Truncated

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…)

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The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far off that the p-values become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The (more…)

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