MATH 305 - Statistical Modeling (4)

This course builds on students’ knowledge of statistics by introducing modern statistical modeling techniques vital for understanding large and complex data sets that arise in multiple fields. Students will advance their knowledge in fitting statistical models to data to unearth information, determine significant factors, and make accurate predictions. Students will learn how to develop tools for real-life applications in social, natural, and health sciences; humanities; business analytics; and other STEM fields. Additionally, students will work with the R programming language to perform analyses and generate reproducible reports. This course includes many opportunities for formal and informal collaborations with classmates. Topics include multiple linear regression, generalized linear models, resampling methods, nonlinear regression, smoothing, and tree-based methods. Prereqs: Grades of C or better in MATH 205, MATH 210, and DATA 401. Offered: Spring.