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4.1.5. F , x, let (S)2=(1/n)? 1(x-?Find or a random sample X1, EKS)2]. Compare this with EG). i-1
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By definition, \small (S')^2 becomes the variance of the Random Sample X. Where variance is the expectation of the squared deviation of a random variable from its mean.

{\displaystyle {\begin{aligned}\operatorname {Var} (X)&=\operatorname {E} \left[(X-\operatorname {E} [X])^{2}\right]\\&=\operatorname {E} \left[X^{2}-2X\operatorname {E} [X]+\operatorname {E} [X]^{2}\right]\\&=\operatorname {E} \left[X^{2}\right]-2\operatorname {E} [X]\operatorname {E} [X]+\operatorname {E} [X]^{2}\\&=\operatorname {E} \left[X^{2}\right]-\operatorname {E} [X]^{2}\end{aligned}}}

\small S' = E[X^2] - E[X]^2

\small E[S'] = E[E[X^2]] - E[E[X]^2]

Now,

Since the expected value of an expected value is just that. It stops being random once you take one expected value, so iteration doesn't change.

So,

\small E[S' ]= E[X^2] - E[X]^2

Also \small S^2 is the sample variance with the formula:

\small S^2 = \frac{1}{n-1}\sum (X_i - \overline{X})^2

So, By comparision,

\small S^2 = \frac{n}{n-1}(\frac{1}{n}\sum (X_i - \overline{X})^2)

\small S^2 = \frac{n}{n-1}(S')^2

So,

\small E[S^2] = \frac{n}{n-1}E[(S')^2]

So, \small E[S^2]    is \small \frac{n}{n-1} times \small E[(S')^2] .

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