STS 2320 Statistical Modeling

This course emphasizes rationales, applications and interpretations of regression methods. Advanced statistical software will be used. Topics include simple linear regression, multiple linear regression, indicator variables, robustness, influence diagnostics, model selection, and logistic regression for dichotomous response variables and binomial counts. Written reports link statistical theory and practice with communication of results.

Credits

4 sh

Prerequisite

STS 2120 or permission of the statistics program coordinator

Course Types

First-Year Foundation; Science

Offered

• Fall
• Spring

Course Outcomes

1. Statistics promotes quantitative critical thinking skills that should serve the student in the rest of their course studies at Elon. Specific outcomes that should prove valuable include the ability to:
   * Fit and interpret simple linear and multiple linear regression models.
   * Assess the suitability of models.
   * Use various model selection procedures to build regression models.
   * Determine when logistic regression models would be appropriate and use them to make inferences.
   * Identify which regression models are most appropriate in given real-world settings.
   * Develop sufficient statistical skills to critically examine the research of others and carefully perform their own research.
   * Effectively organize and present data both visually and in writing.
   * Use techniques of statistical inference, particularly statistical models, to help assess scientific questions of interest.