[From Bruce Abbott (950117.0930 EST)]

Rick Marken (950116.2045 PST)

Bruce Abbott (950116.1730 EST) --

Nice analyses Bruce. But it seems like you forgot the most basic

analysis of all; the one where you find the variable that is the

best predictor of the subject's response variations (H). There

are only three possibilities because there are only three variables

that the subject can actually see: C1, C2 and C3. Why not use the

multiple regression capabilities of Minitab to find the relative

contribution of each of these variables to the variance in H?

Why, do you think it will tell us something about how subjects control? (;->

Rick, the way Minitab has been set up on our LAN, it does not get enough

workspace to handle multiple regression with three predictors on an 1800 row

dataset. I DID perform the bivariate regressions (not reported) using

cursor position as explanatory variable and handle position as response

variable; you can get some idea of the result by squaring the correlations

(which were reported). I'm going to see if there is a way to get the memory

allocation for Minitab increased.

Another thing I would like to have done is to generate some

three-dimensional plots of these data (response surface), as well as

scatterplot matrices. Minitab has a new Windows-based version out that

apparently can do these, but I haven't seen it yet.

Do the PCT-analysis programs you have written estimate the reference value

from the data? When the data set contains only cursor and handle positions,

(i.e., lacks the disturbance tables), the disturbance values can be

calculated exactly only if the reference values are known (In THREECV1 all

three = 319). For this reason I save the references on the first line of

the data file: they are needed by Bill's PCT analysis in order to

reconstruct the disturbances. I found that the calculations fail if I do

not subtract the reference value.

What other sorts of analyses do your programs perform on data like these?

Regards,

Bruce