Here are some
data where the values of both variables were obtained by sampling. They
are the homocysteine and folate (as measured by CLC) levels for a sample
of individuals. Both variables are skewed to the right and the joint
distribution does not have an elliptical shape. If a straight line was
fitted to the data with HCY as a response, the variability about the line
would be much greater for smaller values of folate and there is a
suggestion that the drop in HCY with increasing vitamin status is greater
at lower folate levels.
transformations are applied to both variables, the distributions of
the individual variables are less skewed and their joint distributions is
roughly ellipsoidal. A straight line seem a like reasonable candidate for
describing the association between the variables and the variances appear to
be roughly constant about the line.
Often both variables will not need to be transformed and, even when two transformations are necessary, they may not be the same, When only one variable needs to be transformed in a simple linear regression, should it be the response or the predictor? Consider a data set showing a quadratic (parabolic) effect between Y and X. There are two ways to remove the nonlinearity by transforming the data. One is to square the predictor; the other is to take the square root of the response. The rule that is used to determine the approach is, "First, transform the Y variable to achieve homoscedasticity (constant variance). Then, transform the X variable to achieve linearity."
Transforming the X variable does little to change distribution of the
data about the (possibly nonlinear) regression line. Transforming X is
equivalent to cutting the joint distribution into vertical slices and
changing the spacing of the slices. This doesn't do anything to the
vertical locations of data within the slices. Transforming the Y variable
not only changes the shape of regression line, but it alters the relative
vertical spacing of the observations. Therefore, it has been suggested
that the Y variable be transformed first to achieve constant variance
around a possibly non-linear regression curve and then the X variable be
transformed to make things linear.