In total there are 5 2 = 25 5^2=25 5 2 = 25 samples or pairs which are possible
S a m p l e M e a n S a m p l e M e a n ( 2 , 2 ) 2 ( 2 , 4 ) 3 ( 2 , 6 ) 4 ( 2 , 8 ) 5 ( 2 , 10 ) 6 ( 4 , 2 ) 3 ( 4 , 4 ) 4 ( 4 , 6 ) 5 ( 4 , 8 ) 6 ( 4 , 10 ) 7 ( 6 , 2 ) 4 ( 6 , 4 ) 5 ( 6 , 6 ) 6 ( 6 , 8 ) 7 ( 6 , 10 ) 8 ( 8 , 2 ) 5 ( 8 , 4 ) 6 ( 8 , 6 ) 7 ( 8 , 8 ) 8 ( 8 , 10 ) 9 ( 10 , 2 ) 6 ( 10 , 4 ) 7 ( 10 , 6 ) 8 ( 10 , 8 ) 9 ( 10 , 10 ) 10 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:}
Sample & Mean & & Sample & Mean & & \\ \hline
(2,2) & 2 & & (2,4) & 3 \\
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(2,6) & 4 & & (2,8) & 5 \\
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(2,10) & 6 & & (4,2) & 3 \\
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(4,4) & 4 & & (4,6) & 5 \\
\hdashline
(4,8) & 6 & & (4,10) & 7 \\
\hdashline
(6,2) & 4 & & (6,4) & 5 \\
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(6,6) & 6 & & (6,8) & 7 \\
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(6,10) & 8 & & (8,2) & 5 \\
\hdashline
(8,4) & 6 & & (8,6) & 7 \\
\hdashline
(8,8) & 8 & & (8,10) & 9 \\
\hdashline
(10,2) & 6 & & (10,4) & 7 \\
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(10,6) & 8 & & (10,8) & 9 \\
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(10,10) & 10 & & \\
\end{array} S am pl e ( 2 , 2 ) ( 2 , 6 ) ( 2 , 10 ) ( 4 , 4 ) ( 4 , 8 ) ( 6 , 2 ) ( 6 , 6 ) ( 6 , 10 ) ( 8 , 4 ) ( 8 , 8 ) ( 10 , 2 ) ( 10 , 6 ) ( 10 , 10 ) M e an 2 4 6 4 6 4 6 8 6 8 6 8 10 S am pl e ( 2 , 4 ) ( 2 , 8 ) ( 4 , 2 ) ( 4 , 6 ) ( 4 , 10 ) ( 6 , 4 ) ( 6 , 8 ) ( 8 , 2 ) ( 8 , 6 ) ( 8 , 10 ) ( 10 , 4 ) ( 10 , 8 ) M e an 3 5 3 5 7 5 7 5 7 9 7 9 The table shows that there are nine possible values of the sample mean X ˉ . \bar{X}. X ˉ .
x ˉ p ( x ˉ ) 2 1 / 25 3 2 / 25 4 3 / 25 5 4 / 25 6 5 / 25 7 4 / 25 8 3 / 25 9 2 / 25 10 1 / 25 \def\arraystretch{1.5}
\begin{array}{c:c}
\bar{x} & p(\bar{x}) \\ \hline
2 & 1/25 \\
3 & 2/25 \\
4 & 3/25 \\
5 & 4/25 \\
6 & 5/25 \\
7 & 4/25 \\
8 & 3/25 \\
9 & 2/25 \\
10 & 1/25 \\
\end{array} x ˉ 2 3 4 5 6 7 8 9 10 p ( x ˉ ) 1/25 2/25 3/25 4/25 5/25 4/25 3/25 2/25 1/25
μ X ˉ = ∑ i x ˉ i p ( x ˉ i ) = 2 ( 1 25 ) + 3 ( 2 25 ) + 4 ( 3 25 ) \mu_{\bar{X}}=\sum_i\bar{x}_ip(\bar{x}_i)=2(\dfrac{1}{25})+3(\dfrac{2}{25})+4(\dfrac{3}{25}) μ X ˉ = i ∑ x ˉ i p ( x ˉ i ) = 2 ( 25 1 ) + 3 ( 25 2 ) + 4 ( 25 3 )
+ 5 ( 4 25 ) + 6 ( 5 25 ) + 7 ( 4 25 ) + 8 ( 3 25 ) + 9 ( 2 25 ) +5(\dfrac{4}{25})+6(\dfrac{5}{25})+7(\dfrac{4}{25})+8(\dfrac{3}{25})+9(\dfrac{2}{25}) + 5 ( 25 4 ) + 6 ( 25 5 ) + 7 ( 25 4 ) + 8 ( 25 3 ) + 9 ( 25 2 )
+ 10 ( 1 25 ) = 6 +10(\dfrac{1}{25})=6 + 10 ( 25 1 ) = 6
∑ i x ˉ i 2 p ( x ˉ i ) = ( 2 ) 2 ( 1 25 ) + ( 3 ) 2 ( 2 25 ) + ( 4 ) 2 ( 3 25 ) \sum_i\bar{x}_i^2p(\bar{x}_i)=(2)^2(\dfrac{1}{25})+(3)^2(\dfrac{2}{25})+(4)^2(\dfrac{3}{25}) i ∑ x ˉ i 2 p ( x ˉ i ) = ( 2 ) 2 ( 25 1 ) + ( 3 ) 2 ( 25 2 ) + ( 4 ) 2 ( 25 3 )
+ ( 5 ) 2 ( 4 25 ) + ( 6 ) 2 ( 5 25 ) + ( 7 ) 2 ( 4 25 ) + ( 8 ) 2 ( 3 25 ) +(5)^2(\dfrac{4}{25})+(6)^2(\dfrac{5}{25})+(7)^2(\dfrac{4}{25})+(8)^2(\dfrac{3}{25}) + ( 5 ) 2 ( 25 4 ) + ( 6 ) 2 ( 25 5 ) + ( 7 ) 2 ( 25 4 ) + ( 8 ) 2 ( 25 3 )
+ ( 9 ) 2 ( 2 25 ) + ( 10 ) 2 ( 1 25 ) = 40 +(9)^2(\dfrac{2}{25})+(10)^2(\dfrac{1}{25})=40 + ( 9 ) 2 ( 25 2 ) + ( 10 ) 2 ( 25 1 ) = 40
V a r ( X ˉ ) = σ X ˉ 2 = ∑ i x ˉ i 2 p ( x ˉ i ) − ( ∑ i x ˉ i p ( x ˉ i ) ) 2 Var(\bar{X})=\sigma_{\bar{X}}^2=\sum_i\bar{x}_i^2p(\bar{x}_i)-(\sum_i\bar{x}_ip(\bar{x}_i))^2 Va r ( X ˉ ) = σ X ˉ 2 = i ∑ x ˉ i 2 p ( x ˉ i ) − ( i ∑ x ˉ i p ( x ˉ i ) ) 2
= 40 − ( 6 ) 2 = 4 =40-(6)^2=4 = 40 − ( 6 ) 2 = 4
σ X ˉ = σ X ˉ 2 = 100 = 10 \sigma_{\bar{X}}=\sqrt{\sigma_{\bar{X}}^2}=\sqrt{100}=10 σ X ˉ = σ X ˉ 2 = 100 = 10
μ = 2 + 4 + 6 + 8 + 10 5 = 6 \mu=\dfrac{2+4+6+8+10}{5}=6 μ = 5 2 + 4 + 6 + 8 + 10 = 6 In sampling with replacement the mean of all sample means equals the mean of the population:
μ X ˉ = 6 = μ \mu_{\bar{X}}=6=\mu μ X ˉ = 6 = μ
σ 2 = 1 5 ( ( 2 − 6 ) 2 + ( 4 − 6 ) 2 + ( 6 − 6 ) 2 \sigma^2=\dfrac{1}{5}((2-6)^2+(4-6)^2+(6-6)^2 σ 2 = 5 1 (( 2 − 6 ) 2 + ( 4 − 6 ) 2 + ( 6 − 6 ) 2
+ ( 8 − 6 ) 2 + ( 10 − 6 ) 2 ) = 8 +(8-6)^2+(10-6)^2)=8 + ( 8 − 6 ) 2 + ( 10 − 6 ) 2 ) = 8 When sampling with replacement the variation of all sample means equals the variation of the population divided by the sample size
σ X ˉ 2 = σ 2 n \sigma_{\bar{X}}^2=\dfrac{\sigma^2}{n} σ X ˉ 2 = n σ 2
4 = 8 2 4=\dfrac{8}{2} 4 = 2 8
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