1. Suppose a group of 700 smokers (who all wanted to give up smoking) were randomly assigned to receive an antidepressant drug or a placebo for six weeks. Of the 386 patients who received the antidepressant drug, 44 were not smoking one year later. Of the 314 patients who received the placebo, 126 were not smoking one year later. Given the null hypothesis $H_0: p_1 = p_2$ and the alternative hypothesis $H_a: p_1 \ne p_2$, conduct a test to see if taking an antidepressant drug can help smokers stop smoking. Use $\alpha = 0.05$

(a)   The rejection region is $|z| >$
(b)   The test statistic is $z =$

The final conclusion is

A. We can reject the null hypothesis that $p_1 = p_2$ and conclude that the antidpressant drug can help smokers stop smoking.
B. There is not sufficient evidence to reject the null hypothesis that $p_1 = p_2$ and conclude that the antidpressant drug can help smokers stop smoking.

2. Construct the 95% confidence interval for the difference between the proportions of those who gave up smoking with and without the antidepressant drug.
$< (p_1 - p_2) <$

Which of the following is the correct interpretation for your answer in part 2?
A. We can be 95% confident that the difference between the proportions of smokers who gave up smoking with and without the antidpressant drug lies in the interval
B. There is a 95% chance that the difference between the proportions of smokers who gave up smoking with and without the antidpressant drug lies in the interval
C. We can be 95% confident that the difference between the proportions of smokers who gave up smoking with and without the antidpressant drug lies in the interval
D. None of the above