Using Transformations to Improve Your Linear Regression Model - March 2018 [Sample] скачать в хорошем качестве

Using Transformations to Improve Your Linear Regression Model - March 2018 [Sample]

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. We will examine some transformations and discuss how the interpretation of coefficients changes after a transformation. In particular, we will pay very close attention to the log transformation, which has earned widely deserved popularity in the research community. Finally, we will review situations where you should avoid transformations altogether.