var.test {stats}  R Documentation 
Performs an F test to compare the variances of two samples from normal populations.
var.test(x, ...)
## Default S3 method:
var.test(x, y, ratio = 1,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, ...)
## S3 method for class 'formula'
var.test(formula, data, subset, na.action, ...)
x , y 
numeric vectors of data values, or fitted linear model
objects (inheriting from class 
ratio 
the hypothesized ratio of the population variances of

alternative 
a character string specifying the alternative
hypothesis, must be one of 
conf.level 
confidence level for the returned confidence interval. 
formula 
a formula of the form 
data 
an optional matrix or data frame (or similar: see

subset 
an optional vector specifying a subset of observations to be used. 
na.action 
a function which indicates what should happen when
the data contain 
... 
further arguments to be passed to or from methods. 
The null hypothesis is that the ratio of the variances of the
populations from which x
and y
were drawn, or in the
data to which the linear models x
and y
were fitted, is
equal to ratio
.
A list with class "htest"
containing the following components:
statistic 
the value of the F test statistic. 
parameter 
the degrees of the freedom of the F distribution of the test statistic. 
p.value 
the pvalue of the test. 
conf.int 
a confidence interval for the ratio of the population variances. 
estimate 
the ratio of the sample variances of 
null.value 
the ratio of population variances under the null. 
alternative 
a character string describing the alternative hypothesis. 
method 
the character string

data.name 
a character string giving the names of the data. 
bartlett.test
for testing homogeneity of variances in
more than two samples from normal distributions;
ansari.test
and mood.test
for two rank
based (nonparametric) twosample tests for difference in scale.
x < rnorm(50, mean = 0, sd = 2)
y < rnorm(30, mean = 1, sd = 1)
var.test(x, y) # Do x and y have the same variance?
var.test(lm(x ~ 1), lm(y ~ 1)) # The same.