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# Use the SMOKE for this question. The variable cigs is the number of cigarettes smoked per day. How many people in the sample do not

Use the SMOKE for this question.

- The variable
*cigs*is the number of cigarettes smoked per day. How many people in the sample do not smoke at all? What fraction of people claim to smoke 20 cigarettes a day?? Why do you think there is a pileup of people at 20 cigarettes? - Given your answers to part (i), does cigs seem a good candidate for having a conditional Poisson distribution?
- Estimate a Poisson regression model for
*cigs*, including log(cigpric), log(income), white, educ, age, and as explanatory variables. What are the estimated price and income elasticities? - Using the maximum likelihood standard errors, are the price and income variables statistically significant at 5% level?
- Obtain the estimate of . What is ? (hint: “dispersion parameter” from R output) How should you adjust the standard errors from part (iv)?
- Using the adjusted standard errors from part (v), are the price and income elasticities now statistically different from zero? Explain.
- Are the education and age variables significant using the more robust standard errors? How do you interpret the coefficient on
*educ*?

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