@b@cSign Test

@bDescription

The Sign Test is typically used to test the null hypothesis that two matched
samples have the same central tendency. To that end, the test considers only
the number of times one variable's value exceeds the value of the other as a
percentage of all non-equal matched pairs. In essence, it asks the question,
how likely is it that this proportion would occur randomly.

It is a non-parametric test, and, as such, requires no assumptions about the
underlying distribution of the data. 


@bNotes

The implementation in xldlas uses an approximation to the binomial
distribution (a beta function), so is inaccurate for very small sample
sizes.



@b@cTest for Equality of Means

@bDescription

This is a standard t-test of two sample means. It is predicated on the
assumption that both samples are drawn from populations that have equal
variances. This is highly unrealistic.


@bNotes

If you compare the variance calculations from this test to those supplied by
the Summarize command, you may be surprised to get different numbers. Welcome
to the population versus sample problem. When summarizing data, xldlas
generally assumes that you are dealing with a population (not a sample).
When performing the mean equality test, however, it has to assume you have
sample data (if you're testing population data, then you already know the
answer!). If you want the summarize command to assume you have sample data,
then click on the "Assume Sample Data" checkbox in the Preferences window. 

@b@cDurbin-Watson Test

@bDescription

The Durbin-Watson test is generally used to look for autocorrelation in the
residuals of a time-series regression. It would be very sensible, therefore,
to run a regression and subtract the independent variable from the fitted
values in order to generate data for this test. 

Although this does take a few steps, it is much more flexible than just
reporting the results of the test in the regression output.

@bNotes

xldlas just calculates the test statistic (d); it does not calculate the
confidence/significance. I'll be glad to add this as soon as someone can
send me a formula for calculating it.
