1. Suppose that, for a certain population, we can predict log earnings from log height as...

 

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1. Suppose that, for a certain population, we can predict log earnings from log height as follows: A person who is 66 inches tall is predicted to have earnings of $30,000. Every increase of 1% in height corresponds to a predicted increase of 0.8% in earnings. The earnings of approximately 95% of people fall within a factor of 1.1 of predicted values. Give the equation of the regression line. 2. An economist runs a regression examining the relations between the average price of cigarettes, P, and the quantity purchased, Q, across a large sample of counties in the U.S., assuming the following model: ln Q = a + ln P Suppose the estimate for is -0.3. Interpret this coefficient. 1. Suppose that, for a certain population, we can predict log earnings from log height as follows: A person who is 66 inches tall is predicted to have earnings of $30,000. Every increase of 1% in height corresponds to a predicted increase of 0.8% in earnings. The earnings of approximately 95% of people fall within a factor of 1.1 of predicted values. Give the equation of the regression line. 2. An economist runs a regression examining the relations between the average price of cigarettes, P, and the quantity purchased, Q, across a large sample of counties in the U.S., assuming the following model: ln Q = a + ln P Suppose the estimate for is -0.3. Interpret this coefficient.

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