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