I have genuinely no idea what you're talking about.
Then, my apologies - I had presumed that the basics would have been clear as they are not uncommon, although probably more so at ATPL level than PPL/CPL. However, no matter, let's try again and I'll endeavour to make things a little easier and clearer for you.
Many of the carpets
A simple, single line graph will be presenting two sets of values.
Sometimes we see multiple single line graphs plotted on the one page.
Although each of the lines is unrelated (mathematically) sometimes, for convenience, they may all be plotted on the one graph.
Basically, each line gives us some two dimensional information for its particular value.
An example with which you will be familiar is in the CASA workbook at Loading System Echo Figure 10.
When we get to presenting more complicated data (ie the underlying equation is more complex with multiple variables), we could use a large number of two dimensional graphs at the expense of having many, many pages in the book. However, for many sets of data, it is far easier to plot in a three dimensional arrangement. This is very common in the presentation of aviation information such as in flight manuals or pilots' operating handbooks.
The difference between this and the previous graph is that the values for the various lines relate to one of the variables in the underlying equation whereas, previously, the various lines were unrelated mathematically.
(As an aside, the technique known as Dimensional Analysis was developed to allow us to represent hundreds, even thousands of tabular or graphical data in a single equation using what we refer to as a non-dimensional coefficient. Examples are Mach Number, Lift Coefficient, Drag Coefficient, and so on).
Let's look at the workbook at, say, the Takeoff Weight Chart Aircraft - Echo Figure 12 graph. This is the old DCA style P-chart presentation for the aircraft's takeoff weight information. The figure presents a vast amount of data in a fairly simple to use arrangement. When drawing up the various equations as graphs (and I have done quite a few of these in the dim dark past) the designer positions each separate graph according to background data values (which often are not shown) to make the whole thing work properly. Each separate graph, effectively, is a three dimensional graph in that there are three different, but mathematically related, values presented in the graph.
If we look at any one line, it is just a simple 2D graph. However, when we include a range of related value lines we obtain, in effect, 3D information. This style of graphing usually is referred to as a "carpet" graph/plot or, sometimes, just a "grid". Strictly, a carpet relates to a slightly different style of presentation but the basics are the same.
Just a mathematics buzz word. A related buzz word, for the lines as plotted, is that the line values (eg OAT variation in the density height carpet) are called "parameters".
are characterised by non-linear parametric variables
"Non-linear" refers to a line which is a curve as distinct from a straight line (which we refer to as being "linear"). "Parametric" means those items in a related set of things (ie the parameters) and "variable" a set of values.
So all your technique is trying to do is linearise the carpet.
If the carpet parameter lines are all separated by the same value, then they represent a straight line or linear variation and your technique works. If they aren't, then your proposed technique sets out to corrupt the data by forcing the region between each line to be linear rather than the correct curve shape. This will intentionally introduce errors varying in size depending on the actual data. The following example should make this clear -
This graphic makes use of the cross plot approach described below. The blue line is the correct curve while the red line segments are the separate linearising approach you are proposing. The extent to which any errors might be of practical importance will depend on the actual equations, values and so on.
So, how do we get around this ?
If the parameter is somewhat near linear, a better technique (having played with it during your exam preparation) is to draw a suitable length straight line with scaled intervals (say 10). The drawn line then can be inclined against the printed axis scale (ie between consecutive grid lines) and used as a considerably more accurate scale than would be achieved using a rule in the manner you describe.. This approach is shown below -
If the carpet parameters are obviously non-linear, you are out of luck unless you want to go to the trouble of cross plotting and reading off the required value from the cross plot graph.
Cross-plotting is a technique where you start with a graph of many lines, read off a bunch of points, and then re-plot those points on another, separate graph so that you can better extract desired information to an improved accuracy. With a reasonable amount of practice, you get to the point where you can eyeball where the interpolated line should go (and can rough it in freehand) but that probably will remain the province of techo folk who do this sort of stuff for a living.