Random samples with size 5 are drawn from the population containing the values 26, 32, 41, 50, 58, and 63. Construct the distribution of the mean of all samples.
We have population values 26, 32, 41, 50, 58, 63, population size N=6 and sample size n=5.
Mean of population "(\\mu)" = "\\dfrac{26+32+41+50+58+63}{6}=45"
Variance of population
"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{\\dfrac{1064}{6}}\\approx13.3167"
The number of possible samples which can be drawn without replacement is "^{N}C_n=^{6}C_5=6."
"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c}\n no & Sample & Sample \\\\\n& & mean\\ (\\bar{x})\n\\\\ \\hline\n 1 & 26, 32, 41, 50, 58 & 207\/5 \\\\\n \\hdashline\n 2 & 26, 32, 41, 50, 63 & 212\/5 \\\\\n \\hdashline\n 3 & 26, 32, 41, 58, 63 & 220\/5\\\\\n \\hdashline\n 4 & 26, 32, 50, 58, 63 & 229\/5 \\\\\n \\hdashline\n 5 & 26, 41, 50, 58, 63 & 238\/5 \\\\\n \\hdashline\n 6 & 32, 41, 50, 58, 63 & 244\/5 \\\\\n \\hdashline\n\\end{array}"Mean of sampling distribution
"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=45=\\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{304814}{150}-(45)^2=\\dfrac{1064}{150}= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})""\\sigma_{\\bar{X}}=\\sqrt{\\dfrac{1064}{150}}\\approx2.6633"
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