We have population values 3,5,7,9,  population size N=4 and sample size n=2.
Mean of population ( μ ) (\mu) ( μ ) 
3 + 5 + 7 + 9 4 = 6 \dfrac{3+5+7+9}{4}=6 4 3 + 5 + 7 + 9  = 6 
Variance of population 
σ 2 = Σ ( x i − x ˉ ) 2 n \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n} σ 2 = n Σ ( x i  − x ˉ ) 2  
  
= 9 + 1 + 1 + 9 4 = 5 =\dfrac{9+1+1+9}{4}=5 = 4 9 + 1 + 1 + 9  = 5  
σ = σ 2 = 5 ≈ 2.24 \sigma=\sqrt{\sigma^2}=\sqrt{5}\approx2.24 σ = σ 2  = 5  ≈ 2.24  The number of possible samples which can be drawn without replacement is 2 4 = 16. 2^4=16. 2 4 = 16. 
n o S a m p l e S a m p l e m e a n   ( x ˉ ) 1 3 , 3 3 2 3 , 5 4 3 3 , 7 5 4 3 , 9 6 5 5 , 3 4 6 5 , 5 5 7 5 , 7 6 8 5 , 9 7 9 7 , 3 5 10 7 , 5 6 11 7 , 7 7 12 7 , 9 8 13 9 , 3 6 14 9 , 5 7 15 9 , 7 8 16 9 , 9 9 \def\arraystretch{1.5}
   \begin{array}{c:c:c:c:c}
   no  & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
   1 & 3,3 & 3 \\
   \hdashline
  2 & 3,5  & 4 \\
   \hdashline
   3 & 3,7 & 5 \\
   \hdashline
   4 & 3,9  & 6 \\
   \hdashline
   5 & 5,3 & 4 \\
   \hdashline
   6 & 5,5 & 5 \\
   \hdashline
   7 & 5,7 & 6 \\
   \hdashline
   8 & 5,9 & 7 \\
   \hdashline
   9 & 7,3 & 5 \\
   \hdashline
   10 & 7,5 & 6 \\
   \hdashline
   11 & 7,7 & 7 \\
   \hdashline
   12 & 7,9 & 8 \\
   \hdashline
   13 & 9,3 & 6 \\
   \hdashline
   14 & 9,5 & 7 \\
   \hdashline
   15 & 9,7 & 8 \\
   \hdashline
   16 & 9,9 & 9 \\
   \hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  S am pl e 3 , 3 3 , 5 3 , 7 3 , 9 5 , 3 5 , 5 5 , 7 5 , 9 7 , 3 7 , 5 7 , 7 7 , 9 9 , 3 9 , 5 9 , 7 9 , 9  S am pl e m e an   ( x ˉ ) 3 4 5 6 4 5 6 7 5 6 7 8 6 7 8 9   
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 3 1 / 16 3 / 16 9 / 16 4 2 / 16 8 / 16 32 / 16 5 3 / 16 15 / 16 75 / 16 6 4 / 16 24 / 16 144 / 16 7 3 / 16 21 / 16 147 / 16 8 2 / 16 16 / 16 128 / 16 9 1 / 16 9 / 16 81 / 16 \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
  3 & 1/16 & 3/16 & 9/16\\
   \hdashline
  4 & 2/16 & 8/16 & 32/16\\
   \hdashline
  5 & 3/16 & 15/16 & 75/16\\
   \hdashline
  6 & 4/16 & 24/16 & 144/16\\
   \hdashline
  7 & 3/16 & 21/16 & 147/16\\
   \hdashline
   8 & 2/16 & 16/16 & 128/16\\
   \hdashline
   9 & 1/16 & 9/16 & 81/16\\
   \hdashline
\end{array} X ˉ 3 4 5 6 7 8 9  f ( X ˉ ) 1/16 2/16 3/16 4/16 3/16 2/16 1/16  X ˉ f ( X ˉ ) 3/16 8/16 15/16 24/16 21/16 16/16 9/16  X ˉ 2 f ( X ˉ ) 9/16 32/16 75/16 144/16 147/16 128/16 81/16   
Mean of sampling distribution 
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 6 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=6=\mu μ X ˉ  = E ( X ˉ ) = ∑ X ˉ i  f ( X ˉ i  ) = 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 = 616 16 − ( 6 ) 2 = 5 2 = 2.5 = σ 2 n =\dfrac{616}{16}-(6)^2=\dfrac{5}{2}=2.5= \dfrac{\sigma^2}{n} = 16 616  − ( 6 ) 2 = 2 5  = 2.5 = n σ 2   
σ X ˉ = 5 2 ≈ 1.58 \sigma_{\bar{X}}=\sqrt{\dfrac{5}{2}}\approx1.58 σ X ˉ  = 2 5   ≈ 1.58  
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