I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.

There are NO assumptions in any linear model about the distribution of the independent variables.

Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables. But no, the model makes no assumptions about them.

## Regression analysis for dummy dependent variable (CH_07)

They **do not** need to be normally distributed or continuous.

It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values.

A highly skewed independent variable may be made more symmetric with a transformation.

Tagged as: distribution, Independent Variables, normality, predictor variables, regression models