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unbounded

Linear Regression for an Outcome Variable with Boundaries

by Karen Grace-Martin  4 Comments

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. [Read more…] about Linear Regression for an Outcome Variable with Boundaries

Tagged With: bounded, categorical variable, ceiling effect, floor effect, linear regression, logistic regression, unbounded

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Issues with Truncated Data

by Jeff Meyer  2 Comments

by Jeff Meyer

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? [Read more…] about Issues with Truncated Data

Tagged With: bounded, count, Truncated, unbounded

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  • Count Models: Understanding the Log Link Function
  • Count vs. Continuous Variables: Differences Under the Hood
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data

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