P = .6944 N = 1,597 7. Now use P to estimate π (the population proportion). Specifically, construct a 95% confidence interval for the proportion of the population that falls into the category you’re focusing on. Show your work in the space below. Provide a brief interpretation of the confidence interval. c.i.= p+/- Z(alpha/2)*Op c.i.=69.44+/-1.96*Squareroot((p*q)/N) c.i.= 69.44+/-1.96*Sqrt((69.44*30.56)/1597) c.i.= 69.44+/-1.96*1.15 c.i.= 69.44+/-2.26 67.18 to 71.7 This shows that the population mean proportion of people who voted in the 1992 election is 95% sure to be between .6718 and .717. 8. The next step is to use Stata to check your work. You will use the proportion command to do this. Type proportion followed by the name of your variable. Stata will give each category a name. For example, if your variable was gender, Stata might call the male category “_prop_1” and the female category “_prop_2.” Pay attention to the names Stata assigns each of the categories when comparing your calculated confidence intervals to the ones Stata produces. Make sure that the Stata results are roughly equivalent to those you calculated by hand. If they’re not, go back and figure out what is causing the discrepancy. Once you have consistent results, copy and paste the command and results to the output sheet. Copy and paste the command and results from the Stata results window to the appropriate place in the output sheet, circling the confidence interval on the output .
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