We have population values 6,9,12,15,21, population size N=5 and sample size n=3.
Mean of population ( μ ) (\mu) ( μ ) = 6 + 9 + 12 + 15 + 21 5 = 12.6 \dfrac{6+9+12+15+21}{5}=12.6 5 6 + 9 + 12 + 15 + 21 = 12.6
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 1 5 ( 43.56 + 12.96 + 0.36 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=\dfrac{1}{5}(43.56+12.96+0.36 σ 2 = n Σ ( x i − x ˉ ) 2 = 5 1 ( 43.56 + 12.96 + 0.36
+ 5.76 + 70.56 ) = 26.64 +5.76+70.56)=26.64 + 5.76 + 70.56 ) = 26.64
σ = σ 2 = 26.64 ≈ 5.1614 \sigma=\sqrt{\sigma^2}=\sqrt{26.64}\approx5.1614 σ = σ 2 = 26.64 ≈ 5.1614 A. Select a random sample of size 3 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 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 6 , 9 , 12 9 2 6 , 9 , 15 10 3 6 , 9 , 21 12 4 6 , 12 , 15 11 5 6 , 12 , 21 13 6 6 , 15 , 21 14 7 9 , 12 , 15 12 8 9 , 12 , 21 14 9 9 , 15 , 21 15 10 12 , 15 , 21 16 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 6,9,12 & 9 \\
\hdashline
2 & 6,9,15 & 10 \\
\hdashline
3 & 6,9,21 & 12 \\
\hdashline
4 & 6,12,15 & 11 \\
\hdashline
5 & 6,12,21 & 13 \\
\hdashline
6 & 6,15,21 & 14 \\
\hdashline
7 & 9,12,15 & 12 \\
\hdashline
8 & 9,12,21 & 14 \\
\hdashline
9 & 9,15,21 & 15 \\
\hdashline
10 & 12,15,21 & 16 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 S am pl e 6 , 9 , 12 6 , 9 , 15 6 , 9 , 21 6 , 12 , 15 6 , 12 , 21 6 , 15 , 21 9 , 12 , 15 9 , 12 , 21 9 , 15 , 21 12 , 15 , 21 S am pl e m e an ( x ˉ ) 9 10 12 11 13 14 12 14 15 16
B.
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 9 1 / 10 9 / 10 81 / 10 10 1 / 10 10 / 10 100 / 10 11 1 / 10 11 / 10 121 / 10 12 2 / 10 24 / 10 288 / 10 13 1 / 10 13 / 10 169 / 10 14 2 / 10 28 / 10 392 / 10 15 1 / 10 15 / 10 225 / 10 16 1 / 10 16 / 10 256 / 10 \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
9 & 1/10 & 9/10 & 81/10 \\
\hdashline
10 & 1/10 & 10/10 & 100/10 \\
\hdashline
11 & 1/10 & 11/10 & 121/10 \\
\hdashline
12 & 2/10 & 24/10 & 288/10 \\
\hdashline
13 & 1/10 & 13/10 & 169/10 \\
\hdashline
14 & 2/10 & 28/10 & 392/10 \\
\hdashline
15 & 1/10 & 15/10 & 225/10 \\
\hdashline
16 & 1/10 & 16/10 & 256/10 \\
\hdashline
\end{array} X ˉ 9 10 11 12 13 14 15 16 f ( X ˉ ) 1/10 1/10 1/10 2/10 1/10 2/10 1/10 1/10 X ˉ f ( X ˉ ) 9/10 10/10 11/10 24/10 13/10 28/10 15/10 16/10 X ˉ 2 f ( X ˉ ) 81/10 100/10 121/10 288/10 169/10 392/10 225/10 256/10
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 126 10 = 12.6 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=\dfrac{126}{10}=12.6=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 10 126 = 12.6 = 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 = 1632 10 − ( 12.6 ) 2 = 4.44 = σ 2 n ( N − n N − 1 ) =\dfrac{1632}{10}-(12.6)^2=4.44= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 10 1632 − ( 12.6 ) 2 = 4.44 = n σ 2 ( N − 1 N − n )
σ X ˉ = 4.44 ≈ 2.1071 \sigma_{\bar{X}}=\sqrt{4.44}\approx2.1071 σ X ˉ = 4.44 ≈ 2.1071
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