We have population values 12, 15, 18, 19, 21, 23, 35, and 40 population size N=8 and sample size n=7.
Mean of population ( μ ) (\mu) ( μ ) =
12 + 15 + 18 + 19 + 21 + 23 + 35 + 40 8 = 22.875 \dfrac{12+15+18+19+21+23+35+40}{8}=22.875 8 12 + 15 + 18 + 19 + 21 + 23 + 35 + 40 = 22.875
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 82.859375 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=82.859375 σ 2 = n Σ ( x i − x ˉ ) 2 = 82.859375
σ = σ 2 = 6 \sigma=\sqrt{\sigma^2}=\sqrt{6} σ = σ 2 = 6 The number of possible samples which can be drawn without replacement is N C n = 8 C 7 = 8. ^{N}C_n=^{8}C_7=8. N C n = 8 C 7 = 8.
n o S a m p l e S a m p l e m e a n ( x ˉ ) 1 12 , 15 , 18 , 19 , 21 , 23 , 35 143 / 7 2 12 , 15 , 18 , 19 , 21 , 23 , 40 148 / 7 3 12 , 18 , 19 , 21 , 23 , 35 , 40 168 / 7 4 12 , 15 , 19 , 21 , 23 , 35 , 40 165 / 7 5 12 , 15 , 18 , 21 , 23 , 35 , 40 164 / 7 6 12 , 15 , 18 , 19 , 23 , 35 , 40 162 / 7 7 12 , 15 , 18 , 19 , 21 , 35 , 40 160 / 7 8 15 , 18 , 19 , 21 , 23 , 35 , 40 171 / 7 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 12, 15, 18, 19, 21, 23, 35 & 143/7 \\
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2 & 12, 15, 18, 19, 21, 23, 40 & 148/7 \\
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3 & 12, 18, 19, 21, 23, 35,40 & 168/7 \\
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4 & 12, 15, 19, 21, 23, 35, 40 & 165/7 \\
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5 & 12, 15, 18, 21, 23, 35, 40 & 164/7 \\
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6 & 12, 15,18, 19, 23, 35,40 & 162/7 \\
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7 & 12,15, 18, 19, 21, 35,40 & 160/7 \\
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8 & 15, 18, 19, 21, 23, 35, 40 & 171/7 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 S am pl e 12 , 15 , 18 , 19 , 21 , 23 , 35 12 , 15 , 18 , 19 , 21 , 23 , 40 12 , 18 , 19 , 21 , 23 , 35 , 40 12 , 15 , 19 , 21 , 23 , 35 , 40 12 , 15 , 18 , 21 , 23 , 35 , 40 12 , 15 , 18 , 19 , 23 , 35 , 40 12 , 15 , 18 , 19 , 21 , 35 , 40 15 , 18 , 19 , 21 , 23 , 35 , 40 S am pl e m e an ( x ˉ ) 143/7 148/7 168/7 165/7 164/7 162/7 160/7 171/7
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 143 / 7 1 / 8 143 / 56 20449 / 392 148 / 7 1 / 8 148 / 56 21904 / 392 168 / 7 1 / 8 168 / 56 28224 / 392 165 / 7 1 / 8 165 / 56 27225 / 392 164 / 7 1 / 8 164 / 56 26896 / 392 162 / 7 1 / 8 162 / 56 26244 / 392 160 / 7 1 / 8 160 / 56 25600 / 392 171 / 7 1 / 8 171 / 56 29241 / 392 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
\bar{X} & f(\bar{X}) &\bar{X} f(\bar{X}) &\bar{X}^2 f(\bar{X})\\ \hline
143/7 & 1/8 & 143/56 & 20449/392\\
\hdashline
148/7 & 1/8 & 148/56 & 21904/392\\
\hdashline
168/7 & 1/8 & 168/56 & 28224/392\\
\hdashline
165/7 & 1/8 & 165/56 & 27225/392 \\
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164/7 & 1/8 & 164/56 & 26896/392\\
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162/7 & 1/8 & 162/56 & 26244/392 \\
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160/7 & 1/8 & 160/56 & 25600/392 \\
\hdashline
171/7 & 1/8 & 171/56 & 29241/392 \\
\hdashline
\end{array} X ˉ 143/7 148/7 168/7 165/7 164/7 162/7 160/7 171/7 f ( X ˉ ) 1/8 1/8 1/8 1/8 1/8 1/8 1/8 1/8 X ˉ f ( X ˉ ) 143/56 148/56 168/56 165/56 164/56 162/56 160/56 171/56 X ˉ 2 f ( X ˉ ) 20449/392 21904/392 28224/392 27225/392 26896/392 26244/392 25600/392 29241/392
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 22.875 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=22.875=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 22.875 = 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 = 205783 392 − ( 183 8 ) 2 = 5303 3136 = σ 2 n ( N − n N − 1 ) =\dfrac{205783}{392}-(\dfrac{183}{8})^2=\dfrac{5303}{3136}= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 392 205783 − ( 8 183 ) 2 = 3136 5303 = n σ 2 ( N − 1 N − n )
σ X ˉ = 5303 3136 ≈ 1.3004 \sigma_{\bar{X}}=\sqrt{\dfrac{5303}{3136}}\approx1.3004 σ X ˉ = 3136 5303 ≈ 1.3004
S E = σ X ˉ n = 5303 3136 7 ≈ 0.4915 SE=\dfrac{\sigma_{\bar{X}}}{\sqrt{n}}=\dfrac{\sqrt{\dfrac{5303}{3136}}}{\sqrt{7}}\approx0.4915 SE = n σ X ˉ = 7 3136 5303 ≈ 0.4915
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