Linear Regression for an Outcome Variable with Boundaries

July 22nd, 2019 by

The following statement might surprise you, but it’s true.

To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is:

  • Continuous
  • Unbounded
  • Measured on an interval or ratio scale

The normality assumption is about the errors in the model, which have the same distribution as Y|X. It’s absolutely possible to have a skewed distribution of Y and a normal distribution of errors because of the effect of X. (more…)

Issues with Truncated Data

May 12th, 2016 by

In a previous post we explored bounded variables and the difference between truncated and censored. Can we ignore the fact that a variable is bounded and just run our analysis as if the data wasn’t bounded? (more…)