1. Mean
"\\mu=\\dfrac{1+3+5+7+9}{5}=5"Variance
"\\sigma^2=\\dfrac{1}{5}\\big((2-3.4)^2+(6-2.4)^2+(8-3.4)^2""(0-3.4)^2+(1-3.4)^2\\big)=9.44"
Standard deviation
"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{9.44}\\approx3.0725"
2. We have population values "2,6,8,0,1" population size "N=5" and sample size "n=2." Thus, the number of possible samples which can be drawn without replacement is
"\\dbinom{N}{n}=\\dbinom{5}{2}=10""\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c}\n Sample & Sample & Sample \\ mean \\\\\n No. & values & (\\bar{X}) \\\\ \\hline\n 1 & 2,6 & 4 \\\\\n \\hdashline\n 2 & 2,8 & 5 \\\\\n \\hdashline\n 3 & 2,0 & 1 \\\\\n \\hdashline\n 4 & 2,1 & 1.5 \\\\\n \\hdashline\n 5 & 6,8 & 7 \\\\\n \\hdashline\n 6 & 6,0 & 3 \\\\\n \\hdashline\n 7 & 6,1 & 3.5 \\\\\n \\hdashline\n 8 & 8,0 & 4 \\\\\n \\hdashline\n 9 & 8,1 & 4.5 \\\\\n \\hdashline\n 10 & 0,1 & 0.5 \\\\ \\hline\n\\end{array}"3. The sampling distribution of the sample means.
"\\def\\arraystretch{1.5}\n \\begin{array}{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\/2 & 1& 1\/10 & 1\/20 & 1\/40 \\\\\n \\hdashline\n 1 & 1 & 1\/10 & 2\/20 & 4\/40 \\\\\n \\hdashline\n 3\/2 & 1 & 1\/10 & 3\/20 & 9\/40 \\\\\n \\hdashline\n 3 & 1 & 1\/10 & 6\/20 & 36\/40 \\\\\n \\hdashline\n 7\/2 & 1 & 1\/10 & 7\/20 & 49\/40 \\\\\n \\hdashline\n 4 & 2 & 2\/10 & 16\/20 & 128\/40 \\\\\n \\hdashline\n 9\/2 & 1& 1\/10 & 9\/20 & 81\/40 \\\\\n \\hdashline\n 5 & 1& 1\/10 & 10\/20 & 100\/40 \\\\\n \\hdashline\n 7 & 1 & 1\/10 & 14\/20 & 196\/40 \\\\\n \\hdashline\n Total & 10 & 1 & 68\/20 & 604\/40 \\\\ \\hline\n\\end{array}"4.
"E(\\bar{X})=\\sum\\bar{X}f(\\bar{X})=\\dfrac{68}{20}=3.4"The mean of the sampling distribution of the sample means is equal to the
the mean of the population.
"E(\\bar{X})=3.4=\\mu"5.
"Var(\\bar{X})=\\sum\\bar{X}^2f(\\bar{X})-(\\sum\\bar{X}f(\\bar{X}))^2""=\\dfrac{604}{40}-(\\dfrac{68}{20})^2=\\dfrac{1416}{400}=\\dfrac{354}{100}=3.54""\\sqrt{Var(\\bar{X})}=\\sqrt{\\dfrac{354}{100}}\\approx1.8815"Verification:
"Var(\\bar{X})=\\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})=\\dfrac{9.44}{2}(\\dfrac{5-2}{5-1})""=3.54, True"
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