A population consists of the numbers of 1-5.List all the possible samples of size 3 from this population and construct the sampling distribution of the sample mean.
We have population values 1,2,3,4,5, population size N=5 and sample size n=3.
Mean of population "(\\mu)" = "\\dfrac{1+2+3+4+5}{5}=3"
Variance of population
"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{2}\\approx1.4142"
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 & 1,2,3 & 6\/3 \\\\\n \\hdashline\n 2 & 1,2,4 & 7\/3 \\\\\n \\hdashline\n 3 & 1,2,5 & 8\/3\\\\\n \\hdashline\n 4 & 1,3,4 & 8\/3 \\\\\n \\hdashline\n 5 & 1,3,5 & 9\/3 \\\\\n \\hdashline\n 6 & 1,4,5 & 10\/3 \\\\\n \\hdashline\n 7 & 2,3,4 & 9\/3 \\\\\n \\hdashline\n 8 & 2,3,5 & 10\/3 \\\\\n \\hdashline\n 9 & 2,4,5 & 11\/3 \\\\\n \\hdashline\n 10 & 3,4,5 & 12\/3 \\\\\n \\hdashline\n\\end{array}"Mean of sampling distribution
"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=3=\\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{840}{90}-(3)^2=\\dfrac{1}{3}= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})""\\sigma_{\\bar{X}}=\\sqrt{\\dfrac{1}{3}}\\approx0.57735"
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