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Constant Variance

6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

by Karen Grace-Martin  9 Comments

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 that the p-value 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 [Read more…] about 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

Tagged With: Assumptions, categorical outcome, categorical variable, Censored, Constant Variance, dependent variable, Discrete Counts, normality, ordinal variable, Proportion, Truncated, Zero Inflated

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