# F.INV.RT

Formulas / F.INV.RT
Calculate the inverse of the right-tailed F probability distribution.
`F.INV.RT(probability,deg_freedom1,deg_freedom2)`
• probability - associated with the F cumulative distribution
• deg_freedom1 - required, numerator degrees of freedom
• deg_freedom2 - required, denominator degrees of freedom

## Examples

• `=F.INV.RT(0.95,3,4)`

The F.INV.RT function can be used to calculate the inverse of the F probability distribution that returns a value of 2.79, which is the inverse of the F probability distribution with a probability of 0.95 and degrees of freedom of 3 and 4, respectively.

• `=F.INV.RT(0.90,5,6)`

The F.INV.RT function can also be used to find the critical value for a given probability and degrees of freedom. For example, if we need to find the critical value for a probability of 0.90 and degrees of freedom of 5 and 6, we can use the function which returns a value of 2.43.

## Summary

The F.INV.RT function returns the inverse of the F probability distribution and produces an error value if Probability is outside of the permitted range (0 to 1).

• The F.INV.RT function returns the inverse of the (right-tailed) F probability distribution and uses an iterative search technique for the x argument.
• The F.INV.RT function requires a probability argument to calculate the inverse.

What is the F.INV.RT Function?
The F.INV.RT Function is a statistical function used to calculate the inverse of the (right-tailed) F probability distribution.
How is the F.INV.RT Function Used?
The F.INV.RT Function can be used to compare the variability of two data sets.
What are the Advantages of Using the F.INV.RT Function?
• It is an efficient way to compare variability between two sets of data.
• It can be used to determine the probability of observing a difference between two data sets.
• It is a reliable way to evaluate the statistical significance of a difference between two data sets.
What are the Limitations of Using the F.INV.RT Function?
• It is only applicable when comparing two data sets.
• It is not suitable for comparing more than two sets of data.
• It does not take into account any additional factors that may affect the comparison. 