We have population values 1,2,3,4,5, population size N=5 and sample size n=3.
Mean of population ( μ ) (\mu) ( μ ) = 1 + 2 + 3 + 4 + 5 5 = 3 \dfrac{1+2+3+4+5}{5}=3 5 1 + 2 + 3 + 4 + 5 = 3
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 N = 4 + 1 + 0 + 1 + 4 5 = 2 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{N}=\dfrac{4+1+0+1+4}{5}=2 σ 2 = N Σ ( x i − x ˉ ) 2 = 5 4 + 1 + 0 + 1 + 4 = 2 σ = σ 2 = 2 ≈ 1.4142 \sigma=\sqrt{\sigma^2}=\sqrt{2}\approx1.4142 σ = σ 2 = 2 ≈ 1.4142
The number of possible samples which can be drawn without replacement is N C n = 5 C 3 = 10. ^{N}C_n=^{5}C_3=10. N C n = 5 C 3 = 10.
n o S a m p l e S a m p l e m e a n ( x ˉ ) 1 1 , 2 , 3 6 / 3 2 1 , 2 , 4 7 / 3 3 1 , 2 , 5 8 / 3 4 1 , 3 , 4 8 / 3 5 1 , 3 , 5 9 / 3 6 1 , 4 , 5 10 / 3 7 2 , 3 , 4 9 / 3 8 2 , 3 , 5 10 / 3 9 2 , 4 , 5 11 / 3 10 3 , 4 , 5 12 / 3 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 1,2,3 & 6/3 \\
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2 & 1,2,4 & 7/3 \\
\hdashline
3 & 1,2,5 & 8/3\\
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4 & 1,3,4 & 8/3 \\
\hdashline
5 & 1,3,5 & 9/3 \\
\hdashline
6 & 1,4,5 & 10/3 \\
\hdashline
7 & 2,3,4 & 9/3 \\
\hdashline
8 & 2,3,5 & 10/3 \\
\hdashline
9 & 2,4,5 & 11/3 \\
\hdashline
10 & 3,4,5 & 12/3 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 S am pl e 1 , 2 , 3 1 , 2 , 4 1 , 2 , 5 1 , 3 , 4 1 , 3 , 5 1 , 4 , 5 2 , 3 , 4 2 , 3 , 5 2 , 4 , 5 3 , 4 , 5 S am pl e m e an ( x ˉ ) 6/3 7/3 8/3 8/3 9/3 10/3 9/3 10/3 11/3 12/3
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 6 / 3 1 / 10 6 / 30 36 / 90 7 / 3 1 / 10 7 / 30 49 / 90 8 / 3 2 / 10 16 / 30 128 / 90 9 / 3 2 / 10 18 / 30 162 / 90 10 / 3 2 / 10 20 / 30 200 / 90 11 / 3 1 / 10 11 / 30 121 / 90 12 / 3 1 / 10 12 / 30 144 / 90 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
\bar{X} & f(\bar{X}) &\bar{X} f(\bar{X})& \bar{X}^2f(\bar{X})
\\ \hline
6/3 & 1/10 & 6/30 & 36/90 \\
\hdashline
7/3 & 1/10 & 7/30 & 49/90 \\
\hdashline
8/3 & 2/10 & 16/30 & 128/90 \\
\hdashline
9/3 & 2/10 & 18/30 & 162/90 \\
\hdashline
10/3 & 2/10 & 20/30 & 200/90 \\
\hdashline
11/3 & 1/10 & 11/30 & 121/90 \\
\hdashline
12/3 & 1/10 & 12/30 & 144/90 \\
\hdashline
\end{array} X ˉ 6/3 7/3 8/3 9/3 10/3 11/3 12/3 f ( X ˉ ) 1/10 1/10 2/10 2/10 2/10 1/10 1/10 X ˉ f ( X ˉ ) 6/30 7/30 16/30 18/30 20/30 11/30 12/30 X ˉ 2 f ( X ˉ ) 36/90 49/90 128/90 162/90 200/90 121/90 144/90
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 3 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=3=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 3 = μ
The variance of sampling distribution
V a r ( X ˉ ) = σ X ˉ 2 = ∑ X ˉ i 2 f ( X ˉ i ) − [ ∑ X ˉ i f ( X ˉ i ) ] 2 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 Va r ( X ˉ ) = σ X ˉ 2 = ∑ X ˉ i 2 f ( X ˉ i ) − [ ∑ X ˉ i f ( X ˉ i ) ] 2 = 840 90 − ( 3 ) 2 = 1 3 = σ 2 n ( N − n N − 1 ) =\dfrac{840}{90}-(3)^2=\dfrac{1}{3}= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 90 840 − ( 3 ) 2 = 3 1 = n σ 2 ( N − 1 N − n )
σ X ˉ = 1 3 ≈ 0.57735 \sigma_{\bar{X}}=\sqrt{\dfrac{1}{3}}\approx0.57735 σ X ˉ = 3 1 ≈ 0.57735
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