The CHIINV function in Google Sheets is an essential statistical tool used to find the inverse of the right-tailed chi-squared distribution. It is particularly useful in hypothesis testing and statistical analysis, allowing users to generate critical values based on specified confidence levels and degrees of freedom. This function is widely applicable in fields such as finance, social sciences, and engineering, where analysis of variance is common.

Syntax

CHIINV(probability, degrees_freedom)

• probability: This is the probability associated with the chi-squared distribution. It should be a number between 0 and 1.
• degrees_freedom: This represents the degrees of freedom of the chi-squared distribution, which must be a positive integer.

Example #1

CHIINV(0.95, 10)

In this example, the function will calculate the chi-squared critical value for a probability of 0.95 with 10 degrees of freedom. The result would typically be around 18.307. This means that 5% of the area under the chi-squared distribution lies to the right of this value.

Example #2

CHIINV(0.90, 5)

This function call determines the critical value for a probability of 0.90 with 5 degrees of freedom. The result would be approximately 11.070, indicating that 10% of the chi-squared distribution area is above this threshold.

Example #3

CHIINV(0.99, 15)

Here, the function calculates the chi-squared critical value at a probability of 0.99 with 15 degrees of freedom. The output would be around 27.488, which signifies that only 1% of the distribution lies to the right of this value.

Error handling

• NUM!: This error occurs if the probability is either less than 0 or greater than 1, or if the degrees_freedom is not a positive integer.
• VALUE!: This arises when the probability or degrees_freedom arguments are non-numeric values.

Conclusion

In summary, the CHIINV function in Google Sheets is a valuable resource for statisticians and data analysts, enabling them to efficiently determine critical chi-squared values. Understanding its syntax and usage can significantly enhance the ability to conduct robust statistical analyses. This function is a key component in making data-driven decisions across various fields.