# solution

Public corruption and bad weather. Refer to the Journal of Law and Economics (November 2008) study of the link between Federal Emergency Management Agency (FEMA) disaster relief and public corruption, Exercise 11.24 (p. 656). You used the data in the file to fit a straight-line model relating a stateâ€™s average annual number of public corruption convictions (y) to the stateâ€™s average annual FEMA relief (x).

a. Estimate s2 , the variance of the random error term in the model.

b. Estimate s, the standard deviation of the random error term in the model.

c. Which of the estimates, part a or b, can be interpreted practically? Why?

d. Make a statement about how accurate the model is in predicting a stateâ€™s average annual number of public corruption convictions.

Exercise 11.24

Public corruption and bad weather. The Federal Emergency Management Agency (FEMA) provides disaster relief for states impacted by natural disasters (e.g., hurricanes, tornados, floods). Do these bad weather windfalls lead to public corruption? This was the research question of interest in an article published in the Journal of Law and Economics (November 2008). Data on y = average annual number of public corruption convictions (per 100,000 residents) and x = average annual FEMA relief (in dollars) per capita for each of the 50 states were used in the investigation.

a. Access the data, saved in the file, and construct a scatterplot. Do you observe a trend?

b. Fit the simple linear regression model, E() = 0 Â 1, to the data and obtain estimates of the y-intercept and slope.

c. Practically interpret the estimated y-intercept and estimated slope.

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