The GAMMA.INV function in Google Sheets is a powerful statistical tool designed to return the value of the inverse gamma cumulative distribution function. It requires a probability value along with two parameters: alpha and beta. This function is particularly useful in various fields such as finance, quality control, and other statistical analyses where gamma distributions are applicable. By understanding its components and how to effectively use it, users can gain valuable insights from their data.

Syntax

GAMMA.INV(probability, alpha, beta)

• probability: This is the probability at which to evaluate the inverse gamma cumulative distribution function. It should be a value between 0 and 1.
• alpha: This parameter shapes the distribution and must be greater than zero.
• beta: This is a scale parameter, which also must be greater than zero.

Example #1

GAMMA.INV(0.5, 2, 3)

In this instance, the function calculates the inverse gamma cumulative distribution function for a probability of 0.5, with alpha set to 2 and beta set to 3. The result might yield a value around 1.3094, indicating the point at which 50% of the distribution lies to the left.

Example #2

GAMMA.INV(0.75, 5, 1)

This example evaluates the inverse gamma cumulative distribution function for a probability of 0.75, with parameters alpha as 5 and beta as 1. The outcome can be approximately 6.499, showcasing where 75% of the distribution is located.

Example #3

GAMMA.INV(0.9, 4, 2)

Here, we are looking at a 90% probability with alpha set to 4 and beta set to 2. The function might return a value of about 8.8923, highlighting the specific point within the gamma distribution.

Error handling

• NUM!: This error occurs when the probability is less than 0 or greater than 1, or if either alpha or beta is not greater than 0.
• VALUE!: This indicates that the inputs are of the wrong type, such as using non-numeric values for probability, alpha, or beta.

Conclusion

In summary, the GAMMA.INV function is a crucial tool for analyzing data related to the gamma distribution. By providing the inverse cumulative distribution value based on specific probability and shape parameters, it opens up avenues for more informed decision-making in various statistical analyses. With a clear understanding of its syntax and practical application, users can leverage this function effectively in their work.