The answer of that question is19.
Answer:
Check Explanation
Step-by-step explanation:
According to the Central limit theorem, the population mean (μ) is approximately equal to the mean of sampling distribution (μₓ).
And the standard deviation of the sampling distribution (σₓ) is related to the population standard deviation (σ) through
Standard deviation of the sampling distribution = (Population standard deviation)/(√N)
where N = Sample size
σₓ = (σ/√N)
So, population mean (μ) = Mean of sampling distribution (μₓ)
Population Standard deviation = (Standard deviation of the sampling distribution) × √N
= σ × √N
A) The expected value of a given distribution is simply equal to the mean of that distribution.
Hence, the expected value of random variable Y thay varies with different samples is given as
E(Y) = Mean of sampling distribution = μₓ
But μₓ = μ
Hence, E(Y) = μ (Proved)
B) Var (Y) is given as the square of the random distribution's standard deviation.
Var (Y) = (standard deviation of the sampling distribution)²
= (σ/√N)²
= (σ²/N) (Proved)
Hope this Helps!!!
