Jun 20, 2024  
2023-24 Catalog 
    
2023-24 Catalog [ARCHIVED CATALOG]

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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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