To address the issue of nonconstant variance, which transformation should be used as the dependent variable?

To effectively address the issue of nonconstant variance, the recommended transformation for the dependent variable is the reciprocal transformation, represented as 1/y. This transformation is particularly useful when the variance of the dependent variable increases with the magnitude of the response variable, a situation known as heteroscedasticity.

When you apply the reciprocal transformation, you effectively compress larger values of y while expanding smaller values. This can stabilize the variance across the range of the data, as it mitigates the influence of outliers or disproportionately large values that might inflate variance, allowing for more reliable model estimates.

In contrast, other transformations like the logarithmic or square root may be appropriate in certain scenarios but are generally suited for different variance structures or when dealing with specific types of non-linearity. Thus, using the reciprocal transformation is a targeted approach to directly address the variance issue by transforming the scale of the dependent variable in a way that aids in achieving homoscedasticity.