a ) a) a )
p ( x ≤ 10 ) = p ( x = 6 ) + p ( x = 7 ) + p ( x = 8 ) + p ( x = 9 ) + p ( x = 10 ) = 0.03 + 0.08 + 0.15 + 0.2 + 0.19 = 0.65 p( x \le10 ) = p(x=6) + p(x =7) + p(x=8) + p(x=9) + p(x=10)
= 0.03 + 0.08 + 0.15 + 0.2 + 0.19
= 0.65 p ( x ≤ 10 ) = p ( x = 6 ) + p ( x = 7 ) + p ( x = 8 ) + p ( x = 9 ) + p ( x = 10 ) = 0.03 + 0.08 + 0.15 + 0.2 + 0.19 = 0.65
b ) b) b )
p ( 8 ≤ x ≤ 12 ) = p ( x = 8 ) + p ( x = 9 ) + p ( x = 10 ) + p ( x = 11 ) + p ( x = 12 ) = 0.15 + 0.2 + 0.19 + 0.16 + 0.1 = 0.8 p( 8 \le x \le 12) = p(x=8) + p(x=9) + p(x=10) + p(x=11) + p(x=12)
= 0.15 + 0.2 + 0.19 + 0.16 + 0.1
= 0.8 p ( 8 ≤ x ≤ 12 ) = p ( x = 8 ) + p ( x = 9 ) + p ( x = 10 ) + p ( x = 11 ) + p ( x = 12 ) = 0.15 + 0.2 + 0.19 + 0.16 + 0.1 = 0.8
c ) c) c )
The probability that no days will be lost next summer will be given by,
p ( x = 0 ) = 1 − ( p ( x = 6 ) + p ( x = 7 ) + p ( x = 8 ) + p ( x = 9 ) + p ( 10 ) + p ( 11 ) + p ( 12 ) ) = 1 − 1 = 0 p(x=0)= 1 - ( p(x=6) + p(x=7) + p(x=8) + p(x=9) + p(10) + p(11) + p(12))
= 1 - 1
= 0 p ( x = 0 ) = 1 − ( p ( x = 6 ) + p ( x = 7 ) + p ( x = 8 ) + p ( x = 9 ) + p ( 10 ) + p ( 11 ) + p ( 12 )) = 1 − 1 = 0
Therefore, the probability that there will be no days lost next summer is zero. We can conclude that there will be some days lost next summer.
d ) d) d )
Mean
E ( x ) = ∑ x p ( x ) = ( 6 × 0.03 ) + ( 7 × 0.08 ) + ( 8 × 0.15 ) + ( 9 × 0.2 ) + ( 10 × 0.19 ) + ( 11 × 0.16 ) + ( 12 × 0.1 ) + ( 13 × 0.07 ) + ( 14 × 0.02 ) = 9.79 ≈ 10 E(x)=\sum xp(x)=(6\times 0.03)+(7\times 0.08)+(8\times 0.15)+(9\times 0.2)+(10\times0.19)+(11\times0.16)+(12\times0.1)+(13\times0.07)+(14\times0.02)=9.79\approx 10 E ( x ) = ∑ x p ( x ) = ( 6 × 0.03 ) + ( 7 × 0.08 ) + ( 8 × 0.15 ) + ( 9 × 0.2 ) + ( 10 × 0.19 ) + ( 11 × 0.16 ) + ( 12 × 0.1 ) + ( 13 × 0.07 ) + ( 14 × 0.02 ) = 9.79 ≈ 10
Interpretation,
The expected number of days that will be lost next summer due to weather is approximately equal to 10 days.
Standard deviation.
We first determine the variance given by,
v a r ( x ) = E ( x 2 ) − ( E ( x ) ) 2 var(x)=E(x^2)-(E(x))^2 v a r ( x ) = E ( x 2 ) − ( E ( x ) ) 2
Now,
E ( X 2 ) = ∑ x 2 p ( x ) = ( 36 × 0.03 ) + ( 49 × 0.08 ) + ( 64 × 0.15 ) + ( 81 × 0.2 ) + ( 100 × 0.19 ) + ( 121 × 0.16 ) + ( 144 × 0.1 ) + ( 169 × 0.07 ) + ( 196 × 0.02 ) = 99.31 E(X^2)=\sum x^2p(x)=(36\times 0.03)+(49\times 0.08)+(64\times 0.15)+(81\times 0.2)+(100\times0.19)+(121\times0.16)+(144\times0.1)+(169\times0.07)+(196\times0.02)=99.31 E ( X 2 ) = ∑ x 2 p ( x ) = ( 36 × 0.03 ) + ( 49 × 0.08 ) + ( 64 × 0.15 ) + ( 81 × 0.2 ) + ( 100 × 0.19 ) + ( 121 × 0.16 ) + ( 144 × 0.1 ) + ( 169 × 0.07 ) + ( 196 × 0.02 ) = 99.31
So,
v a r ( x ) = 99.31 − 9.7 9 2 = 3.4659 var(x)=99.31-9.79^2=3.4659 v a r ( x ) = 99.31 − 9.7 9 2 = 3.4659
Standard deviation is,
s d ( x ) = v a r ( x ) = 3.4659 = 1.86169278 sd(x)=\sqrt{var (x)}=\sqrt{3.4659}=1.86169278 s d ( x ) = v a r ( x ) = 3.4659 = 1.86169278
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