If p(A) = 0.6 and k is the number of successes of A in n trials
(A) Show that p {550<= k <= 650} = 0.999 , for n = 1000
(B) Find n such that p{0.59n <= k <= 0.61n} = 0.95
"n=1000\\\\p=0.6\\\\q=1-p=1-0.6=0.4"
To solve this question, we shall use the Normal approximation to Binomial.
"\\mu=E(x)=np=0.6\\times1000=600"
Variance, "\\sigma^2=npq=0.6\\times0.4\\times1000=240"
This implies that the standard deviation "\\sigma=\\sqrt{\\sigma^2}=\\sqrt{240}=15.4919334"
"a)"
"p(550\\lt k\\lt650)=p({550-\\mu-0.5\\over\\sigma}\\lt Z\\lt{650-\\mu+0.5\\over\\sigma}=p({550-600-0.5\\over15.49}\\lt Z\\lt{650-600+0.5\\over15.49})=p(-3.26\\lt Z\\lt 3.26)=\\phi(3.26)-\\phi(-3.26)=0.9994-0.0006=0.9988\\approx0.999"
as required.
0.5 is the correction factor.
"b)"
"p=0.6\\\\q=1-p=1-0.6=0.4"
"error=\\epsilon={1-0.95\\over2}=0.025"
Now,
"p(0.59n \\le k \\le0.61n) = 2\\phi({\\epsilon\\sqrt{{n\\over pq}}})-1=0.95"
This implies that,
"2\\phi({\\epsilon\\sqrt{{n\\over pq}}})-1=0.95\\implies\\phi(0.025\\sqrt{n\\over0.24})=0.975"
So,
"(0.025\\sqrt{n\\over0.24})=Z_{0.975}" where "Z_{0.975}" is the table value associated with 0.975. From the tables, "Z_{0.975}=1.96"
Now we have that
"(0.025\\sqrt{n\\over0.24})=1.96\\implies\\sqrt{n\\over0.24}=78.4\\implies n=1475.1744\\approx 1475"
For "p(0.59n \\leq k \\le 0.61n) = 0.95" to hold, the value of "n" must be greater than 1475. That is "n\\gt1475".
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