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Aug 31, 2024
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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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