Exponential or logarithmic transformations to the predictor and criterion variables are useful for which of the following reasons?
A) Transformations allow regression analysis when the relationship between predictor and criterion is not additive.
B) Transformations allow regression analysis when the relationship between predictor and criterion is nonlinear.
C) Transformations allow regression analysis when the error terms are heteroscedastic.
D) None of the above are valid reasons for transforming predictor and criterion variables.
E) All of the above are valid reasons for transforming predictor and criterion variables.

Question

Quizplus · Accepted Answer

Exponential or logarithmic transformations are often used in regression analysis for several reasons: they can help when the relationship between variables is not additive (A), when the relationship is nonlinear (B), and when the error terms exhibit heteroscedasticity, meaning the variability of the error terms is not constant across all levels of the independent variables (C). These transformations can make the model fit the data better by addressing these issues.

Exponential or Logarithmic Transformations to the Predictor and Criterion Variables