Final answer:
Adding variability to a regression imputation introduces randomness in the imputed values to reflect the uncertainty in the estimation. This provides a more realistic representation of the range of possible values that could have been observed.
Step-by-step explanation:
When performing regression imputation, adding variability is done by introducing randomness in the imputed values to reflect the uncertainty in the estimation. This can be achieved by adding a random error term to the imputed values, based on the residual variation in the regression model.
On the other hand, a regression imputation without variability assumes that the imputed values are completely determined by the regression equation, with no random variation. Comparing the two approaches, adding variability can provide a more realistic representation of the uncertainty in the imputed values, allowing for a better understanding of the range of possible values that could have been observed.