MATH 342 - Applied Statistical Methods II

5 CR

This class will focus on various types of general linear models including simple and multiple regression, and log-linear models, as well as stepwise regression, logistic regression, and analysis of variance/covariance. The focus will be on statistics in real-world examples from various sources using programming languages R or Python. Students should expect to produce reports and presentations.

Prerequisite(s): MATH 341 with a C or better, or permission of Mathematics Department Chair.

Course Outcomes

Identify various general linear models and discuss their characteristics, advantages and limitations
Evaluate the relevant aspects of a real-world data set and choose an appropriate type of regression model for data sets of various sizes and formats
Formulate, fit, and apply the models using statistical software such as SAS or R
Perform model assessment and improvement
Interpret results and clearly state conclusions in reports and presentations with close attention to detail and demonstrating knowledge of data extraction and evaluation methods from previous classes

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