Mean
μ = 2 + 5 + 3 + 4 + 2 5 = 3.2 \mu=\dfrac{2+5+3+4+2}{5}=3.2 μ = 5 2 + 5 + 3 + 4 + 2 = 3.2 Variance
σ 2 = 1 5 ( ( 2 − 3.2 ) 2 + ( 5 − 3.2 ) 2 + ( 3 − 3.2 ) 2 \sigma^2=\dfrac{1}{5}\big((2-3.2)^2+(5-3.2)^2+(3-3.2)^2 σ 2 = 5 1 ( ( 2 − 3.2 ) 2 + ( 5 − 3.2 ) 2 + ( 3 − 3.2 ) 2 + ( 4 − 3.2 ) 2 + ( 2 − 3.2 ) 2 ) = 1.36 +(4-3.2)^2+(2-3.2)^2\big)=1.36 + ( 4 − 3.2 ) 2 + ( 2 − 3.2 ) 2 ) = 1.36
Standard deviation
σ = σ 2 = 1.36 ≈ 1.1662 \sigma=\sqrt{\sigma^2}=\sqrt{1.36}\approx1.1662 σ = σ 2 = 1.36 ≈ 1.1662
We have population values 2 , 5 , 3 , 4 , 2 2,5,3,4,2 2 , 5 , 3 , 4 , 2 population size N = 5 N=5 N = 5 and sample size n = 3. n=3. n = 3. Thus, the number of possible samples which can be drawn without replacement is
( N n ) = ( 5 3 ) = 10 \dbinom{N}{n}=\dbinom{5}{3}=10 ( n N ) = ( 3 5 ) = 10 S a m p l e S a m p l e S a m p l e m e a n N o . v a l u e s ( X ˉ ) 1 2 , 2 , 3 7 / 3 2 2 , 2 , 4 8 / 3 3 2 , 2 , 5 3 4 2 , 3 , 4 3 5 2 , 3 , 5 10 / 3 6 2 , 4 , 5 11 / 3 7 2 , 3 , 4 3 8 2 , 3 , 5 10 / 3 9 2 , 4 , 5 11 / 3 10 3 , 4 , 5 4 \def\arraystretch{1.5}
\begin{array}{c:c:c}
Sample & Sample & Sample \ mean \\
No. & values & (\bar{X}) \\ \hline
1 & 2,2,3 & 7/3 \\
\hdashline
2 & 2,2,4 & 8/3 \\
\hdashline
3 & 2,2,5 & 3 \\
\hdashline
4 & 2,3,4 & 3 \\
\hdashline
5 & 2,3,5 & 10/3 \\
\hdashline
6 & 2,4,5 & 11/3 \\
\hdashline
7 & 2,3,4 & 3 \\
\hdashline
8 & 2,3,5 & 10/3 \\
\hdashline
9 & 2,4,5 & 11/3 \\
\hdashline
10 & 3,4,5 & 4 \\ \hline
\end{array} S am pl e N o . 1 2 3 4 5 6 7 8 9 10 S am pl e v a l u es 2 , 2 , 3 2 , 2 , 4 2 , 2 , 5 2 , 3 , 4 2 , 3 , 5 2 , 4 , 5 2 , 3 , 4 2 , 3 , 5 2 , 4 , 5 3 , 4 , 5 S am pl e m e an ( X ˉ ) 7/3 8/3 3 3 10/3 11/3 3 10/3 11/3 4
The sampling distribution of the sample means.
X ˉ f f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 7 / 3 1 1 / 10 7 / 30 49 / 90 8 / 3 1 1 / 10 8 / 30 64 / 90 3 3 3 / 10 27 / 30 243 / 90 10 / 3 2 2 / 10 20 / 30 200 / 90 11 / 3 2 2 / 10 22 / 30 242 / 90 4 1 1 / 10 12 / 30 144 / 90 T o t a l 10 1 96 / 30 942 / 90 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c}
\bar{X} & f & f(\bar{X}) & \bar{X}f(\bar{X})& \bar{X}^2f(\bar{X}) \\ \hline
7/3 & 1& 1/10 & 7/30 & 49/90 \\
\hdashline
8/3 & 1& 1/10 & 8/30 & 64/90 \\
\hdashline
3 & 3 & 3/10 & 27/30 & 243/90 \\
\hdashline
10/3 & 2 & 2/10 & 20/30 & 200/90\\
\hdashline
11/3 & 2 & 2/10 & 22/30 & 242/90 \\
\hdashline
4 & 1 & 1/10 & 12/30 & 144/90 \\
\hdashline
Total & 10 & 1 & 96/30 & 942/90 \\ \hline
\end{array} X ˉ 7/3 8/3 3 10/3 11/3 4 T o t a l f 1 1 3 2 2 1 10 f ( X ˉ ) 1/10 1/10 3/10 2/10 2/10 1/10 1 X ˉ f ( X ˉ ) 7/30 8/30 27/30 20/30 22/30 12/30 96/30 X ˉ 2 f ( X ˉ ) 49/90 64/90 243/90 200/90 242/90 144/90 942/90
E ( X ˉ ) = ∑ X ˉ f ( X ˉ ) = 96 30 = 3.2 E(\bar{X})=\sum\bar{X}f(\bar{X})=\dfrac{96}{30}=3.2 E ( X ˉ ) = ∑ X ˉ f ( X ˉ ) = 30 96 = 3.2 The mean of the sampling distribution of the sample means is equal to the
the mean of the population.
E ( X ˉ ) = 3.2 = μ E(\bar{X})=3.2=\mu E ( X ˉ ) = 3.2 = μ
V a r ( X ˉ ) = ∑ X ˉ 2 f ( X ˉ ) − ( ∑ X ˉ f ( X ˉ ) ) 2 Var(\bar{X})=\sum\bar{X}^2f(\bar{X})-(\sum\bar{X}f(\bar{X}))^2 Va r ( X ˉ ) = ∑ X ˉ 2 f ( X ˉ ) − ( ∑ X ˉ f ( X ˉ ) ) 2 = 942 90 − ( 96 30 ) 2 = 68 300 = 17 75 =\dfrac{942}{90}-(\dfrac{96}{30})^2=\dfrac{68}{300}=\dfrac{17}{75} = 90 942 − ( 30 96 ) 2 = 300 68 = 75 17 V a r ( X ˉ ) = 17 75 ≈ 0.4761 \sqrt{Var(\bar{X})}=\sqrt{\dfrac{17}{75}}\approx0.4761 Va r ( X ˉ ) = 75 17 ≈ 0.4761 Verification:
V a r ( X ˉ ) = σ 2 n ( N − n N − 1 ) = 1.36 3 ( 5 − 3 5 − 1 ) Var(\bar{X})=\dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1})=\dfrac{1.36}{3}(\dfrac{5-3}{5-1}) Va r ( X ˉ ) = n σ 2 ( N − 1 N − n ) = 3 1.36 ( 5 − 1 5 − 3 ) = 0.68 3 = 17 75 , T r u e =\dfrac{0.68}{3}=\dfrac{17}{75}, True = 3 0.68 = 75 17 , T r u e
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