Random samples with size 5 are drawn from the population containing the values 26, 32, 41, 50, 58, and 63.
Determine the standard error of the sample means
We have population values 26, 32, 41, 50, 58, and 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}}"
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, 63, 58 & 220\/5 \\\\\n \\hdashline\n 4 & 26, 32, 63, 50, 58 & 229\/5 \\\\\n \\hdashline\n 5 & 26, 63, 41, 50, 58 & 238\/5 \\\\\n \\hdashline\n 6 & 63, 32, 41, 50, 58 & 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})"The standard error of the sample means is
"\\sigma_{\\bar{X}}=\\sqrt{\\dfrac{1064}{150}}\\approx2.6633"
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