Glet x1, x2, . . , xn be a random sample from any distribution with mean µ and variance σ 2 . since σ 2 = v ar[x1] = e[(x − µ) 2 ], it makes sense to estimate this probability weighted average with a similar sample average such as σc2 : = xn i=1 (xi − x) 2 n . (a) show that this is a biased estimator of σ 2 . what is the bias? (b) adjust your estimator from part (a) to make it an unbiased estimator. your result will be known as the sample variance and will be denoted by s 2 . (note: some people use the more intuitive biased estimator given here as their definition of the sample variance and also denote it with s 2 . more people use the unbiased estimator you will be finding here. those are the cool people! )