Performing F-Test in R programming – var.test() Method

With the help of

method, we can perform the f-test between two normal population with some hypothesis that variances of two populations are equal in R programming.**var.test()**

Syntax:`var.test()`

Return:Returns the F-Test score for some hypothesis.

**Example 1:**

`x <` `-` `rnorm(` `50` `, mean` `=` `0` `)` `y <` `-` `rnorm(` `50` `, mean` `=` `1` `)` ` ` `# Using var.test() method` ` ` `gfg <` `-` `var.test(x, y)` ` ` `print` `(gfg)` |

**Output:**

F test to compare two variances data: x and y F = 1.0779, num df = 49, denom df = 49, p-value = 0.794 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 0.6116778 1.8994484 sample estimates: ratio of variances 1.077892

**Example 2:**

`x <` `-` `rnorm(` `50` `, mean` `=` `1.2` `)` `y <` `-` `rnorm(` `50` `, mean` `=` `1.8` `)` ` ` `# Using var.test() method` ` ` `gfg <` `-` `var.test(x, y)` ` ` `print` `(gfg)` |

**Output:**

F test to compare two variances data: x and y F = 2.382, num df = 49, denom df = 49, p-value = 0.002911 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 1.351715 4.197491 sample estimates: ratio of variances 2.381976