Perhaps surprising to engineers, Excel's lackluster performance as a
statistics engine has been well-documented by practicing statisticians for
more than a decade.
Engineers use Excel for many numerical tasks. They don't use if for finite element computations (although I suppose you could coax Excel to do that too)
because it wasn't intended for that purpose. They shouldn't use it
for statistics either, and for the same reason - Excel doesn't do a very
good job at tasks for which it was not intended.
which in addition to being the best software for applied statistics
available anywhere, at any price, is FREE. There is
another reason not to use Excel for statistics - it is statistically
unreliable. Here are some documented examples:
- A. Talha Yalta, "The
accuracy of statistical distributions in Microsoft® Excel 2007,"
Computational Statistics & Data Analysis, Volume 52, Issue 10, 15 June 2008,
"Abstract - We provide an assessment of the statistical distributions in
Microsoft® Excel versions 97 through 2007 ...
We find that the accuracy of various statistical functions in Excel 2007
range from unacceptably bad to acceptable but significantly inferior in
comparison to alternative implementations. In particular, for the binomial,
Poisson, inverse standard normal, inverse beta, inverse student’s t, and
inverse F distributions, it is possible to obtain results with zero accurate
digits as shown with numerical examples."
Don't use Excel for statistical calculations if you require a
credible result. Use R (or JMP, Minitab, SAS,
SPSS, S-Plus, Stata, ... all of which have substantial licensing fees -
R is free).