A population consists of six values (6, 9, 12, 15, 18, and 21). (with illustration)
e. Determine the mean, variance and standard deviation of the sample mean.
We have population values 6,9,12,15,21, population size N=5 and sample size n=3.
Mean of population "(\\mu)" = "\\dfrac{6+9+12+15+21}{5}=12.6"
Variance of population
"+5.76+70.56)=26.64"
"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{26.64}\\approx5.1614"
A. Select a random sample of size 3 without replacement. We have a sample distribution of sample mean.
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 & 6,9,12 & 9 \\\\\n \\hdashline\n 2 & 6,9,15 & 10 \\\\\n \\hdashline\n 3 & 6,9,21 & 12 \\\\\n \\hdashline\n 4 & 6,12,15 & 11 \\\\\n \\hdashline\n 5 & 6,12,21 & 13 \\\\\n \\hdashline\n 6 & 6,15,21 & 14 \\\\\n \\hdashline\n 7 & 9,12,15 & 12 \\\\\n \\hdashline\n 8 & 9,12,21 & 14 \\\\\n \\hdashline\n 9 & 9,15,21 & 15 \\\\\n \\hdashline\n 10 & 12,15,21 & 16 \\\\\n \\hdashline\n\\end{array}"B.
Mean of sampling distribution
"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=\\dfrac{126}{10}=12.6=\\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{1632}{10}-(12.6)^2=4.44= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})""\\sigma_{\\bar{X}}=\\sqrt{4.44}\\approx2.1071"
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