and simulations are developed. Emphasis will be on solving real problems with hands-on work including randomization statistics, time series, data mining and big data analysis. Cross-listed with STA 418. Offered fall semester. Prerequisite: Admission to a graduate program in biostatistics, computer information systems, data science, or health informatics and bioinformatics.
Project-oriented overview of major statistical techniques commonly used in problems encountered in health professions. Students will learn to use a major statistical computing package. Hypothesis testing, t-tests, regression, analysis of variance, analysis of covariance, categorical data analysis, nonparametric statistics. Offered fall, winter, and spring/summer semesters.
Spring/Summer 2024 - Hybrid Fall 2024 - Online
Traditional and modern computationally intensive statistical modeling techniques. Basics of probability theory, including conditional probability, Bayes' Theorem, and univariate probability models. Regression modeling and prediction including simple linear, multiple, logistic, Poisson, and nonlinear and nonparametric regression. Methods for model selection and shrinkage. Emphasis is on application and interpretation using statistical software. Offered fall semester. Prerequisite: Admission into the data science professional science master's program.
Independent research in an area of statistics or biostatistics that is of interest to the student and the supervising faculty member. Readings and discussions may be appropriate. Hours, credits, meeting times, and the topic(s) in statistics or biostatistics are determined by the student and faculty mentor. Offered fall and winter semesters. Prerequisites: Departmental approval is required.
Spring/Summer 2024 - Online Fall 2024 - Online
