Al,
You can do this one of two ways using the distribution.
One way is to compute the cumulative probability based on the observed
value:
ChiSquaredDistributionImpl dist = new ChiSquaredDistributionImpl(df);
double p = dist.cumulativeProbability(observed);
then compare p to some confidence level or alpha value. Note,
cumulativeProbablity returns the probability of observing a value less
than or equal to the given value (i.e. the lower tail probability).
More than likely, you'll want the upper tail probability for comparison
purposes, which is simply 1.0  p.
The other was is to compute the critical value for a given confidence
level or alpha value:
ChiSquaredDistributionImpl dist = new ChiSquaredDistributionImpl(df);
double value = dist.inverseCumulativeProbability(level);
then compare value to the observed value. Again, take into account
inverseCumulativeProbability uses the lower tail for computing critical
values.
HTH,
Brent Worden
Original Message
From: Al Lelopath [mailto:jdaues@gmail.com]
Sent: Wednesday, March 05, 2008 9:32 AM
To: Jakarta Commons Users List
Subject: [math] ChiSquaredDistributionImpl
I am using ChiSquaredDistributionImpl, but I am not confident I am
doing it correctly.
I've calculated the "observed" value (to compare to the critical
value) and the degrees of freedom.
I create the impl like so:
ChiSquaredDistributionImpl chiSquaredDistributionImpl = new
ChiSquaredDistributionImpl(degreesOfFreedom);
double chiSquareCriticalValue =
chiSquaredDistributionImpl.cumulativeProbability(degreesOfFreedom);
but something does seem quite right.
For one thing, I haven't specified a confidence interval for the
critical value, i.e. 95%? 98%?
Is there an example of how to do this?

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