a) Point Estimate is "\\hat{p}=\\dfrac{x}{n},\\ x" is the number of successes, "n" is the sample size
b) The critical value for "\\alpha=0.05" is "z_c=z_{1-\\alpha\/2}=1.96." The corresponding confidence interval is computed as shown below:
"=(\\hat{p}-z_c\\sqrt{{\\hat{p}(1-\\hat{p})\\over n}}, \\hat{p}+z_c\\sqrt{{\\hat{p}(1-\\hat{p})\\over n}})="
"=(0.58-1.96\\sqrt{{0.58(1-0.58)\\over 500}}, 0.58+1.96\\sqrt{{0.58(1-0.58)\\over 500}})="
Therefore, based on the data provided, the "95\\%" confidence interval for the population proportion is "0.5367<p<0.6233," which indicates that we are 95% confident that the true population proportion "p" is contained by the interval "(0.5367, 0.6233)."
c) The following null and alternative hypotheses need to be tested:
"H_0: p\\geq0.6"
"H_1:p<0.6"
This corresponds to a left-tailed test, for which a z-test for one population proportion needs to be used.
Based on the information provided, the significance level is "\\alpha=0.05," and the critical value for a left-tailed test is "z_c=-1.645."
The rejection region for this left-tailed test is "R=\\{z:z<-1.645\\}"
The z-statistic is computed as follows:
Since it is observed that "z=-0.9129>-1.645=z_c," it is then concluded that the null hypothesis is not rejected. Therefore, there is not enough evidence to claim that the population proportion "p" is less than "0.6" , at the "\\alpha=0.05" significance level.
Using the P-value approach: The p-value is "p=0.180621," and since "p=0.180621>0.05," it is concluded that the null hypothesis is not rejected. Therefore, there is not enough evidence to claim that the population proportion "p" is less than "0.6," at the "\\alpha=0.05" significance level.
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