We have population values 1,3,4,5 and 7 population size N=5 and sample size n=2.
(i) The number of possible samples which can be drawn without replacement is N C n = 5 C 2 = 10. ^{N}C_n=^{5}C_2=10. N C n = 5 C 2 = 10.
n o S a m p l e S a m p l e m e a n ( x ˉ ) 1 1 , 3 4 / 2 2 1 , 4 5 / 2 3 1 , 5 6 / 2 4 1 , 7 8 / 2 5 3 , 4 7 / 2 6 3 , 5 8 / 2 7 3 , 7 10 / 2 8 4 , 5 9 / 2 9 4 , 7 11 / 2 10 5 , 7 12 / 2 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
no & Sample & Sample \\
& & mean\ (\bar{x})
\\ \hline
1 & 1,3 & 4/2 \\
\hdashline
2 & 1,4 & 5/2 \\
\hdashline
3 & 1,5 & 6/2 \\
\hdashline
4 & 1,7 & 8/2 \\
\hdashline
5 & 3,4 & 7/2 \\
\hdashline
6 & 3,5 & 8/2 \\
\hdashline
7 & 3,7 & 10/2 \\
\hdashline
8 & 4,5 & 9/2 \\
\hdashline
9 & 4,7 & 11/2 \\
\hdashline
10 & 5,7 & 12/2 \\
\hdashline
\end{array} n o 1 2 3 4 5 6 7 8 9 10 S am pl e 1 , 3 1 , 4 1 , 5 1 , 7 3 , 4 3 , 5 3 , 7 4 , 5 4 , 7 5 , 7 S am pl e m e an ( x ˉ ) 4/2 5/2 6/2 8/2 7/2 8/2 10/2 9/2 11/2 12/2
(ii) Mean of population ( μ ) (\mu) ( μ ) =
1 + 3 + 4 + 5 + 7 5 = 4 \dfrac{1+3+4+5+7}{5}=4 5 1 + 3 + 4 + 5 + 7 = 4
Variance of population
σ 2 = Σ ( x i − x ˉ ) 2 n = 9 + 1 + 0 + 1 + 9 5 = 4 \sigma^2=\dfrac{\Sigma(x_i-\bar{x})^2}{n}=\dfrac{9+1+0+1+9}{5}=4 σ 2 = n Σ ( x i − x ˉ ) 2 = 5 9 + 1 + 0 + 1 + 9 = 4
σ = σ 2 = 4 = 2 \sigma=\sqrt{\sigma^2}=\sqrt{4}=2 σ = σ 2 = 4 = 2
(iii)
X ˉ f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 4 / 2 1 / 10 4 / 20 16 / 40 5 / 2 1 / 10 5 / 20 25 / 40 6 / 2 1 / 10 6 / 20 36 / 40 7 / 2 1 / 10 7 / 20 49 / 40 8 / 2 2 / 10 16 / 20 128 / 40 9 / 2 1 / 10 9 / 20 81 / 40 10 / 2 1 / 10 10 / 20 100 / 40 11 / 2 1 / 10 11 / 20 121 / 40 12 / 2 1 / 10 12 / 20 144 / 40 \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
4/2 & 1/10 & 4/20 & 16/40\\
\hdashline
5/2 & 1/10 & 5/20 & 25/40\\
\hdashline
6/2 & 1/10 & 6/20 & 36/40\\
\hdashline
7/2 & 1/10 & 7/20 & 49/40\\
\hdashline
8/2 & 2/10 & 16/20 & 128/40\\
\hdashline
9/2 & 1/10 & 9/20 & 81/40\\
\hdashline
10/2 & 1/10 & 10/20 & 100/40\\
\hdashline
11/2 & 1/10 & 11/20 & 121/40\\
\hdashline
12/2 & 1/10 & 12/20 & 144/40\\
\hdashline
\end{array} X ˉ 4/2 5/2 6/2 7/2 8/2 9/2 10/2 11/2 12/2 f ( X ˉ ) 1/10 1/10 1/10 1/10 2/10 1/10 1/10 1/10 1/10 X ˉ f ( X ˉ ) 4/20 5/20 6/20 7/20 16/20 9/20 10/20 11/20 12/20 X ˉ 2 f ( X ˉ ) 16/40 25/40 36/40 49/40 128/40 81/40 100/40 121/40 144/40
Mean of sampling distribution
μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 4 = μ \mu_{\bar{X}}=E(\bar{X})=\sum\bar{X}_if(\bar{X}_i)=4=\mu μ X ˉ = E ( X ˉ ) = ∑ X ˉ i f ( X ˉ i ) = 4 = 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 = 700 40 − ( 4 ) 2 = 3 2 = σ 2 n ( N − n N − 1 ) =\dfrac{700}{40}-(4)^2=\dfrac{3}{2}= \dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1}) = 40 700 − ( 4 ) 2 = 2 3 = n σ 2 ( N − 1 N − n )
(iv) The standard error of the mean
σ X ˉ = 3 2 ≈ 1.2247 \sigma_{\bar{X}}=\sqrt{\dfrac{3}{2}}\approx1.2247 σ X ˉ = 2 3 ≈ 1.2247
Comments