Answer:
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
Step-by-step explanation:
∵ When x is a random variable having distribution B(n, p), then for sufficiently large value of n, the following random variable has a standard normal distribution,

Where,
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Here the variable X has a binomial distribution,
Such that, np (1 - p) ≥ 10 ⇒ n is sufficiently large.
Where, n is the total numbers of trials, p is success in each trials,
So, the mean of variable X is,

And, variance of variable X is,
