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Formula Generator - CHIINV function

The CHIINV function calculates the inverse of the right-tailed chi-squared distribution. It is commonly used in statistical analysis to determine critical values, estimate confidence intervals, and calculate sample sizes for chi-squared tests. The function takes two arguments: the probability (a value between 0 and 1) and the degrees of freedom (a positive integer). The probability represents the area under the chi-squared distribution curve to the right of the critical value, and the degrees of freedom determine the shape of the distribution.
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How to generate an CHIINV formula using AI.

To get the CHIINV formula, you can ask the AI chatbot the following question: "What is the Excel formula to calculate the inverse of the chi-squared cumulative distribution function (CHIINV)?"

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CHIINV formula syntax.

The CHIINV function in Excel is used to calculate the inverse of the chi-squared distribution. The syntax for the CHIINV function is: CHIINV(probability, degrees_freedom) - probability: This is the probability at which you want to evaluate the inverse chi-squared distribution. It must be between 0 and 1. - degrees_freedom: This is the number of degrees of freedom of the chi-squared distribution. It must be a positive integer. The CHIINV function returns the value x such that the cumulative distribution function (CDF) for the chi-squared distribution is equal to the given probability. This can be interpreted as the value at which the chi-squared distribution cuts off the specified probability. Here's an example of how to use the CHIINV function: =CHIINV(0.05, 10) This formula calculates the inverse chi-squared distribution for a probability of 0.05 and 10 degrees of freedom. The result will be the value x such that the cumulative distribution function (CDF) for the chi-squared distribution is equal to 0.05.

Use Cases & Examples In this use case, we use the CHIINV function in Excel to calculate the inverse of the chi-squared cumulative distribution. This function is commonly used in statistical analysis to determine the critical value for a given probability and degrees of freedom.
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