This assignment will look at customer churn data from several telecommunication companies. The goal is to determine what makes a customer more likely or less likely to churn so these companies can understand how to best retain customers. Supervised programming techniques will be utilized in the model building process, followed by ROC and Bagging AUC analysis to ultimately determine the final model.

Logistic Regression on Student Satisfaction

Week 8 Presentation for STA 490

This assignment is going to analyze the association between BMI and the probability of having a stroke. This relationship will be analyzes using simple logistic regression. This relationship is being analyzed because the World Health Organization identifies strokes as the cause of approximately 11% of deaths around the world, and the World Stroke Organization identifies being overweight, classified as a high BMI, to be one of the top ten causes of strokes. In this assignment, we will analyze and see if the results support the literature of there being an association between BMI and probability of having a stroke.

This project will demonstrate the use of variable selection to determine the final model. Bootstrapping procedures will be used to generate estimated confidence intervals for the coefficients of the regression model identified in a previous assignment. Specifically a model that looks at how different variables like age, hypertension, and diabetes can help predict a patients bmi.

This assignment is going to look into implementing various different model-building techniques in order to find the best model.