We have population values 2,3,4,6, population size N=4 and sample size n=2.
1. Mean of population ( μ ) (\mu) ( μ ) = 2 + 3 + 4 + 6 4 = 3.75 \dfrac{2+3+4+6}{4}=3.75 4 2 + 3 + 4 + 6 = 3.75
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 1 4 ( 3.0625 + 0.5625 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=\dfrac{1}{4}(3.0625+0.5625 σ 2 = n Σ ( x i − x ˉ ) 2 = 4 1 ( 3.0625 + 0.5625
+ 0.0625 + 5.0625 ) = 2.1875 = 35 16 +0.0625+5.0625)=2.1875=\dfrac{35}{16} + 0.0625 + 5.0625 ) = 2.1875 = 16 35
σ = σ 2 = 2.1875 ≈ 1.47902 \sigma=\sqrt{\sigma^2}=\sqrt{2.1875}\approx1.47902 σ = σ 2 = 2.1875 ≈ 1.47902 2. 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 = 4 C 2 = 6. ^{N}C_n=^{4}C_2=6. N C n = 4 C 2 = 6.
n o S a m p l e S a m p l e m e a n ( x ˉ ) 1 2 , 3 5 / 2 2 2 , 4 6 / 2 3 2 , 6 8 / 2 4 3 , 4 7 / 2 5 3 , 6 9 / 2 6 4 , 6 10 / 2 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 2,3 & 5/2 \\
\hdashline
2 & 2,4 & 6/2 \\
\hdashline
3 & 2,6 & 8/2 \\
\hdashline
4 & 3,4 & 7/2 \\
\hdashline
5 & 3,6 & 9/2 \\
\hdashline
6 & 4,6 & 10/2 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 S am pl e 2 , 3 2 , 4 2 , 6 3 , 4 3 , 6 4 , 6 S am pl e m e an ( x ˉ ) 5/2 6/2 8/2 7/2 9/2 10/2
3.
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 5 / 2 1 / 6 5 / 12 25 / 24 6 / 2 1 / 6 6 / 12 36 / 24 7 / 2 1 / 6 7 / 12 49 / 24 8 / 2 1 / 6 8 / 12 64 / 24 9 / 2 1 / 6 9 / 12 81 / 24 10 / 2 1 / 6 10 / 12 100 / 24 \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
5/2 & 1/6 & 5/12 & 25/24 \\
\hdashline
6/2 & 1/6 & 6/12 & 36/24 \\
\hdashline
7/2 & 1/6 & 7/12 & 49/24 \\
\hdashline
8/2 & 1/6 & 8/12 & 64/24 \\
\hdashline
9/2 & 1/6 & 9/12 & 81/24 \\
\hdashline
10/2 & 1/6 & 10/12 & 100/24 \\
\hdashline
\end{array} X ˉ 5/2 6/2 7/2 8/2 9/2 10/2 f ( X ˉ ) 1/6 1/6 1/6 1/6 1/6 1/6 X ˉ f ( X ˉ ) 5/12 6/12 7/12 8/12 9/12 10/12 X ˉ 2 f ( X ˉ ) 25/24 36/24 49/24 64/24 81/24 100/24
4. Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 45 12 = 15 4 = 3.75 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=\dfrac{45}{12}=\dfrac{15}{4}=3.75=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 12 45 = 4 15 = 3.75 = 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 = 355 24 − ( 15 4 ) 2 = 35 48 = σ 2 n ( N − n N − 1 ) =\dfrac{355}{24}-(\dfrac{15}{4})^2=\dfrac{35}{48}= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 24 355 − ( 4 15 ) 2 = 48 35 = n σ 2 ( N − 1 N − n )
σ X ˉ = 35 48 ≈ 0.85391 \sigma_{\bar{X}}=\sqrt{\dfrac{35}{48}}\approx0.85391 σ X ˉ = 48 35 ≈ 0.85391 5.
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