The exercise above can be done analogously based on weight
data instead of area data, providing an estimate sˆ LW for the precision
of estimated length from weight. We can now compare
the precision of length estimates based on area and weight by comparing
sˆ LA with sˆ LW. We can also examine the correlation between
the residuals LAi2Li and LWi2Li. A strong positive correlation will
indicate that the inherent area and weight error variables covariate
much more strongly with each other than with the inherent error
variable for length.
The empirical length frequency distributions based on the calliper
measured, image-based and weight-based lengths were visualized
and compared by kernel smoothing (Wand and Jones,
1995), using a normal kernel with a standard deviation (window
width) h ¼ 0.5 mm. A length step of 0.1mm is used in the visualization.
For the image-based distribution, a frequency interval is
calculated at each length step, to illustrate the maximal effect of
a possible bias in the estimation of the regression slope parameter
b. The interval limits are determined by the frequency values
obtained with bGM
min and bGM
max, and the set of intervals is shown as
a continuous band.