This course provides an introduction to the process of collecting, manipulating, exploring, analyzing, and displaying data using the statistical software R. The collection of elementary statistical analysis techniques introduced will be driven by questions derived from the data. The data used in this course will generally follow a common theme. No prior knowledge of programming, data science, or statistics is required.

Introduction to the probability & statistical theory underlying the estimation of parameters & testing of statistical hypotheses, including those arising in the context of simple & multiple regression models. Students will use computers & statistical programs to analyze data. Examples & applications are drawn from economics, business, & other fields. Students will not receive credit for both STAT 2120 & ECON 3710. Highly Recommended: MATH 1210 or equivalent; Co-requisite: Concurrent enrollment in a lab section of STAT 2120.

This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical data. Prerequisite: MATH 3100 or APMA 3100.