Let X X X n n n p . p. p . 
binomial probability histogram is not too skewed, X X X 
normal distribution with μ = n p \mu=np μ = n p σ = n p q . \sigma=\sqrt{npq}. σ = n pq  . 
In practice, the approximation is adequate provided that both n p ≥ 10 np\ge 10 n p ≥ 10 n q ≥ 10. nq\ge 10. n q ≥ 10. 
Given  n = 100 , p = 0.3 , q = 1 − p = 1 − 0.3 = 0.7. n=100, p=0.3, q=1-p=1-0.3=0.7. n = 100 , p = 0.3 , q = 1 − p = 1 − 0.3 = 0.7. 
n p = 100 ( 0.3 ) = 30 ≥ 10 np=100(0.3)=30\ge 10 n p = 100 ( 0.3 ) = 30 ≥ 10 
n q = 100 ( 0.7 ) = 70 ≥ 10 nq=100(0.7)=70\ge 10 n q = 100 ( 0.7 ) = 70 ≥ 10 
We can use normal approximation for binomial distribution with μ = n p = 100 ( 0.3 ) = 30 , σ = n p q = 100 ( 0.3 ) ( 0.7 ) = 21 \mu=np=100(0.3)=30, \sigma=\sqrt{npq}=\sqrt{100(0.3)(0.7)}=\sqrt{21} μ = n p = 100 ( 0.3 ) = 30 , σ = n pq  = 100 ( 0.3 ) ( 0.7 )  = 21  
Let X ˉ = \bar{X}= X ˉ = X ˉ ∼ N ( μ , σ 2 / n 1 ) \bar{X}\sim N(\mu, \sigma^2/n_1) X ˉ ∼ N ( μ , σ 2 / n 1  ) 
Given n 1 = 50. n_1=50. n 1  = 50. 
a)
P ( X ˉ > 3.9 ) = 1 − P ( Z ≤ 3.9 − 30 21 / 50 ) P(\bar{X}>3.9)=1-P(Z\le\dfrac{3.9-30}{\sqrt{21}/\sqrt{50}}) P ( X ˉ > 3.9 ) = 1 − P ( Z ≤ 21  / 50  3.9 − 30  )  
≈ 1 − P ( Z ≤ − 40.2732 ) ≈ 1 \approx1-P(Z\le-40.2732)\approx1 ≈ 1 − P ( Z ≤ − 40.2732 ) ≈ 1  
b)
P ( 4.1 < X ˉ < 4.4 ) = P ( Z < 4.4 − 30 21 / 50 ) P(4.1<\bar{X}<4.4)=P(Z<\dfrac{4.4-30}{\sqrt{21}/\sqrt{50}}) P ( 4.1 < X ˉ < 4.4 ) = P ( Z < 21  / 50  4.4 − 30  )  
− P ( Z ≤ 4.1 − 30 21 / 50 ) -P(Z\le\dfrac{4.1-30}{\sqrt{21}/\sqrt{50}}) − P ( Z ≤ 21  / 50  4.1 − 30  ) 
≈ P ( Z < − 39.5017 ) − P ( Z ≤ − 39.9646 ) ≈ 0 \approx P(Z<-39.5017)-P(Z\le-39.9646)\approx0 ≈ P ( Z < − 39.5017 ) − P ( Z ≤ − 39.9646 ) ≈ 0  
 c)
P ( X ˉ < 4.0 ) = P ( Z < 4.0 − 30 21 / 50 ) P(\bar{X}<4.0)=P(Z<\dfrac{4.0-30}{\sqrt{21}/\sqrt{50}}) P ( X ˉ < 4.0 ) = P ( Z < 21  / 50  4.0 − 30  )  
≈ P ( Z < − 40.1189 ) ≈ 0 \approx P(Z<-40.1189)\approx0 ≈ P ( Z < − 40.1189 ) ≈ 0  
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