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
This might help ....
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
answer is option c .
This the definition definition of variance . Variance never be negative.Variance is the average squared deviation from the mean. Notice the word “squared”. It may be zero. Variance of constant is zero.Theorem 2 a
Answer C
https://www.macroption.com/can-variance-be-negative/