Answer to Question #341572 in Statistics and Probability for Andrew

Question #341572

Consider a population consisting of 2, 4, 6, 8 and 10. Suppose samples of size 3 are drawn from this population.







a. Describe the sampling distribution of the sample means


b. What are the mean and variance of the sampling distribution of the sample means? c. Construct a histogram for the sampling distribution.

1
Expert's answer
2022-05-17T08:55:06-0400

We have population values 2,4,6,8,10, population size N=5 and sample size n=3.

Mean of population "(\\mu)" = "\\dfrac{2+4+6+8+10}{5}=6"

Variance of population 


"\\sigma^2=\\dfrac{\\Sigma(x_i-\\bar{x})^2}{N}=\\dfrac{16+4+0+4+16}{5}=8"


"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{8}=2\\sqrt{2}\\approx2.8284"


a. The number of possible samples which can be drawn without replacement is "^{N}C_n=^{5}C_3=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 & 2,4,6 & 12\/3 \\\\\n \\hdashline\n 2 & 2,4,8 & 14\/3 \\\\\n \\hdashline\n 3 & 2,4,10 & 16\/3\\\\\n \\hdashline\n 4 & 2,6,8 & 16\/3 \\\\\n \\hdashline\n 5 & 2,6,10 & 18\/3 \\\\\n \\hdashline\n 6 & 2,8,10 & 20\/3 \\\\\n \\hdashline\n 7 & 4,6,8 & 18\/3 \\\\\n \\hdashline\n 8 & 4,6,10 & 20\/3 \\\\\n \\hdashline\n 9 & 4,8,10 & 22\/3 \\\\\n \\hdashline\n 10 & 6,8,10 & 24\/3 \\\\\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\n12\/3 & 1\/10 & 12\/30 & 144\/90 \\\\\n \\hdashline\n 14\/3 & 1\/10 & 14\/30 & 196\/90 \\\\\n \\hdashline\n 16\/3 & 2\/10 & 32\/30 & 512\/90 \\\\\n \\hdashline\n 18\/3 & 2\/10 & 36\/30 & 648\/90 \\\\\n \\hdashline\n 20\/3 & 2\/10 & 40\/30 & 800\/90 \\\\\n \\hdashline\n 22\/3 & 1\/10 & 22\/30 & 484\/90 \\\\\n \\hdashline\n 24\/3 & 1\/10 & 24\/30 & 576\/90 \\\\\n \\hdashline\n\\end{array}"



b. Mean of sampling distribution 

"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=6=\\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{3360}{90}-(6)^2=\\dfrac{4}{3}= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})"

"\\sigma_{\\bar{X}}=\\sqrt{\\dfrac{4}{3}}\\approx1.1547"


c.

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