The F.INV.RT function in Google Sheets is an essential statistical tool used to calculate the inverse of the right-tailed F probability distribution. This function is particularly useful in various fields including finance, research, and quality control, as it allows users to determine critical values for hypothesis testing when comparing variances across different datasets.

Syntax

F.INV.RT(probability, degrees_freedom1, degrees_freedom2)

• probability: A value between 0 and 1 representing the probability associated with the F distribution.
• degrees_freedom1: The numerator degrees of freedom, typically associated with the variance of the first sample.
• degrees_freedom2: The denominator degrees of freedom, generally linked to the variance of the second sample.

Example #1

F.INV.RT(0.95, 5, 10)

This function call calculates the critical value for an F-distribution at a 95% probability level with 5 and 10 degrees of freedom. The result returns approximately 4.956.

Example #2

F.INV.RT(0.90, 3, 12)

Here, the function computes the critical value for a 90% probability level with 3 and 12 degrees of freedom. The result is around 3.437.

Example #3

F.INV.RT(0.85, 2, 5)

This call determines the critical value for an F-distribution at an 85% probability level, using 2 and 5 degrees of freedom. The expected output is roughly 5.897.

Error handling

• NUM!: This error occurs when the probability is not between 0 and 1, or when the degrees of freedom are not both greater than zero.
• VALUE!: Returned when any of the arguments are non-numeric.
• DIV/0!: Indicates that the degrees of freedom cannot be zero, signifying that you cannot perform the calculation under those conditions.

Conclusion

In summary, the F.INV.RT function is a powerful statistical tool for determining critical values of the F probability distribution. By enabling users to perform detailed statistical analyses, it aids in decision-making processes across various disciplines. Understanding this function can greatly enhance the accuracy of your statistical evaluations and hypotheses testing.