oh my...
I'd like to see the statistics on it before jumping to a conclusion that
the American preference is "chi-square" and the British preference is
"chi-squared". I don't see that at all.
------
In keeping with the pronunciation of x^2 and 3^2, maybe "chi-squared" makes
the most sense,.
The "chi-square"? Because the iterated dentals in "chi-squared
distribution" and "chi-squared test" are a little cumbersome to pronounce,
an even slightly lazy pronunciation would sound like "chi-square
distribution" and "chi-square test". There's no need to write it that way
though.
-Dan
On Fri, Oct 18, 2019 at 2:28 PM Richard M. Heiberger <rmh at temple.edu> wrote:
What a delightful question. Bill Cochran discussed this in class
one day about 50 years ago. He said the British usage (which I think
he said was chi-squared,
as is consistent with the other memories in this thread)
is what he learned and previously used. But he had been in the US for
so long that he was now using
the American preference (chi-square).
Rich
On Fri, Oct 18, 2019 at 8:51 AM Martin Maechler
<maechler at stat.math.ethz.ch> wrote:
As it's Friday ..
and I also really want to clean up help files and similar R documents,
both in R's own sources and in my new 'DPQ' CRAN package :
As a trained mathematician, I'm uneasy if a thing has
several easily confusable names, .. but as somewhat
humanistically educated person, I know that natural languages,
English in this case, are much more flexible than computer
languages or math...
Anyway, back to the question(s) .. which I had asked myself a
couple of months ago, and already remained slightly undecided:
The 0-th (meta-)question of course is
0. Is it worth using only one written form for the
?? - distribution, e.g. "everywhere" in R?
The answer is not obvious, as already the first few words of the
(English) Wikipedia clearly convey:
The URL is https://en.wikipedia.org/wiki/Chi-squared_distribution
and the main title therefore also
"Chi-squared distribution"
Then it reads
This article is about the mathematics of the chi-squared
distribution. For its uses in statistics, see chi-squared
test. For the music [...]
In probability theory and statistics, the chi-square
distribution (also chi-squared or ?2-distribution) with k
degrees of freedom is the distribution of a sum of the squares
of k independent standard normal random variables.
The chi-square distribution is a special case of the gamma
distribution and is one of the most widely used probability
distributions in inferential statistics, notably in hypothesis
testing [........]
[........]
So, in title and 1st paragraph its "chi-squared", but then
everywhere(?) the text used "chi-square".
Undoubtedly, Wilson & Hilferty (1931) has been an important
paper and they use "Chi-square" in the title;
also Johnson, Kotz & Balakrishnan (1995)
see R's help page ?pchisq use "Chi-square" in the title of
chapter 18 and then, diplomatically for chapter 29,
"Noncentral ??-Distributions" as title.
So it seems, that historically and using prestigious sources,
"chi-square" to dominate (notably if we do not count "??" as an
alternative).
Things look a bit different when I study R's sources; on one
hand, I find all 4 forms (s.Subject); then in the "R source
history", I see
$ svn log -c11342
------------------------------------------------------------------------
r11342 | <....> | 2000-11-14 ...
Use `chi-squared'.
------------------------------------------------------------------------
which changed 16 (if I counted correctly) cases of 'chi-square' to
I have not found any R-core internal (or public) reasoning about
that change, but had kept it in mind and often worked along that "goal".
As a consequence, "statistically" speaking, much of R's own use has been
standardized to use "chi-squared"; but as I mentioned, I still
find all 4 variants even in "R base" package help files
(which of course I now could quite quickly change (using Emacs M-x
but
... "as it is Friday" ... I'm interested to hear what others
think, notably if you are native English (or "American" ;-)
speaking and/or have some extra good knowledge on such
matters...
Martin Maechler
ETH Zurich