We have that
n=100, p=0.1
P(10 ≤ X ≤ 12) - ?
i) Normal distribution
Mean μ = n p = 100 ⋅ 0.1 = 10 \mu = np= 100\cdot0.1=10 μ = n p = 100 ⋅ 0.1 = 10
Standard deviation σ = n p ( 1 − p ) = 100 ⋅ 0.1 ( 1 − 0.1 ) = 10 ⋅ 0.9 = 9 = 3 \sigma = \sqrt{np(1-p)}=\sqrt{100\cdot0.1(1-0.1)}=\sqrt{10\cdot0.9}=\sqrt9=3 σ = n p ( 1 − p ) = 100 ⋅ 0.1 ( 1 − 0.1 ) = 10 ⋅ 0.9 = 9 = 3
P ( 10 ≤ X ≤ 12 ) = F ( 12 − μ σ ) − F ( 10 − μ σ ) = F ( 12 − 10 3 ) − F ( 10 − 10 3 ) = F ( 2 3 ) − F ( 0 ) ≈ 0.7486 − 0.5 = 0.2486 P(10 ≤ X ≤ 12)=F(\frac{12-\mu}{\sigma})-F(\frac{10-\mu}{\sigma})=F(\frac{12-10}{3})-F(\frac{10-10}{3})=F(\frac{2}{3})-F(0)\approx 0.7486 - 0.5 = 0.2486 P ( 10 ≤ X ≤ 12 ) = F ( σ 12 − μ ) − F ( σ 10 − μ ) = F ( 3 12 − 10 ) − F ( 3 10 − 10 ) = F ( 3 2 ) − F ( 0 ) ≈ 0.7486 − 0.5 = 0.2486
ii) Poisson distribution
λ = n p = 100 ⋅ 0.1 = 10 \lambda=np=100\cdot0.1=10 λ = n p = 100 ⋅ 0.1 = 10
The poisson probability is calculated by the formula
P ( k ) = λ k e − λ k ! P(k)=\frac{\lambda^ke^{-\lambda}}{k!} P ( k ) = k ! λ k e − λ
P ( 10 ≤ X ≤ 12 ) = P ( 10 ) + P ( 11 ) + P ( 12 ) = 1 0 10 e − 10 10 ! + 1 0 11 e − 10 11 ! + 1 0 12 e − 10 12 ! = 0.12511 + 0.11374 + 0.09478 = 0.33363 P(10 ≤ X ≤ 12)=P(10)+P(11)+P(12)=\frac{10^{10}e^{-10}}{10!}+\frac{10^{11}e^{-10}}{11!}+\frac{10^{12}e^{-10}}{12!}=0.12511+0.11374+0.09478=0.33363 P ( 10 ≤ X ≤ 12 ) = P ( 10 ) + P ( 11 ) + P ( 12 ) = 10 ! 1 0 10 e − 10 + 11 ! 1 0 11 e − 10 + 12 ! 1 0 12 e − 10 = 0.12511 + 0.11374 + 0.09478 = 0.33363
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