Molecular Modeling Pro Plus 2D Graph
Output
Below is an
example of the options panel for the XY plot (figure 16) and the resulting
plot. It is taken from a tutorial in the
online help file. This tutorial uses
the development of a model for flash point as an example.

Figure 16. The options panel for the
"advanced" 2D XY plot. We
are going to color the plot by a third variable (connectivity index 1). The minimum and maximum x and y values have
been rounded for better looking graph labels.
We also are requesting that the x axis undergo the 1/x transform. 
Hit the done button in the
options panel. The list of fields again
appears so you can select the field to color the data points by. Select connectivity_1 from the list. The plot in figure 17 appears.


Figure 17. Example of the XY (2D)
"advanced" plot. Flash point
is fairly well correlated with 1/enthalpy of vaporization (the curve drawn
through the data points is that made by the linear least squares regression
model). Data points are colored by a
third variable (connectivity index 1).
It appears that enthalpy of vaporization and connectivity index 1 are
probably also intercorrelated as the colors of the
data points are clustered. The model flash
point = 1/enthalpy of vaporization accounts for about 75% of the variance for
360 values of flash point of solvents and surfactants found in the
industrial.mdb database. Most of the
values with lower connectivity indices (dark blue) are solvents and are found
toward the left side of the graph.
Their behavior is different from surfactants. The Joback and
Reid enthalpy of vaporization calculation was built with solvents
wellrepresented and surfactants not represented. A question to ask about this model is that
is the inverse transform really needed or is it reflecting inaccuracy in the
calculation of enthalpy of vaporization.
Note that a very straightforward linear relationship appears to exist
between flash point and enthalpy of vaporization for solvents. These sorts of insights can only be found
by plotting the data. Note you can
easily see outlying data points in this plot. 
