Newsgroups: bit.listserv.stat-l
Date:         Tue, 5 Jan 1993 13:38:00 EST
From:         "Philip Gallagher,(919)966-1065" <UPHILG@UNC.BITNET>
Subject:      Spreadsheet Software for statistical computing

    Dr. Arday wrote that he doesn't believe that the formulae he
    uses from Fleiss and from Kleinbaum, et al., are "wrong", and
    that the professionals who warn against using naive implementations
    of the texbook formulae (in spreadsheet packages) need to
    substantiate their claims.

    I think Dr. Arday didn't get the right intelligence (in the military
    sense) from the information in Phil Miller's note.  Perhaps it is
    clearer to say that
        ... the textbook formulae are right, but they give wrong
        answers in many implementations, especially implementations
        on digital computers. ...

    Even in hand calculations where one has (in principle!) complete
    control over the number of decimal places, naive implementation
    of the textbook formulae can lead to wrong answers in many
    situations;  one (of which I am sure you are already familiar)
    is when some of the variables range, say, from 1 to 10, and others
    range from 1,000,000,000,000 to 1,000,000,000,010.  Differences
    and proportions (which look great in the closed form formula) can
    cause instant "... can't tell the difference between zero and a
    very small number ..." problems.  In first semester courses one is
    taught to "center and scale" such variables into common ranges
    before hauling out the cookbook formula.

    I am not sufficiently versed in the details of statistical computing
    to spell out in detail the horrendously more complex problems one
    encounters in many of the more sophisticated matrix manipulations
    (although I have seen Ron Helms write many of them out on the
    blackboard), but I am sure that someone on the list can point to
    one of the better texts on statistical computing.  Just the
    choice of WHICH matrix decomposition routine to use for a particular
    task often requires a high-powered consult.  (I perceive this as
    a problem quite different from the "approximate" formulae
    Dr. Arday wrote of.)

    I close with an inadequate reference to work done by two (or more,
    perhaps) folks in the Washington, D.C., area - one at WESTAT and
    his colleague at ?Census?Labor Statistics? in the early 80s.
    For at least two years in a row (I know, because I attended their
    practice presentations at the Washington Statistical Society) they
    evaluated something like 20-30 "stat packages" for presentation
    at the annual Joint Meetings of the ASA, etc.  For some of the
    ill-conditioned data (see the Longley data in the SAS sample
    library, for example) they found that half the packages couldn't
    achieve even two digits of precision.   Which didn't stop the
    packages from reporting many more, as if they were correct.  One
    of the packages couldn't even get the first digit right.

    The lesson here is that, unless one is using carefully chosen
    textbook datasets with low collinearity and "nice" values, the
    implementations of the textbook formulae are not immaterial.
    If an investigator wishes to avoid the work of learning about
    the complexities of statistical computing, then perhaps he or she
    could consider listening to those who have done their years of
    homework.  Hardly anyone can be an expert in everything, after all.
    I suppose this sounds like a flame, although it isn't intended that
    way;  I write only because I feel it would be unprofessional to
    permit misconceptions to go without rebuttal.


                         Phil Gallagher
                         uphilg@unc
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