Newsgroups: bit.listserv.edstat-l
Date:         Thu, 16 Apr 1992 14:45:02 CST
Reply-To:     BIOM030@UNLVM.BITNET
From:         BIOM030@UNLVM.BITNET
Subject:      more on transformations

Here is the second batch of responses.

Gil Klajman, Sociology, Montclair State, Upper Montclair, NJ  07043
KLAJMAN@APOLLO.MONTCLAIR.EDU
Gil writes:
I couldn't help wondering about your statement that the world isn't
operating in log units.  I think there are some distinct issues involved.
One of them deals with whether a particular audience reading your
analysis will understand it, the other concerns the best way to describe
a set of facts (best meaning that the mathematical/conceptual model
represent the best trad-off between precision and parsimony).  Many
things in the world are in fact best described in ways that may not be
intuitively appealing to people not familiar with the discipline or its
mathmatical models.

From:         NIZARD Alain-F <NIZARD@FRBDX11>
Alain writes:

The points are: you must have an underlying model and because of the
poorness of statistical knowlege it *must* be linear relative to
parameters.  Eg: y=a*x+b (a,b parameters) IS linear and y=x**a is NOT
linear (relative to a).
The reason is that mathematicians know almost how to treat linear models
but non linear models are far from good knowledge.  Now what to do? have
a linear model ie a model in which the parameters appear only by sums and
products with constants (or variables) so the second model can be re-
written log(y)=a*log(x) a model that is linear.
So you always can do transformations BUT you have to understand WHY you
do THIS transformation and not an other.

Andrew F. Hayes    Cornell University   Department of Psychology
<NR7@CORNELLA>
Andrew writes:

There are certain requirements/assumptions in virtually all statistical
methods.  In the case of most regression procedures, one of those
assumptions is that the distribution of Y at any and all values of x be
normally distributed. When this assumption is violated, all is not lost,
however, because we can make them normal by transforming them, thereby
allowing us to go ahead with the analysis of choice.  The problem with
this procedure...is it becomes difficult to interpret....

Peter Wollan, wollan@math.mtu.edu
Peter writes:

In general the reason for transforming in a regression problem is to
find the "right" units, that is, the units in which the relationship
is **linear**.
Once you make a prediction, you may well want to transform back to the
original units, but of course you don't then expect the plots to be
linear in the original units.  If you can't find a transform that
achieves linearity, or constant error variance, or normality, then you
may have to go to nonlinear regression, which is much messier.
an experiment that allows you to choose between them...A "method" fits a
model, which is going to be at best a useful approximation to reality.
(George Box:  "All models are wrong.  Some models are useful.")

From:    "Dennis Roberts" <DMR@PSUVM>

Dennis brought up the fact that we are constantly transforming values so
that the Z table can be utilized.  "in some cases, transformations can
help you where original data may create problems. Thus, there are some
examples where transformations are both legitimate and useful.



  Thanks again for all the input.

   April Milliken-Janne
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Subject: (no subject given)