Analytical Methods I

Catalog description

This course provides students with the basic toolkit of statistical methods and models that practitioners use for regression, analysis of variance, and linear models. This toolkit could be based on Python® or on R. Topics include descriptive statistics/data summaries, inference in simple and multiple linear regression, residual analysis, estimation and testing of hypothesis, transformations, polynomial regression, model building with real data, nonlinear regression and linear models. The course is not mathematically advanced but covers a large volume of material. (Prerequisites: DATS200, MATH302 and MATH220) Python® is a registered trademark of the Python Software Foundation.