Transformations don’t always help, but when they do, they can improve your linear regression model in several ways simultaneously.

They can help you better meet the linear regression assumptions of normality and homoscedascity (i.e., equal variances). They also can help avoid some of the artifacts caused by boundary limits in your dependent variable — and sometimes even remove a difficult-to-interpret interaction.

In this webinar, we will review the assumptions of the linear regression model and explain when to consider a transformation of the dependent variable or independent variable.

Read the full article →
This year’s flu strain is very vigorous. The number of people checking in at hospitals is rapidly increasing. Hospitals are desperate to know if they have enough beds to handle those who need their help. You have been asked to analyze a previous year’s hospitalization length of stay by people with the flu who had […]

Read the full article →