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Activity I (Chapter 9

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Activity I (Chapter 9. Prediction for a Dichotomous Variable) - Suppose Y is a dichotomous dependent variable, and the data-generating process for Y can be expressed as:

Yi=0.1+0.03X1i−0.02X2i+Ui

Interpret the coefficients on X1 and X2

Activity II (Chapter 9. Prediction for a Dichotomous Variable) - Suppose you are trying to learn the relationship between the price you charge for your product and the likelihood of purchase by individuals offered that price. You offer your product online and for one month have randomly posted prices between $10 and $30. Using data on purchases and prices, you get the following estimates for a linear probability model:

Purchase=1.7−0.06×Pricei

You are interested in the effect of a $20 price increase (i.e., moving from the lowest price to the highest) on the likelihood of Purchase. Why is answering this question problematic using this model?

Activity III (Chapter 10. Identification and Data Assessment) - Your firm is attempting to learn the effectiveness of a newly developed television ad on its sales. To do this, it has randomly run the ad between 0 and 5 times during one week across a large number of television markets in the United States. It then recorded product sales for the following month for each market. To conduct the analysis, analysts at the firm have assumed the following data-generating process:

Salesi=α+βAdsi+Ui

Regressing Sales on Ads yields βˆ=350. The firm would like to use this number to project the change in Sales when increasing weekly television ads to 20.

a. According to these results, what is the expected change in Sales when Ads increase from 5 to 20?

b. Why should we be skeptical of our result from Part a?

c. What can you do to find an estimate of the effect of increasing Ads from 5 to 20 that is more credible?