I assume that you are are using the Regression tool in the Analysis ToolPak.
For each coefficient, you get the estimate and the standard error of
that estimate (also given by LINEST with the stats argument set to
TRUE). The t statistic is the ratio of the estimate (est) divided by
the standard error (se). This provides a test of whether the model
coefficient is actually zero. To test whether the model coefficient is
equal to some other pre-specified hypothesized value (hv), use
t = (est-hv)/se
The p-value is calculated by
TDIST(ABS(t),df,2)
is the probability of getting a t-statistic at least as extreme as was
observed with your data, if the hypothesized value (zero by default)
were known to be true. If that p-value is too small (often considered
to be <0.05, but that value is not set in stone) then the corresponding
coefficient is considered "statistically significant". The logic, is
that you choose to believe that the hypothesized value is wrong, rather
than believing that you have seen something unusual.
The ATP Regression tool also gives confidence intervals for the
coefficients, which may be more informative than a simple significance
test. The confidence interval indicates how much uncertainty is
associated with the estimate. If the 95% confidence interval for a
coefficient includes zero, then that coefficient is statistically
significantly different from zero at the 0.05 level (p-value <=0.05)
This is a once over lightly of some concepts that are easily abused.
You would be well served to take a class or at least read a text on
statistics before assuming that you know much.
Jerry
