We have population values 2 , 5 , 8 , 11 , 13 , 25 2,5,8,11,13,25 2 , 5 , 8 , 11 , 13 , 25 population size N = 6 N=6 N = 6 and sample size n = 5. n=5. n = 5.
Thus, the number of possible samples which can be drawn without replacements is ( 6 5 ) = 6. \dbinom{6}{5}=6. ( 5 6 ) = 6.
1.
m e a n = μ = 2 + 5 + 8 + 11 + 13 + 25 6 = 32 3 mean=\mu=\dfrac{2+5+8+11+13+25}{6}=\dfrac{32}{3} m e an = μ = 6 2 + 5 + 8 + 11 + 13 + 25 = 3 32 2
V a r i a n c e = σ 2 = 1 6 ( ( 2 − 32 3 ) 2 + ( 5 − 32 3 ) 2 Variance=\sigma^2=\dfrac{1}{6}((2-\dfrac{32}{3})^2+(5-\dfrac{32}{3})^2 Va r ian ce = σ 2 = 6 1 (( 2 − 3 32 ) 2 + ( 5 − 3 32 ) 2
+ ( 8 − 32 3 ) 2 + ( 11 − 32 3 ) 2 + ( 13 − 32 3 ) 2 +(8-\dfrac{32}{3})^2+(11-\dfrac{32}{3})^2+(13-\dfrac{32}{3})^2 + ( 8 − 3 32 ) 2 + ( 11 − 3 32 ) 2 + ( 13 − 3 32 ) 2
+ ( 25 − 32 3 ) 2 ) = 488 9 +(25-\dfrac{32}{3})^2)=\dfrac{488}{9} + ( 25 − 3 32 ) 2 ) = 9 488
σ = σ 2 = 488 9 = 2 122 3 ≈ 7.363574 \sigma=\sqrt{\sigma^2}=\sqrt{\dfrac{488}{9}}=\dfrac{2\sqrt{122}}{3}\approx7.363574 σ = σ 2 = 9 488 = 3 2 122 ≈ 7.363574
3.
S a m p l e v a l u e s S a m p l e m e a n ( x ˉ ) 2 , 5 , 8 , 11 , 13 7.8 2 , 5 , 8 , 11 , 25 10.2 2 , 5 , 8 , 13 , 25 10.6 2 , 5 , 11 , 13 , 25 11.2 2 , 8 , 11 , 13 , 25 11.8 5 , 8 , 11 , 13 , 25 12.4 \def\arraystretch{1.5}
\begin{array}{c:c}
Sample\ values & Sample\ mean\ (\bar{x}) \\ \hline
2,5,8,11,13 & 7.8 \\
\hdashline
2,5,8,11,25 & 10.2 \\
\hdashline
2,5,8,13,25 & 10.6 \\
\hdashline
2,5,11,13,25 & 11.2\\
\hdashline
2,8,11,13,25 & 11.8 \\
\hdashline
5,8,11,13,25 & 12.4 \\
\hdashline
\end{array} S am pl e v a l u es 2 , 5 , 8 , 11 , 13 2 , 5 , 8 , 11 , 25 2 , 5 , 8 , 13 , 25 2 , 5 , 11 , 13 , 25 2 , 8 , 11 , 13 , 25 5 , 8 , 11 , 13 , 25 S am pl e m e an ( x ˉ ) 7.8 10.2 10.6 11.2 11.8 12.4
5. The sampling distribution of the sample mean x ˉ \bar{x} x ˉ and its mean and standard deviation are:
x ˉ f f ( x ˉ ) x ˉ f ( x ˉ ) x ˉ 2 f ( x ˉ ) 7.8 1 1 / 6 7.8 / 6 60.84 / 6 10.2 1 1 / 6 10.2 / 6 104.04 / 6 10.6 1 1 / 6 10.6 / 6 112.36 / 6 11.2 1 1 / 6 11.2 / 6 125.44 / 6 11.8 1 1 / 6 11.8 / 6 139.24 / 6 12.4 1 1 / 6 12.4 / 6 153.76 / 6 S u m = 6 1 32 / 3 695.68 / 6 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& \bar{x} & f & f(\bar{x}) & \bar{x}f(\bar{x}) & \bar{x}^2f(\bar{x})\\ \hline
& 7.8 & 1 & 1/6 & 7.8/6 & 60.84/6 \\
\hdashline
& 10.2 & 1 & 1/6 & 10.2/6 & 104.04/6 \\
\hdashline
& 10.6 & 1 & 1/6 & 10.6/6 & 112.36/6 \\
\hdashline
& 11.2 & 1 & 1/6 & 11.2/6 & 125.44/6 \\
\hdashline
& 11.8 & 1 & 1/6 & 11.8/6 & 139.24/6 \\
\hdashline
& 12.4 & 1 & 1/6 & 12.4/6 & 153.76/6 \\
\hdashline
Sum= & & 6 & 1 & 32/3 & 695.68/6 \\
\hdashline
\end{array} S u m = x ˉ 7.8 10.2 10.6 11.2 11.8 12.4 f 1 1 1 1 1 1 6 f ( x ˉ ) 1/6 1/6 1/6 1/6 1/6 1/6 1 x ˉ f ( x ˉ ) 7.8/6 10.2/6 10.6/6 11.2/6 11.8/6 12.4/6 32/3 x ˉ 2 f ( x ˉ ) 60.84/6 104.04/6 112.36/6 125.44/6 139.24/6 153.76/6 695.68/6
6.
μ X ˉ = E ( X ˉ ) = ∑ x ˉ f ( x ˉ ) = 32 3 \mu_{\bar{X}}=E(\bar{X})=\sum\bar{x}f(\bar{x})=\dfrac{32}{3} μ X ˉ = E ( X ˉ ) = ∑ x ˉ f ( x ˉ ) = 3 32
7.
V a r ( X ˉ ) = σ X ˉ 2 = ∑ x ˉ 2 f ( x ˉ ) − ( ∑ x ˉ f ( x ˉ ) ) 2 Var(\bar{X})=\sigma_{\bar{X}}^2=\sum\bar{x}^2f(\bar{x})-(\sum\bar{x}f(\bar{x}))^2 Va r ( X ˉ ) = σ X ˉ 2 = ∑ x ˉ 2 f ( x ˉ ) − ( ∑ x ˉ f ( x ˉ ) ) 2
= 347.84 3 − ( 32 3 ) 2 = 19.52 9 =\dfrac{347.84}{3}-(\dfrac{32}{3})^2=\dfrac{19.52}{9} = 3 347.84 − ( 3 32 ) 2 = 9 19.52
σ X ˉ = σ X ˉ 2 = 19.52 9 ≈ 1.472715 \sigma_{\bar{X}}=\sqrt{\sigma_{\bar{X}}^2}=\sqrt{\dfrac{19.52}{9}}\approx1.472715 σ X ˉ = σ X ˉ 2 = 9 19.52 ≈ 1.472715
Check
μ X ˉ = E ( X ˉ ) = 32 3 = μ \mu_{\bar{X}}=E(\bar{X})=\dfrac{32}{3}=\mu μ X ˉ = E ( X ˉ ) = 3 32 = μ
V a r ( X ˉ ) = 19.52 9 = σ 2 n ⋅ N − n N − 1 = 488 9 ( 5 ) ⋅ 6 − 5 6 − 1 Var(\bar{X})=\dfrac{19.52}{9}=\dfrac{\sigma^2}{n}\cdot\dfrac{N-n}{N-1}=\dfrac{488}{9(5)}\cdot\dfrac{6-5}{6-1} Va r ( X ˉ ) = 9 19.52 = n σ 2 ⋅ N − 1 N − n = 9 ( 5 ) 488 ⋅ 6 − 1 6 − 5
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