Details

The spread-location plot was suggested in [1] and the version in this Demonstration in [2], which used Mathematica to derive the optimal symmetrizing transformation for for a variety of error distributions.

In this Demonstration, the linear regression is fitted to data generated with , and is t-distributed on four degrees of freedom, is uniformly distributed on , and is set to . So the linear regression model is mis-specified and a log transformation of the response variable is needed. The purpose of the spread-location plot is to detect this type of mis-specification. The loess smoother, shown in red, helps to show if there is a relationship between the variance as measured by and the location as measured by .

Snapshot 1: using a log-transformation, , improves the visualization in the plot of versus for the data shown in the thumbnail, with ; the box-whisker chart confirms that is more symmetrically distributed

Snapshot 2: referring again to the data used in the thumbnail, Snapshot 2 shows that does not work as well

Snapshots 3 and 4: a smaller sample, , is used; the effect of the skewness of when is less dramatic and so is the improvement in using

References

[1] W. S. Cleveland, Visualizing Data, Summit, NJ: Hobart Press, 1993.

[2] A. I. McLeod, "Improved Spread-Location Visualization," Journal of Computational and Graphical Statistics, 8(1), 1999 pp. 135–141. doi:10.1080/10618600.1999.10474806.