One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.
For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.
Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the (more…)
Survival data models provide interpretation of data representing the time until an event occurs. In many situations, the event is death, but it can also represent the time to other bad events such as 
cancer relapse or failure of a medical device. It can also be used to denote time to positive events such as pregnancy. Often patients are lost to follow-up prior to death, but you can still use the information about them while they were in your study to better estimate the survival probability over time.
This is done using the Kaplan-Meier curve, an approach developed by (more…)