Course description

Statistics is about understanding the world through data. We are surrounded by data, so there is a lot to understand. Covers data exploration and display, data gathering methods, probability, and statistical inference methods through contingency tables and linear regression. The emphasis is on thinking scientifically, understanding what is commonly done with data (and doing some of it for yourself), and laying a foundation for further study. Students learn to use statistical software and simulation tools to discover fundamental results. They use computers regularly; the test includes both multimedia materials and a software package. This course does not focus on data from any particular discipline, but will use real-world examples from a wide variety of disciplines and current events.

Students may receive credit for only one course in the following group: AEM 2100, ENGRD 2700, HADM 2010, ILRST 2100/STSCI 2100, MATH 1710, PAM 2100, PAM 2101, PSYCH 2500, SOC 3010, STSCI 2150.

Prerequisites

Introductory algebra

Summer 2024: Online course

Thomas DiCiccio
Thomas DiCiccio
Associate Professor
Jeremy Entner
Jeremy Entner
Lecturer, Department of Statistics and Data Science