Answer to Question #341562 in Statistics and Probability for Mario

Question #341562

Consider a population consisting of 2,6,8,0 and 1. Suppose samples of size 2 are drawn from this population. Find the Mean and Variance of the sampling distribution of sample means.

1
Expert's answer
2022-05-17T09:11:10-0400

We have population values 0,1,2,6,8, population size N=5 and sample size n=2.

Mean of population "(\\mu)" = "\\dfrac{0+1+2+6+8}{5}=3.4"

Variance of population 


"\\sigma^2=\\dfrac{\\Sigma(x_i-\\bar{x})^2}{N}=\\dfrac{1}{5}(11.56+5.76+1.96"




"+6.76+21.16=9.44"


"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{9.44}\\approx3.072458"


The number of possible samples which can be drawn without replacement is "^{N}C_n=^{5}C_2=10."

"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c}\n no & Sample & Sample \\\\\n& & mean\\ (\\bar{x})\n\\\\ \\hline\n 1 & 0,1 & 1\/2 \\\\\n \\hdashline\n 2 & 0,2 & 2\/2 \\\\\n \\hdashline\n 3 & 0,6 & 6\/2\\\\\n \\hdashline\n 4 & 0,8 & 8\/2 \\\\\n \\hdashline\n 5 & 1,2 & 3\/2 \\\\\n \\hdashline\n 6 & 1,6 & 7\/2 \\\\\n \\hdashline\n 7 & 1,8 & 9\/2 \\\\\n \\hdashline\n 8 & 2,6 & 8\/2 \\\\\n \\hdashline\n 9 & 2,8 & 10\/2 \\\\\n \\hdashline\n 10 & 6,8 & 14\/2 \\\\\n \\hdashline\n\\end{array}"




"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c}\n \\bar{X} & f(\\bar{X}) &\\bar{X} f(\\bar{X})& \\bar{X}^2f(\\bar{X}) \n\\\\ \\hline\n1\/2 & 1\/10 & 1\/20 & 1\/40 \\\\\n \\hdashline\n 2\/2 & 1\/10 & 2\/20 & 4\/40 \\\\\n \\hdashline\n 3\/2& 1\/10 & 3\/20 & 9\/40 \\\\\n \\hdashline\n 6\/2 & 1\/10 & 6\/20 & 36\/40 \\\\\n \\hdashline\n 7\/2 & 1\/10 & 7\/20 & 49\/40 \\\\\n \\hdashline\n 8\/2 & 2\/10 & 16\/20 & 128\/40 \\\\\n \\hdashline\n 9\/2 & 1\/10 & 9\/20 & 81\/40 \\\\\n \\hdashline\n 10\/2 & 1\/10 & 10\/20 & 100\/40 \\\\\n \\hdashline\n 14\/2 & 1\/10 & 14\/20 & 196\/40 \\\\\n \\hdashline\n\\end{array}"



Mean of sampling distribution 

"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=\\dfrac{68}{20}=3.4=\\mu"



The variance of sampling distribution 

"Var(\\bar{X})=\\sigma^2_{\\bar{X}}=\\sum\\bar{X}_i^2f(\\bar{X}_i)-\\big[\\sum\\bar{X}_if(\\bar{X}_i)\\big]^2""=\\dfrac{604}{40}-(3.4)^2=3.54= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})"

"\\sigma_{\\bar{X}}=\\sqrt{3.54}\\approx1.881489"




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