1) Point estimate of population proportion of successes "\\hat{p}=\\frac{290}{500}=0.58."
2) "n\\hat{p}=500(0.58)\\geq10," "n(1-\\hat{p})=500(1-0.58)\\geq10." So we can approximate the binomial distribution with a normal distribution.
"z_{value}=1.96\\\\\n\\hat{q}=1-\\hat{p}=1-0.58=0.42.\\\\\nEBP=z_{value}\\sqrt{\\frac{\\hat{p}\\hat{q}}{n}}=1.96\\sqrt{\\frac{(0.58)(0.42)}{500}}\\approx 0.043.\\\\\n(\\hat{p}-EBP,\\hat{p}+EBP)\\\\\n(0.58-0.043,0.58+0.043)\\\\\n(0.537,0.623)\\text{ is our confidence interval}."
3) "H_0: p=0.6, H_1:p<0.6" (left-tailed test).
"pn=(0.6)500>5\\\\\n(1-p)n=(0.4)500>5"
So we will use z-test.
"z_{stat}=\\frac{\\hat{p}-p}{\\sqrt{\\frac{p(1-p)}{n}}}=\\frac{0.58-0.6}{\\sqrt{\\frac{(0.6)(0.4)}{500}}}\\approx -0.9129.\\\\\n\\Phi(z_{cr})=\\frac{1-2\\alpha}{2}=0.45.\\\\\nz_{cr}=-1.64.\\\\\n(-\\infty,-1.64)\\text{ is our critical region}.\\\\\nz_{stat}\\text{ is not in the critical region. So we accept (do not reject) } H_0."
We constructed the 95% confidence interval. It tells us that the population proportion falls in this interval with probability 95% and falls outside this interval with probability 5%. Then we used a hypothesis test to check whether the population proportion equals 0.6 or less than 0.6. We accepted "H_0: p=0.6" at significance level 0.05; 0.6 is in our confidence interval. The result of the 3rd part does not directly follow from the result in the 2nd part.
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