How is variance calculated?

Variance is a statistical measure that represents how far a set of numbers is spread out from their average value, or mean. To calculate variance, one begins by determining the mean of the dataset. Once the mean is found, the next step involves calculating the difference between each individual value in the dataset and the mean. This difference is then squared to eliminate any negative values and to give greater weight to larger deviations.

After squaring the differences, these squared values are summed up. Finally, in the case of a population variance, this sum of squared differences is divided by the total number of entries in the dataset. If calculating the sample variance, however, you would divide by one less than the total number of entries (n-1) to account for the sample’s estimation of the population variance.

This method of squaring the differences ensures that any deviations from the mean are accurately reflected, thus providing a clear measure of variability within the dataset. The correct calculation method aligns perfectly with the description of the third choice, making it the accurate answer.