We have population values 1 , 3 , 5 , 7 1,3,5,7 1 , 3 , 5 , 7 population size N = 4 N=4 N = 4 and sample size n = 2. n=2. n = 2.
Thus, the number of possible samples which can be drawn without replacement is ( 4 2 ) = 6. \dbinom{4}{2}=6. ( 2 4 ) = 6.
S a m p l e v a l u e s S a m p l e m e a n ( X ˉ ) 1 , 3 2 1 , 5 3 1 , 7 4 3 , 5 4 3.7 5 5 , 7 6 \def\arraystretch{1.5}
\begin{array}{c:c}
Sample\ values & Sample\ mean(\bar{X}) \\ \hline
1,3 & 2\\
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1,5 & 3\\
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1,7 & 4\\
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3,5 & 4\\
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3.7 & 5\\
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5,7 & 6\\
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\end{array} S am pl e v a l u es 1 , 3 1 , 5 1 , 7 3 , 5 3.7 5 , 7 S am pl e m e an ( X ˉ ) 2 3 4 4 5 6
The sampling distribution of the sample mean X ˉ \bar{X} X ˉ is
X ˉ f f ( X ˉ ) X f ( X ˉ ) X 2 f ( X ˉ ) 2 1 1 / 6 2 / 6 4 / 6 3 1 1 / 6 3 / 6 9 / 6 4 2 2 / 6 8 / 6 32 / 6 5 1 1 / 6 5 / 6 25 / 6 6 1 1 / 16 6 / 6 36 / 6 S u m = 6 1 4 53 / 3 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c:c}
& \bar{X} & f & f(\bar{X}) & Xf(\bar{X})& X^2f(\bar{X}) \\ \hline
& 2 & 1 & 1/6 & 2/6 & 4/6\\
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& 3 & 1 & 1/6 & 3/6 & 9/6 \\
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& 4 & 2 & 2/6 & 8/6 & 32/6\\
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& 5 & 1 & 1/6 & 5/6 & 25/6 \\
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& 6 & 1 & 1/16 & 6/6 & 36/6 \\
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Sum= & & 6 & 1 & 4 & 53/3\\
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\end{array} S u m = X ˉ 2 3 4 5 6 f 1 1 2 1 1 6 f ( X ˉ ) 1/6 1/6 2/6 1/6 1/16 1 X f ( X ˉ ) 2/6 3/6 8/6 5/6 6/6 4 X 2 f ( X ˉ ) 4/6 9/6 32/6 25/6 36/6 53/3
μ = 1 + 3 + 5 + 7 4 = 4 \mu=\dfrac{1+3+5+7}{4}=4 μ = 4 1 + 3 + 5 + 7 = 4
σ 2 = 1 4 ( ( 1 − 4 ) 2 + ( 3 − 4 ) 2 + ( 5 − 4 ) 2 + ( 7 − 4 ) 2 ) \sigma^2=\dfrac{1}{4}((1-4)^2+(3-4)^2+(5-4)^2+(7-4)^2) σ 2 = 4 1 (( 1 − 4 ) 2 + ( 3 − 4 ) 2 + ( 5 − 4 ) 2 + ( 7 − 4 ) 2 )
= 5 =5 = 5
Check
The mean of the sample means is
μ X ˉ = E ( X ˉ ) = 4 = μ \mu_{\bar{X}}=E(\bar{X})=4=\mu μ X ˉ = E ( X ˉ ) = 4 = μ
V a r ( X ˉ ) = σ X ˉ 2 = E ( X ˉ 2 ) − ( E ( X ˉ ) ) 2 Var(\bar{X})=\sigma^2_{\bar{X}}=E(\bar{X}^2)-(E(\bar{X}))^2 Va r ( X ˉ ) = σ X ˉ 2 = E ( X ˉ 2 ) − ( E ( X ˉ ) ) 2
= 53 3 − ( 4 ) 2 = 5 3 =\dfrac{53}{3}-(4)^2=\dfrac{5}{3} = 3 53 − ( 4 ) 2 = 3 5 The standard deviation of the sample means is
σ X ˉ = σ X ˉ 2 = 5 / 3 \sigma_{\bar{X}}=\sqrt{\sigma^2_{\bar{X}}}=\sqrt{5/3} σ X ˉ = σ X ˉ 2 = 5/3
σ 2 n ⋅ N − n N − 1 = 5 3 ⋅ 4 − 2 4 − 1 = σ X ˉ 2 \dfrac{\sigma^2}{n}\cdot\dfrac{N-n}{N-1}=\dfrac{5}{3}\cdot \dfrac{4-2}{4-1}=\sigma^2_{\bar{X}} n σ 2 ⋅ N − 1 N − n = 3 5 ⋅ 4 − 1 4 − 2 = σ X ˉ 2
μ X ˉ = μ \mu_{\bar{X}}=\mu μ X ˉ = μ
σ X ˉ = σ n N − n N − 1 \sigma_{\bar{X}}=\dfrac{\sigma}{\sqrt{n}}\sqrt{\dfrac{N-n}{N-1}} σ X ˉ = n σ N − 1 N − n
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