Supplies background necessary to design and analyze field and laboratory experiments. Focus on statistical analysis for biological scientists. Primary emphasis on analysis of variance, regression, and experimental design. Computer-assisted analysis experience provided.

Prerequisites: a course in basic statistical analysis.

Supplies background necessary to design and analyze field and laboratory experiments. Focus on statistical analysis for biological scientists. Primary emphasis on analysis of variance, regression, and experimental design. Computer-assisted analysis experience provided.

Prerequisites: a course in basic statistical analysis.

Graduate level independent project or research, unrelated to students' thesis or dissertation.

An overview course designed to provide First-Year students with information, opportunities, and skills to ease their transition into college and build a successful foundation necessary to reach their educational goals.

Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.

[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]

The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks.

Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building.

A modern treatment of probability theory based on abstract measure and integration. Random variables, expectations, independence, laws of large numbers, central limit theorem, and general conditioning using the Radon-Nikodym theorem. Introduction to stochastic processes: martingales, Brownian motion. Prerequisite: Math 623.

This course provides a forum for training in statistical consulting. Application of statistical methods to real problems, as well as interpersonal and communication aspects of consulting are explored in the consulting environment. Students enrolled in this class will become eligible to conduct consulting projects as consultants in the Statistical Consulting and Collaboration Services group in the Department of Mathematics and Statistics.

Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.

[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]