We have population values 2,5,6,8,11, population size N=5 and sample size n=2.
Mean of population ( μ ) (\mu) ( μ ) = 2 + 5 + 6 + 8 + 11 5 = 6.4 \dfrac{2+5+6+8+11}{5}=6.4 5 2 + 5 + 6 + 8 + 11 = 6.4
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 1 5 ( 19.36 + 1.96 + 0.16 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=\dfrac{1}{5}(19.36+1.96+0.16 σ 2 = n Σ ( x i − x ˉ ) 2 = 5 1 ( 19.36 + 1.96 + 0.16
+ 2.56 + 21.16 ) = 9.04 +2.56+21.16)=9.04 + 2.56 + 21.16 ) = 9.04
σ = σ 2 = 9.04 ≈ 3.00666 \sigma=\sqrt{\sigma^2}=\sqrt{9.04}\approx3.00666 σ = σ 2 = 9.04 ≈ 3.00666 A. Select a random sample of size 2 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 2 = 10. ^{N}C_n=^{5}C_2=10. N C n = 5 C 2 = 10.
n o S a m p l e S a m p l e m e a n ( x ˉ ) 1 2 , 5 7 / 2 2 2 , 6 8 / 2 3 2 , 8 10 / 2 4 2 , 11 13 / 2 5 5 , 6 11 / 2 6 5 , 8 13 / 2 7 5 , 11 16 / 2 8 6 , 8 14 / 2 9 6 , 11 17 / 2 10 8 , 11 19 / 2 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 2,5 & 7/2 \\
\hdashline
2 & 2,6 & 8/2 \\
\hdashline
3 & 2,8 & 10/2 \\
\hdashline
4 & 2,11 & 13/2 \\
\hdashline
5 & 5,6 & 11/2 \\
\hdashline
6 & 5,8 & 13/2 \\
\hdashline
7 & 5,11 & 16/2 \\
\hdashline
8 & 6,8 & 14/2 \\
\hdashline
9 & 6,11 & 17/2 \\
\hdashline
10 & 8,11 & 19/2 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 S am pl e 2 , 5 2 , 6 2 , 8 2 , 11 5 , 6 5 , 8 5 , 11 6 , 8 6 , 11 8 , 11 S am pl e m e an ( x ˉ ) 7/2 8/2 10/2 13/2 11/2 13/2 16/2 14/2 17/2 19/2
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 7 / 2 1 / 10 7 / 20 49 / 40 8 / 2 1 / 10 8 / 20 64 / 40 10 / 2 1 / 10 10 / 20 100 / 40 11 / 2 1 / 10 11 / 20 121 / 40 13 / 2 2 / 10 26 / 20 338 / 40 14 / 2 1 / 10 14 / 20 196 / 40 16 / 2 1 / 10 16 / 20 256 / 40 17 / 2 1 / 10 17 / 20 289 / 40 19 / 2 1 / 10 19 / 20 361 / 40 \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
7/2 & 1/10 & 7/20 & 49/40 \\
\hdashline
8/2 & 1/10 & 8/20 & 64/40 \\
\hdashline
10/2 & 1/10 & 10/20 & 100/40 \\
\hdashline
11/2 & 1/10 & 11/20 & 121/40 \\
\hdashline
13/2 & 2/10 & 26/20 & 338/40 \\
\hdashline
14/2 & 1/10 & 14/20 & 196/40 \\
\hdashline
16/2 & 1/10 & 16/20 & 256/40 \\
\hdashline
17/2 & 1/10 & 17/20 & 289/40 \\
\hdashline
19/2 & 1/10 & 19/20 & 361/40 \\
\hdashline
\end{array} X ˉ 7/2 8/2 10/2 11/2 13/2 14/2 16/2 17/2 19/2 f ( X ˉ ) 1/10 1/10 1/10 1/10 2/10 1/10 1/10 1/10 1/10 X ˉ f ( X ˉ ) 7/20 8/20 10/20 11/20 26/20 14/20 16/20 17/20 19/20 X ˉ 2 f ( X ˉ ) 49/40 64/40 100/40 121/40 338/40 196/40 256/40 289/40 361/40
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 128 20 = 6.4 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=\dfrac{128}{20}=6.4=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 20 128 = 6.4 = 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 = 1774 40 − ( 6.4 ) 2 = 3.39 = σ 2 n ( N − n N − 1 ) =\dfrac{1774}{40}-(6.4)^2=3.39= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 40 1774 − ( 6.4 ) 2 = 3.39 = n σ 2 ( N − 1 N − n )
σ X ˉ = 3.39 ≈ 1.8412 \sigma_{\bar{X}}=\sqrt{3.39}\approx1.8412 σ X ˉ = 3.39 ≈ 1.8412
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