STAT 467 Applied Linear Regression Models

Introduction to linear regression models. The course provides an introduction to the most commonly used models in statistical data analysis. Topics may include: simple linear regression, multiple regression, least squares estimators, inference, hypothesis testing, analysis of variance, and statistical model-building strategies. Regression diagnostics, analysis of complex data sets and scientific writing skills are emphasized. Methods are illustrated with data sets drawn from the health, biological, and social sciences. Computations require the use of a statistical software package such as R.  

Credits

3

Prerequisite

STAT 201 or STAT 216 or STAT 453 or permission of department chair.

General Education

Offered

  • Spring