MATH 354 - Data Analysis I - Normal Model Inference

An applied regression course that involves modeling data with normal 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 inferences for normal parameters, 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 or BIOL 320 or PSYC 309 or MATH 260 or COSC 290
Major/Minor Restrictions: None
Class Restriction: None
Area of Inquiry: Natural Sciences & Mathematics
Liberal Arts CORE: None

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