Updating formula for the sample covariance and correlation
tests that are described in Two Sample t-Test with Equal Variances and Two Sample t-Test with Unequal Variances.
We begin with an example which is an extension of Example 1 of Two Sample t-Test with Equal Variances.
It's useful when creating statistical models since low variance can be a sign that you are over-fitting your data. Calculate the mean of the sample by adding all the data points together and dividing by the number of data points. Subtract the mean from each data point and square each result. Add all the squared values together and divide them by n - 1, where “n” is the number of data points, to get the variance.
This argument is not relevant for SSTot, df Tot and MSTot (since the result is the same in either case).
These functions ignore any empty or non-numeric cells.
We can therefore use the F-test (see Two Sample Hypothesis Testing of Variances) to determine whether or not to reject the null hypothesis.
Theorem 1: If a sample is made as described in Definition 1, with the can also be calculated as DEVSQ(G7: G9)/F7.