Explain why the formulas for sample variance and population variance are different.
A. Variance is defined as the mean deviation, and, for a population, is computed as the sum of deviations divided by N. The sample variance will be biased and will consistently underestimate the corresponding population value.
B. Variance is defined as the mean squared deviation, and, for a population, is computed as the sum of squared deviations divided by N. The sample variance will be biased and will consistently underestimate the corresponding population value.
C. Variance is defined as the mean squared deviation, and, for a population, is computed as the sum of deviations divided by N. The sample variance will be biased and will consistently underestimate the corresponding population value.

Distinctions are also the mean squared variance just are calculated as the number of standard differences divided by "N" and for a population. After all, if a sample uses this very same definition.

Its standard deviation would become partially skewed as well as the resulting community value becomes continuously underestimate.
Its experimented formula consequently includes a correction modification for a bias. Its modification involves dividing by instead of n.

ellefashionista ellefashionista · Answer 2 · 2021-09-15T03:38:16+02:00

Answer:

The answer is "option C".

Step-by-step explanation:

Distinctions are also the mean squared variance just are calculated as the number of standard differences divided by "N" and for a population. After all, if a sample uses this very same definition.

Its standard deviation would become partially skewed as well as the resulting community value becomes continuously underestimate.

Its experimented formula consequently includes a correction modification for a bias. Its modification involves dividing by instead of n.