F Test to Compare Two Variances
DESCRIPTION:
Performs an F test to compare variances of two samples from normal populations.
USAGE: 
var.test(x, y, alternative="two.sided", conf.level=.95)
REQUIRED ARGUMENTS:
x,y:
numeric vectors. NAs and Infs are allowed but will be removed.
OPTIONAL ARGUMENTS:
alternative:
character string, one of "greater", "less" or "two.sided", or just the initial letter of each, indicating the specification of the alternative hypothesis. alternative refers to the true population variance for x in relation to that for y.
conf.level:
confidence level for the returned confidence interval, restricted to lie between zero and one.
VALUE:
A list of class "htest", containing the following components:

statistic:
the F-statistic, with names attribute "F".
parameters:
vector of length 2 giving the degrees of freedom of the F-distribution associated with statistic. Component parameters has names attribute c("num df", "denom df").
p.value:
the p-value for the test.
conf.int:
a confidence interval (vector of length 2) for the ratio of the true population variance for x to that for y. The confidence level is recorded in the attribute conf.level.
estimate:
vector of length 2 giving the sample variances; these estimate the corresponding population parameters. Component estimate has a names attribute describing its elements.
null.value:
always 1, the value of the ratio of population variances specified by the null hypothesis. Component null.value has names attribute "ratio of variances".
alternative:
records the value of the input argument alternative: "greater", "less" or "two.sided".
method:
character string giving the name of the method used.
data.name:
character string (vector of length 1) containing the actual names of the input vectors x and y.
NULL:
The null hypothesis states that the population variances are equal. The alternative hypothesis states that the variance of the population from which x is drawn is greater, less than, or simply not equal to the variance of the population from which y is drawn, depending on the value of input argument alternative.
TEST:
It is assumed that both x and y are drawn from normal populations. Outliers in the data may have a significant effect on the results through their relatively strong influence on the variance estimates.
DETAILS:
The F statistic (returned component statistic) is defined as var(x)/var(y), the ratio of sample variances. If both x and y were drawn from normal populations, then under the null hypothesis the statistic has an F distribution with length(x) - 1 and length(y) - 1 degrees of freedom. See the hardcopy help-file for a precise definition of the p-value, and expressions for the confidence intervals.
REFERENCES:
Box, G. E. P., Hunter, W. G. and Hunter, J. S. (1978). Statistics for Experimenters. New York: Wiley.

Snedecor, G. W. and Cochran, W. G. (1980). Statistical Methods, 7th ed. Ames, Iowa: Iowa State University Press.

SEE ALSO:
var , F .
EXAMPLES: 
var.test(x, y, conf.level=.9)
        # The null hypothesis is that 'x' and 'y' come from
        # populations with the same variance. These populations
        # are assumed to be normal. The alternative hypothesis is
        # that the population variances are not equal. The
        # confidence interval for the ratio of the population
        # variances will have a confidence level of 0.90.
var.test(x, y, alternative="greater")
        # The null hypothesis is as above. The alternative
        # hypothesis is that the population variance for
        # 'x' is greater than that for 'y'.