If X X X is a binomial random variable with mean μ = n p μ = np μ = n p and variance σ 2 = n p q , σ^2 = npq, σ 2 = n pq , then the limiting form of the distribution of
Z = X − n p n p q Z=\dfrac{X-np}{\sqrt{npq}} Z = n pq X − n p as n → ∞ , n → ∞, n → ∞ , is the standard normal distribution N ( 0 , 1 ) . N(0,1). N ( 0 , 1 ) .
Given n = 100 , p = 0.9 , q = 1 − p = 0.1 n=100, p=0.9, q=1-p=0.1 n = 100 , p = 0.9 , q = 1 − p = 0.1
μ = n p = 100 ( 0.9 ) = 90 , n p q = 100 ( 0.9 ) ( 0.1 ) = 9 \mu=np=100(0.9)=90, npq=100(0.9)(0.1)=9 μ = n p = 100 ( 0.9 ) = 90 , n pq = 100 ( 0.9 ) ( 0.1 ) = 9 Use the Continuity Correction Factor
(i)
P ( 84 ≤ X ≤ 95 ) = P ( X < 95 + 0.5 ) − P ( X < 84 − 0.5 ) P(84\le X\le95)=P(X<95+0.5)-P(X<84-0.5) P ( 84 ≤ X ≤ 95 ) = P ( X < 95 + 0.5 ) − P ( X < 84 − 0.5 )
= P ( Z < 95.5 − 90 9 ) − P ( Z < 83.5 − 90 9 ) =P(Z<\dfrac{95.5-90}{\sqrt{9}})-P(Z<\dfrac{83.5-90}{\sqrt{9}}) = P ( Z < 9 95.5 − 90 ) − P ( Z < 9 83.5 − 90 )
≈ P ( Z < 1.833333 ) − P ( Z < − 2.166667 ) \approx P(Z<1.833333)-P(Z<-2.166667) ≈ P ( Z < 1.833333 ) − P ( Z < − 2.166667 )
≈ 0.96662 − 0.01513 ≈ 0.9515 \approx0.96662-0.01513\approx0.9515 ≈ 0.96662 − 0.01513 ≈ 0.9515
(ii)
P ( X < 86 ) = P ( X < 86 − 0.5 ) P(X<86)=P(X<86-0.5) P ( X < 86 ) = P ( X < 86 − 0.5 )
= P ( Z < 85.5 − 90 9 ) ≈ P ( Z < − 1.833333 ) =P(Z<\dfrac{85.5-90}{\sqrt{9}})\approx P(Z<-1.833333) = P ( Z < 9 85.5 − 90 ) ≈ P ( Z < − 1.833333 )
≈ 0.0334 \approx0.0334 ≈ 0.0334
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