Applied Statistics.

Catalog description

This course introduces modern applied statistical modeling with an emphasis on data-driven decision-making. Topics include exploratory data analysis, data visualization, regression and classification methods, resampling techniques, model assessment and validation, and introductory generalized linear models. Students will work with real-world datasets and use statistical software to perform analyses, interpret results, and present findings. Applications are drawn from business, engineering, social sciences, and data science. Prerequisite: MATH 146 or MATH 165 .