| 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 p-value 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) two-sample 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.