🚀 go-pugleaf

In article <2975@tymix.Tymnet.COM> rick@daffodil.tymnet.com (Rick McFarland) writes:
>Why don't you try calculating the correlation coefficient: the closer it
>is to 1 or -1, the more linear your data.
>
>
>
>------------------------------------------------
>"ad astra per aspera"

I do not think that the corr. coef. would tell much at all right away.    
If he is asking about linearity I'm sure he has in mind fitting a straight line.
He says:

               " I have a question regarding, linearity
of data points.  Does anybody know of any statistical
test for linearity. I would appreciate any kind of
help, references etc."

I would think that PLOTTING the data first would be necessary in order to asses
the rest of the test:

ie: if there is apattern other than somekind of random scattering along some
invisible straight line (ie: curvilinear like a parabola) then he should fit
a polynomial model (possibly of second order), then the reduced model with only
the linear term and THEN asses linearity by means of the corresponding tests.

If not consider looking at : "Problem solving: a statistician's guide"
                             Christopher Chatfield,
                            Chapman and Hall,    pages 117-118

In it there are 4 sets of data constructed by Anscombe (1973). All have r=.82,
however:

set 1 is your typical "around the straight line" cloud

set 2 is 3/4 of a seemingly "branches down" parabola on which all the point are
      perfectly lined on it.

set 3  is a set of 11 points in which 10 are perfectly lined on a stright line
      and the 11th is an obvious outlier

set 4 is a setof 11 points, but this time the first 10 make a perfectly vertical      straight line. The 11th one is again a far away outlier.

REMEMBER:  ALL them have r = 0.82

   Jorge David Rivas        (undergrad) Dept. of Math & Stats
                                       Simon Fraser University
                                       Burnaby, B.C.  CANADA