Note
In Excel 2010, the CHIDIST() function was replaced with the CHISQ.DIST.RT() function, and the CHISQ.DIST() function was added to increase the accuracy of the results. To ensure the backward compatibility of CHISQ.DIST.RT(), the CHIDIST() function is still available.
Syntax. CHISQ.DIST.RT(x,degrees_freedom)
Definition. This function returns the values of the right-tailed probability of chi-square distribution. The χ2 c2-distribution is required for a c2-test. Use this test to compare observed and expected values. For example, an experiment might hypothesize that the regular use of vitamins reduces the risk of colds. By comparing the observed results with the expected results, you can validate this hypothesis.
Arguments
x (required). The probability value at which you want to evaluate the distribution
degrees_freedom (required). The number of degrees of freedom
Note
If one of the arguments isn’t a numeric value, the CHISQ.DIST.RT() function returns the #VALUE! error. If x is negative, CHISQ.DIST.RT() returns the #NUM! error value.
If degrees_freedom isn’t an integer, the decimal places are truncated. If degrees_freedom is less than 1 or greater than or equal to 1010, the function returns the #NUM! error.
CHISQ.DIST.RT() is calculated as CHISQ.DIST.RT = P(X>x), where x is a c2 random variable.
Background. The CHISQ.DIST.RT() function calculates the right-tailed probability of the chi-square distribution.
See Also
You will find more information about chi-tests and significance levels in the description of the CHISQ.TEST() function.
The CHISQ.INV.RT() function is the inverse function of CHISQ.DIST.RT().
Example. Assume that you are a manufacturer of vitamins and want to prove that the regular use of Vitamin C reduces the risk of catching colds. You took two samples from the same population, where 22 of the 936 participants had a cold. The first sample contains the expected values, and the second sample contains the observed values. The goal is to prove your assumption that Vitamin C protects against colds (the null hypothesis) is correct.
You have already calculated the critical value c with a given probability (significance level a) of 2.5 percent, as well as the degrees of freedom and v as measure for the total deviation.
See Also
The descriptions of the CHISQ.TEST() and CHISQ.INV.RT() functions explain how to calculate these components.
Until now, because of the results of your statistical calculations, you have had to reject the null hypothesis—the assumption that Vitamin C protects against colds.
Therefore, you want to perform one last test to confirm the null hypothesis. You use the CHISQ.DIST.RT() function to calculate the probability for v, to compare the significance level and the calculated probability value.
Figure 12-19 shows how the probability for v is calculated.
If you compare CHISQ.DIST.RT() for v, you can see that v is smaller than the significance level. As a consequence, the null hypothesis is confirmed.