vartest2
              Two-sample F test for equal variances.
 h = vartest2 (x, y) performs an F test of the
 hypothesis that the independent data in vectors x and y come from
 normal distributions with equal variance, against the alternative that they
 come from normal distributions with different variances.  The result is
 h = 0 if the null hypothesis ("variance are equal") cannot be rejected
 at the 5% significance level, or h = 1 if the null hypothesis can be
 rejected at the 5% level.
 x and y may also be matrices or N-D arrays.  For matrices,
 vartest2 performs separate tests along each column and returns a
 vector of results.  For N-D arrays, vartest2 works along the first
 non-singleton dimension and x and y must have the same size along
 all the remaining dimensions.
 vartest treats NaNs as missing values, and ignores them.
 [h, pval] = vartest (…) returns the p-value.  That
 is the probability of observing the given result, or one more extreme, by
 chance if the null hypothesisis true.
 [h, pval, ci] = vartest (…) returns a
  confidence interval for the true ratio
 var(X)/var(Y).
 [h, pval, ci, stats] = vartest (…)
 returns a structure with the following fields:
| fstat | the value of the test statistic | |
| df1 | the numerator degrees of freedom of the test | |
| df2 | the denominator degrees of freedom of the test | 
 […] = vartest (…, name, value), …
 specifies one or more of the following name/value pairs:
| Name | Value | |
|---|---|---|
| "alpha" | the significance level. Default is 0.05. | |
| "dim" | dimension to work along a matrix or an N-D array. | |
| "tail" | a string specifying the alternative hypothesis | 
| "both" | variance is not v (two-tailed, default) | |
| "left" | variance is less than v (left-tailed) | |
| "right" | variance is greater than v (right-tailed) | 
See also: ttest2, kstest2, bartlett_test, levene_test
Source Code: vartest2