Proportion p has approximately normal distribution with parameters "\\mu_p=\\hat p. \\space \\sigma_p=\\sqrt{\\frac{\\hat p\\cdot (1-\\hat p)}{n}}". Thus for p analisis.we use normal distribution"N(\\hat p,\\sqrt{\\frac{\\hat p\\cdot (1-\\hat p)}{n}})". So C- confidence interval for mean M(p) will be "\\hat p \\pm z^*_\\frac{1-C}{2}\\cdot \\sqrt{\\frac{\\hat p\\cdot (1-\\hat p)}{n}}"
where "z^*_\\frac{1+C}{2}" is such level that P(N(0,1)"> z^*_\\frac{1-C}{2}=\\frac{1-C}{2}" Therfore margin error will be E="z^*_\\frac{1-C}{2}\\cdot \\sqrt{\\frac{\\hat p\\cdot (1-\\hat p)}{n}}" . Fron this equation we define size of sample "n=\\frac{\\hat p\\cdot (1-\\hat p)\\cdot(z^*_\\frac{1-C}{2})^2 }{E^2}" .We are given with E=8%=0.08, "\\hat p=145\/200=0.725" . C=98%=0.98 and calculate: "z^*_\\frac{1-C}{2}=z^*_{0.01}="
=qnorm(0.99,0,1)=2.326 where qnorm is statistical function from Mathcad therefore we have "n=\\frac{0.725\\cdot(1-0.725)\\cdot 2.326^2}{0.08^2}=168.543\\approx 169"
So size of sample should be 169.
b)
H0: p"\\le" 0.65 - zero hypothesis;
H1: p>0.65- alternative hypothesis;
"\\alpha=0.05" significance level;
z=qnorm(1-0.05,0,1)=1.645- z-value corresponding to significance level(by using Mathcad);
Let "Y=\\frac{\\frac{X}{200}-0.65}{\\sqrt{0.65\\cdot (1-0.85)\/200}}"
If historical value 0.65 of the proportion is 0.65 is true then we will have Y~N(0,1) or Y will have standard normal distribution and for rejection of the hypothesis X/200=0.65 is enough to meet an event witch follows from 0.65 hypothesis but has small probability. Such event is Y"\\ge z_{0.05}" with probebility 0.05
We use equation Y=z=1.645 for finding minimal bound for Y and therefore for X.
From it eqution we have "X=(0.65+1.645\\cdot\\sqrt{0.65\\cdot (1-0.65)\/200})\\cdot {200}" =142.689"\\approx 143" .
So for rejecting hypothesis p<=65% with confidence it is enough to form sample with size 143 or more.
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