1.
We have population values "1,2,3,4,5,6" population size "N=6"
"\\mu=\\dfrac{1+2+3+4+5+6}{6}=3.5"
"\\sigma^2=\\dfrac{1}{6}((1-3.5)^2+(2-3.5)^2+(3-3.5)^2""+(4-3.5)^2+(5-3.5)^2+(6-3.5)^2)=2.92"
The number of possible samples which can be drawn without replacement is
"\\dbinom{N}{n}=\\dbinom{6}{2}=15"
"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c}\n Sample & Sample & Sample \\ mean \\\\\n No. & values & (\\bar{X}) \\\\ \\hline\n 1 & 1,2 & 1.5 \\\\\n \\hdashline\n 2 & 1,3 & 2 \\\\\n \\hdashline\n 3 & 1,4 & 2.5\\\\\n \\hdashline\n 4 & 1,5 & 3 \\\\\n \\hdashline\n 5 & 1,6 & 3.5 \\\\\n \\hdashline\n 6 & 2,3 & 2.5 \\\\\n \\hdashline\n 7 & 2,4 & 3 \\\\\n \\hdashline\n 8 & 2,5& 3.5 \\\\\n \\hdashline\n 9 & 2,6 & 4 \\\\\n \\hdashline\n 10 & 3,4 &3.5\\\\\n\\hdashline\n 11 & 3,5 & 4 \\\\\n \\hdashline\n 12 & 3,6 & 4.5 \\\\\n \\hdashline\n 13&4,5 & 4.5 \\\\\n \\hdashline\n 14 & 4,6 & 5 \\\\\n \\hdashline\n 15 & 5,6 &5.5 \\\\\n \\hline\n\\end{array}"
2.
The sampling distribution of the sample means.
"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c:c}\n& \\bar{X} & f & f(\\bar{X}) & \\bar{X}f(\\bar{X})& \\bar{X}^2f(\\bar{X}) \\\\ \\hline\n & 1.5 & 1 & 1\/15 &0.1& 0.15 \\\\\n \\hdashline\n & 2& 1 & 1\/15 & 0.13 & 0.266 \\\\\n \\hdashline\n & 2.5& 2 & 2\/15 & 0.333& 0.8333 \\\\\n \\hdashline\n & 3 & 2 & 2\/15 & 0.4& 1.2 \\\\\n \\hdashline\n & 3.5 & 3 & 3\/15 & 0.7 & 2.45 \\\\\n \\hdashline\n & 4 & 2 & 2\/15& 0.533 & 2.133 \\\\\n \\hdashline\n & 4.5 & 2 & 2\/15 & 0.6 & 2.7 \\\\\n \\hdashline\n & 5 & 1 & 1\/15 & 0.333 & 1.666 \\\\\n \\hdashline\n & 5.5 & 1 & 1\/15 & 0.366 & 2.0166\\\\\n \\hdashline\n \n Total & & 15 & 1 & 3.5 & 13.415\\\\ \\hline\n\\end{array}"
4.
"E(\\bar{X})=\\sum\\bar{X}f(\\bar{X})=3.5"
The mean of the sampling distribution of the sample means is equal to the mean of the population.
"E(\\bar{X})=\\mu_{\\bar{X}}=3.5=\\mu"
"Var(\\bar{X})=\\sum\\bar{X}^2f(\\bar{X})-(\\sum\\bar{X}f(\\bar{X}))^2"
"={13.415}-(3.5)^2=1.165"
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