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How does adding variability to a regression imputation compare to one without?

User Yousri
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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.

User Tzahi Leh
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