If the difference between the expectation of the square of a random variable $\left(E\left[X^2\right]\right)$ and the square of the expectation of the random variable $\left(E\left[X\right]\right)^2$ is denoted by $R$, then
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Answer is (C).The difference between $(E[X²])$ and $($$E[X]$$)$$^{2}$ is called variance of a random variable. Variance measures how far a set of numbers is spread out. (A variance of zero indicates that all the values are identical.) A non-zero variance is always positive.
V(x) = E(x^2) - [E(x)]^{2 }= R
where V(x) is the Variance of x, Since Variance is Square and Hence Never be Neagtive, R>=0
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@sahil you can see my response sheet...