1.
We have population values 1 , 2 , 3 , 4 , 5 , 6 1,2,3,4,5,6 1 , 2 , 3 , 4 , 5 , 6 population size N = 6. N=6. N = 6.
μ = 1 + 2 + 3 + 4 + 5 + 6 6 = 3.5 \mu=\dfrac{1+2+3+4+5+6}{6}=3.5 μ = 6 1 + 2 + 3 + 4 + 5 + 6 = 3.5 2.
σ 2 = 1 N ∑ i ( x i − μ ) 2 \sigma^2=\dfrac{1}{N}\sum_i(x_i-\mu)^2 σ 2 = N 1 i ∑ ( x i − μ ) 2
σ 2 = 1 6 ( ( 1 − 3.5 ) 2 + ( 2 − 2.5 ) 2 + ( 3 − 3.5 ) 2 + ( 4 − 3.5 ) 2 \sigma^2=\dfrac{1}{6}\big((1-3.5)^2+(2-2.5)^2+(3-3.5)^2+(4-3.5)^2 σ 2 = 6 1 ( ( 1 − 3.5 ) 2 + ( 2 − 2.5 ) 2 + ( 3 − 3.5 ) 2 + ( 4 − 3.5 ) 2
( 5 − 3.5 ) 2 + ( 6 − 3.5 ) 2 ) = 17.5 6 ≈ 2.916667 (5-3.5)^2+(6-3.5)^2\big)=\dfrac{17.5}{6}\approx2.916667 ( 5 − 3.5 ) 2 + ( 6 − 3.5 ) 2 ) = 6 17.5 ≈ 2.916667 3.
σ = σ 2 = 17.5 6 ≈ 1.707825 \sigma=\sqrt{\sigma^2}=\sqrt{\dfrac{17.5}{6}}\approx1.707825 σ = σ 2 = 6 17.5 ≈ 1.707825
4.
Let sample size n = 2. n=2. n = 2. Thus, the number of possible samples which can be drawn without replacement is
( N n ) = ( 6 2 ) = 15 \dbinom{N}{n}=\dbinom{6}{2}=15 ( n N ) = ( 2 6 ) = 15
2.
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 1 , 2 3 / 2 2 1 , 3 4 / 2 3 1 , 4 5 / 2 4 1 , 5 6 / 2 5 1 , 6 7 / 2 6 2 , 3 5 / 2 7 2 , 4 6 / 2 8 2 , 5 7 / 2 9 2 , 6 8 / 2 10 3 , 4 7 / 2 11 3 , 5 8 / 2 12 3 , 6 9 / 2 13 4 , 5 9 / 2 14 4 , 6 10 / 2 15 5 , 6 11 / 2 \def\arraystretch{1.5}
\begin{array}{c:c:c}
Sample & Sample & Sample \ mean \\
No. & values & (\bar{X}) \\ \hline
1 & 1,2 & 3/2 \\
\hdashline
2 & 1,3 & 4/2 \\
\hdashline
3 & 1,4 & 5/2 \\
\hdashline
4 & 1,5 & 6/2\\
\hdashline
5 & 1,6 & 7/2 \\
\hdashline
6 & 2,3 & 5/2 \\
\hline
7 & 2,4 & 6/2 \\
\hline
8 & 2,5 & 7/2 \\
\hline
9 & 2,6 & 8/2 \\
\hline
10 & 3,4 & 7/2 \\
\hline
11 & 3,5 & 8/2 \\
\hline
12 & 3,6 & 9/2 \\
\hline
13 & 4, 5 & 9/2 \\
\hline
14 & 4,6 & 10/2 \\
\hline
15 & 5,6 & 11/2 \\
\hline
\end{array} S am pl e N o . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 S am pl e v a l u es 1 , 2 1 , 3 1 , 4 1 , 5 1 , 6 2 , 3 2 , 4 2 , 5 2 , 6 3 , 4 3 , 5 3 , 6 4 , 5 4 , 6 5 , 6 S am pl e m e an ( X ˉ ) 3/2 4/2 5/2 6/2 7/2 5/2 6/2 7/2 8/2 7/2 8/2 9/2 9/2 10/2 11/2
X ˉ f f ( X ˉ ) X ˉ f ( X ˉ ) X ˉ 2 f ( X ˉ ) 3 / 2 1 1 / 15 3 / 30 9 / 60 4 / 2 1 1 / 15 4 / 30 16 / 60 5 / 2 2 2 / 15 10 / 30 50 / 90 6 / 2 2 2 / 15 12 / 30 72 / 60 7 / 2 3 3 / 10 21 / 30 147 / 60 8 / 2 2 2 / 15 16 / 30 128 / 60 9 / 2 2 2 / 15 18 / 30 162 / 60 10 / 2 1 1 / 15 10 / 30 100 / 60 11 / 2 1 1 / 15 11 / 30 121 / 60 T o t a l 15 1 105 / 30 805 / 60 \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
3/2 & 1& 1/15 & 3/30 & 9/60 \\
\hdashline
4/2 & 1 & 1/15 & 4/30 & 16/60 \\
\hdashline
5/2 & 2 & 2/15 & 10/30 & 50/90 \\
\hdashline
6/2 & 2 & 2/15 & 12/30 & 72/60 \\
\hdashline
7/2 & 3 & 3/10 & 21/30 & 147/60 \\
\hdashline
8/2 & 2 & 2/15 & 16/30 & 128/60 \\
\hdashline
9/2 & 2& 2/15 & 18/30 & 162/60 \\
\hdashline
10/2 & 1 & 1/15 & 10/30 & 100/60 \\
\hdashline
11/2 & 1 & 1/15 & 11/30 & 121/60 \\
\hdashline
Total & 15 & 1 & 105/30 & 805/60 \\ \hline
\end{array} X ˉ 3/2 4/2 5/2 6/2 7/2 8/2 9/2 10/2 11/2 T o t a l f 1 1 2 2 3 2 2 1 1 15 f ( X ˉ ) 1/15 1/15 2/15 2/15 3/10 2/15 2/15 1/15 1/15 1 X ˉ f ( X ˉ ) 3/30 4/30 10/30 12/30 21/30 16/30 18/30 10/30 11/30 105/30 X ˉ 2 f ( X ˉ ) 9/60 16/60 50/90 72/60 147/60 128/60 162/60 100/60 121/60 805/60
E ( X ˉ ) = ∑ X ˉ f ( X ˉ ) = 105 30 = 3.5 E(\bar{X})=\sum\bar{X}f(\bar{X})=\dfrac{105}{30}=3.5 E ( X ˉ ) = ∑ X ˉ f ( X ˉ ) = 30 105 = 3.5 The mean of the sampling distribution of the sample means is equal to the
the mean of the population.
E ( X ˉ ) = 3.5 = μ E(\bar{X})=3.5=\mu E ( X ˉ ) = 3.5 = 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
= 805 60 − ( 105 30 ) 2 = 7 6 ≈ 1.666667 =\dfrac{805}{60}-(\dfrac{105}{30})^2=\dfrac{7}{6}\approx1.666667 = 60 805 − ( 30 105 ) 2 = 6 7 ≈ 1.666667
V a r ( X ˉ ) = 7 6 ≈ 1.080123 \sqrt{Var(\bar{X})}=\sqrt{\dfrac{7}{6}}\approx1.080123 Va r ( X ˉ ) = 6 7 ≈ 1.080123 Verification:
V a r ( X ˉ ) = σ 2 n ( N − n N − 1 ) = 17.5 6 ( 2 ) ( 6 − 2 6 − 1 ) Var(\bar{X})=\dfrac{\sigma^2}{n}(\dfrac{N-n}{N-1})=\dfrac{17.5}{6(2)}(\dfrac{6-2}{6-1}) Va r ( X ˉ ) = n σ 2 ( N − 1 N − n ) = 6 ( 2 ) 17.5 ( 6 − 1 6 − 2 ) = 3.5 3 ≈ 1.666667 , T r u e =\dfrac{3.5}{3}\approx1.666667, True = 3 3.5 ≈ 1.666667 , T r u e
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