MATH 354 - Data Analysis I - Generalized Linear Models

An applied regression course that involves modeling data with generalized linear and nonparametric models including hands on Tukey-style data analysis with statistics software. Students explore topics that are widely used today across disciplines in academic research and in business; such topics include point and interval estimation, correlation, regression, analysis of variance (ANOVA), model diagnostics, model building, and transformations. Students will start with regression analysis with a single predictor variable, then consider regression analysis where two or more variables are used for making predictions. While applied, this course aims to combine theory and application to emphasize the need for understanding each methods’ theoretical foundation. This conversation is had through illustrating a variety of inferences, residual analyses and fully exploring the implications of our assumptions.

Credits: 1.00
Prerequisites:

ECON 375

BIOL 320

or PSYC 309 or (

and MATH 260) or (

and

) or (

and