We have population values 5,6,9,12,15, population size N=5 and sample size n=2.
Mean of population ( μ ) (\mu) ( μ ) = 5 + 6 + 9 + 12 + 15 5 = 9.4 \dfrac{5+6+9+12+15}{5}=9.4 5 5 + 6 + 9 + 12 + 15 = 9.4
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 1 5 ( 19.36 + 11.56 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=\dfrac{1}{5}(19.36+11.56 σ 2 = n Σ ( x i − x ˉ ) 2 = 5 1 ( 19.36 + 11.56
+ 0.16 + 6.76 + 31.36 ) = 13.84 +0.16+6.76+31.36)=13.84 + 0.16 + 6.76 + 31.36 ) = 13.84
σ = 13.84 ≈ 3.720215 \sigma=\sqrt{13.84}\approx3.720215 σ = 13.84 ≈ 3.720215
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 5 , 6 11 / 2 2 5 , 9 14 / 2 3 5 , 12 17 / 2 4 5 , 15 20 / 2 5 6 , 9 15 / 2 6 6 , 12 18 / 2 7 6 , 15 21 / 2 8 9 , 12 21 / 2 9 9 , 15 24 / 2 10 12 , 15 27 / 2 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 5,6 & 11/2 \\
\hdashline
2 & 5,9 & 14/2 \\
\hdashline
3 & 5,12 & 17/2 \\
\hdashline
4 & 5,15 & 20/2 \\
\hdashline
5 & 6,9 & 15/2 \\
\hdashline
6 & 6,12 & 18/2 \\
\hdashline
7 & 6,15 & 21/2 \\
\hdashline
8 & 9,12 & 21/2 \\
\hdashline
9 & 9,15 & 24/2 \\
\hdashline
10 & 12,15 & 27/2 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 S am pl e 5 , 6 5 , 9 5 , 12 5 , 15 6 , 9 6 , 12 6 , 15 9 , 12 9 , 15 12 , 15 S am pl e m e an ( x ˉ ) 11/2 14/2 17/2 20/2 15/2 18/2 21/2 21/2 24/2 27/2
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 11 / 2 1 / 10 11 / 20 121 / 40 14 / 2 1 / 10 14 / 20 196 / 40 15 / 2 1 / 10 15 / 20 225 / 40 17 / 2 1 / 10 17 / 20 289 / 40 18 / 2 1 / 10 18 / 20 324 / 40 20 / 2 1 / 10 20 / 20 400 / 40 21 / 2 2 / 10 42 / 20 882 / 40 24 / 2 1 / 10 24 / 20 576 / 40 27 / 2 1 / 10 27 / 20 729 / 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
11/2 & 1/10 & 11/20 & 121/40 \\
\hdashline
14/2 & 1/10 & 14/20 & 196/40 \\
\hdashline
15/2 & 1/10 & 15/20 & 225/40 \\
\hdashline
17/2 & 1/10 & 17/20 & 289/40 \\
\hdashline
18/2 & 1/10 & 18/20 & 324/40 \\
\hdashline
20/2 & 1/10 & 20/20 & 400/40 \\
\hdashline
21/2 & 2/10 & 42/20 & 882/40 \\
\hdashline
24/2 & 1/10 & 24/20 & 576/40 \\
\hdashline
27/2 & 1/10 & 27/20 & 729/40 \\
\hdashline
\end{array} X ˉ 11/2 14/2 15/2 17/2 18/2 20/2 21/2 24/2 27/2 f ( X ˉ ) 1/10 1/10 1/10 1/10 1/10 1/10 2/10 1/10 1/10 X ˉ f ( X ˉ ) 11/20 14/20 15/20 17/20 18/20 20/20 42/20 24/20 27/20 X ˉ 2 f ( X ˉ ) 121/40 196/40 225/40 289/40 324/40 400/40 882/40 576/40 729/40
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 188 20 = 9.4 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=\dfrac{188}{20}=9.4=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 20 188 = 9.4 = 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 = 3742 40 − ( 5.4 ) 2 = 5.19 = σ 2 n ( N − n N − 1 ) =\dfrac{3742}{40}-(5.4)^2=5.19= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 40 3742 − ( 5.4 ) 2 = 5.19 = n σ 2 ( N − 1 N − n )
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