Week 4 HomeworkWeek 4 HomeworkDATA 310Note: It will be impossible to receive full credit for this assignment without providing a written explanationof your answers as indicated in each...

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Week 4 Homework Week 4 Homework DATA 310 Note: It will be impossible to receive full credit for this assignment without providing a written explanation of your answers as indicated in each question. These written explanations are important - they help us understand if you are digesting the content and most importantly, they give you practice interpreting your analysis. Question 1 (a) This question is going to have you further investigate some polling data. Upload “AZPoll-Fake.Rdata” into R. Note: this file began as real polling data from the NYT, but I’ve created a fake variable “clinton.thermometer” so that we have a continuous measure to work with.So don’t take too seriously the conclusions that we draw from this question! (b) “clinton.thermometer” is a (again, fake) measure of how each respondent feels about Secretary Clinton, with 0 indicating that they feel very “cool” towards her, and 100 indicating they feel very “warm” towards her. Pretending for a moment that this is a simple random sample, calculate using our known equations: (i) the 95% confidence interval for “clinton.thermometer”; (ii) the 95% confidence interval for “clinton.thermometer” among those voting for clinton (“clinton”==1); (iii) the 95% confidence interval for “clinton.thermometer”among those not voting for clinton (“clinton”==0). (c) Perform a t test to find the confidence interval for the difference in means of “clinton.thermometer” for those who voted for Clinton and those who voted for Trump. Explain your findings. (d) Confirm for yourself that using the lm() command gives you the same difference in means at the ttest function you got in (c). What does the estimate for “(Intercept)” mean in this output? What does the estimate for “clinton” mean in this output? (You do not have to use the survey weights provided, though you may.) (e) Use the lm() function to build a bivariate regression that uses someone’s years of education to predict their feelings toward Secretary Clinton. (yseduc is your independent variable and clinton.thermometer is your dependent variable.) Interpret the “(Intercept)” and “yseduc” estimates. 1 Question 1