We have got a brand-new topic now let us talk about XYPlots. You may be familiar with these.
Social science is fond of these things. You have a series of data – income and education
by a sample size of 10, you plot them together, we have education vs. income – you end up
seeing correlations like this. It is also called a scatterplot. Now of course you can
do this same thing with process data. We have support for this in ProcessBook through this
XYPlot. So if I click on this XYPlot button or choose draw XYPlot I can then try to
find out what the correlation is between two process variables. So for example I am going
to look at ba:temp.1. That is a temperature and that is going to be my x axis. As you
can see you have radio buttons to choose that. I am going to plot that against CDT158. That
is going to be basically the y-axis part of this. Let me just go ahead and complete this
so you can show what we show just right off the bat with the defaults. What we are showing
here the temperature plotted against the bottom, the other variable CDT158 plotted across the
y-axis, and what were doing is reporting the y coefficient right up here. So there is an
inverse correlation. As the temperature increases this CDT158 decreases. You see the inverse
correlation? It is a strong r coefficient .7 to indicate that there is a strong relationship
between the two. Some of the things we did not point out when I was doing this, first
of all, in many cases you would like to tune these scatter plots so that they are just
showing the data, not the lines. So one of the things you can do is remove the Connecting
Lines, remove the grids, and you have something that looks a little bit more like a traditional
scatterplot. Now I can also look at how the data is aligned in this instance by double-clicking
right up here where the title would normally appear. Let me go ahead and put in a plot
title and I will show you what I am talking about. Let us put in the title. Title goes
here. As you can see, this area right in here, if you double-click on that is going to go
ahead and launch this statistics. So it is telling us, for example, how many items in
this sample and what the coefficient of correlation is, what the slope is etc. But you can also
look at the raw data for both PI tags involved. Here is my x-axis. Let us look at the y-axis.
We have correlated these in time: 3:45 to 3:45, 3:55 to 3:55, etc. So as we are going
forward in time we are taking the x value, we are plotting it against the y value. Now
if you're familiar with PI data collection you will recognize that this seems a little
odd. It would be the wildest of coincidences if there is actually an archive value at 5:15
5:25 5:35 for each and every one of these. So, in fact what we're doing is, by default,
we're interpolating values. As you can see right here, we're choosing the interpolate
method. We're doing this at 10 min. intervals so I end up with 49 values. If I were to change
this to say 20 min. intervals, we would end up with fewer in this sample. As you can see
fewer dots and now there's only 25 instead of 49. So the idea here is that this interval
determines... essentially determines what your sample population Is going to be if you
choose interpolated. You have other options. What you could do is you can just say let
us do recorded data. What that simply going to do is find all the recorded values for
BA:TEMP.1 and it will synchronize the y tag to those recorded values. So in this case
let us look at the grand finale here. The end result here is if we look at the data
you we're seeing--let us put both of these on here now--we're seeing that every single
value 353, 357, 59, 409--these are the actual archive values of the tag on the x-axis, the
temperature. What we're doing is we are interpolating a synchronized value for that timestamp. So
409 to 409, 429 to 429. These are not at evenly spaced intervals. They are at the intervals
of the occurrence of data on the tag on the x-axis. So these are some of the options you
have. In addition to synchronizing but you can also specify that we do some--well some
things that do not make any sense. For example, you can say interpolate in which now we are
comparing the recorded values against the interpolated values. It does not make much
sense to do that in this instance because of course they're all going to be at nonmatching
timestamps. One of the other things you can do is do a match which indicates that it is
only going to compare when there is a complete match ; when both of them have a value at
exactly the same timestamp. And in fact it looks like there are a few instances of that.
Here if we look at the data again, let's look at the two next to each other, for most of
the sample size there is no corresponding data but... but occasionally yes , there we
go, we see just a few instances where at exactly the same moment in time, there was a value
for both. So we have tried to make this flexible enough that depending on what your requirements
are, what your reporting requirements may be, or just what your standards are for doing
this calculation, you have the option to do that. You also have the option here to change
which one is the x and which one is the y using this radio button. We do support adding
multiple trends on here. You do not have to just comparing temperature against one variable.
Finally the XYPlot allows you to do a type of a cross correlation where you are comparing
these two variables but not for the same period of time. Let me give you an example. If I
set BA:TEMP to interpolated values and this is also at interpolated values, but at different
times--for example, let us say we suspect that the temperature is a leading indicator.
Temperature is a lagging indicator by about an hour. Let us go with yesterday +7 hours
to yesterday +15 hours. That is the 7 to 3. That is for the x-axis, the temperature. But
then here we will go yesterday +8 hours Y+8H to Y+16 hours. Since it is the same interval,
the same number of interpolated values, but it is for a different period of time. So we're
suspecting this is a leading this is a lagging indicator. Let us see if there is a coefficient
of correlation. And it is apparently my guess was wrong. This coefficient of correlation
indicates no correlation at all and if we compare the individual values by hovering
over here you can see. Take a look at the timestamp. It's 3:50 compared to 2:50 on the
y-axis vs. the x-axis. So if we go through these you will see all of these are offset
exactly one hour 11:30 to 10:30. If we look at the actual set of data, you'll see that
same thing. So it is a nice opportunity to do an interesting kind of a crosscorrelation:
7:40 to 8:40, 7:50 to 8:50 etc. By being able to do this so simply you can find correlations
that may not have been apparent. Now for more information on the formatting options available,
just take a look in your PI ProcessBook users guide you find this in chapter 6 on XYPlots.