STAT 432 - Introduction to Linear Regression and Logistic Regression

Units: 3
Computational methods in linear regression and logistic regression. Model selection methods. Predictive modeling and forecasting. Attention to model assessment, graphical techniques, and assumption checking. Emphasis on real data from science, social sciences, and business. Use of statistical software. Report writing.

Prerequisites: STAT 330 or STAT 310.
Possible Instructional Methods: On-ground.
Grading: A-F or CR/NC (student choice).
Course Typically Offered: Spring ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply basic computational skills in descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis, including the use of large data sets.
Analyze data using appropriate software, including cloud-based software, and to interpret results covering descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis, including the use of large data sets.
Communicate to others results involving descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis using reproducible research best practices.
All of these are done in the context of regression.