i)
μ = n p = 100 ⋅ 0.1 = 10 \mu=np=100\cdot0.1=10 μ = n p = 100 ⋅ 0.1 = 10
σ = n p ( 1 − p ) = 100 ⋅ 0.1 ( 1 − 0.1 ) = 3 \sigma=\sqrt{np(1-p)}=\sqrt{100\cdot0.1(1-0.1)}=3 σ = n p ( 1 − p ) = 100 ⋅ 0.1 ( 1 − 0.1 ) = 3
P ( 10 ≤ X ≤ 12 ) ≈ P ( 9.5 ≤ Y ≤ 12.5 ) = P(10 \leq X \leq12)\approx P(9.5 \leq Y \leq12.5)= P ( 10 ≤ X ≤ 12 ) ≈ P ( 9.5 ≤ Y ≤ 12.5 ) =
= P ( 9.5 − 10 3 ≤ Z ≤ 12.5 − 10 3 ) = P ( − 0.17 ≤ Z ≤ 0.83 ) = =P(\frac{9.5-10}{3} \leq Z\leq\frac{12.5-10}{3})=P(-0.17\leq Z\leq0.83)= = P ( 3 9.5 − 10 ≤ Z ≤ 3 12.5 − 10 ) = P ( − 0.17 ≤ Z ≤ 0.83 ) =
= 0.7967 − 0.4325 = 0.3642 =0.7967-0.4325=0.3642 = 0.7967 − 0.4325 = 0.3642
ii)
λ = n p = 10 \lambda=np=10 λ = n p = 10
P ( X = k ) = λ k e − λ k ! P(X=k)=\frac{\lambda^ke^{-\lambda}}{k!} P ( X = k ) = k ! λ k e − λ
P ( 10 ≤ X ≤ 12 ) = P ( X = 10 ) + P ( X = 11 ) + P ( X = 12 ) P(10 \leq X \leq12)=P(X=10)+P(X=11)+P(X=12) P ( 10 ≤ X ≤ 12 ) = P ( X = 10 ) + P ( X = 11 ) + P ( X = 12 )
P ( X = 10 ) = 1 0 10 e − 10 10 ! = 0.1251 P(X=10)=\frac{10^{10}e^{-10}}{10!}=0.1251 P ( X = 10 ) = 10 ! 1 0 10 e − 10 = 0.1251
P ( X = 11 ) = 1 0 11 e − 10 11 ! = 0.1137 P(X=11)=\frac{10^{11}e^{-10}}{11!}=0.1137 P ( X = 11 ) = 11 ! 1 0 11 e − 10 = 0.1137
P ( X = 12 ) = 1 0 12 e − 10 12 ! = 0.0948 P(X=12)=\frac{10^{12}e^{-10}}{12!}=0.0948 P ( X = 12 ) = 12 ! 1 0 12 e − 10 = 0.0948
P ( 10 ≤ X ≤ 12 ) = 0.1251 + 0.1137 + 0.0948 = 0.3336 P(10 \leq X \leq12)=0.1251+0.1137+0.0948=0.3336 P ( 10 ≤ X ≤ 12 ) = 0.1251 + 0.1137 + 0.0948 = 0.3336
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