Public corruption and bad weather. Refer to the Journal of Law and Economics (Nov. 2008) study of the link between Federal Emergency Management Agency (FEMA) disaster relief and public corruption, Exercise 11.58 (p. 670). You determined that the p-value for testing the adequacy of the straight-line model relating a state’s average annual number of public corruption convictions (y) to the state’s average annual FEMA relief (x) was p = .102. Based on this result, would you recommend using the model to predict the number of public corruption convictions in a state with an annual FEMA relief of x = 5 thousand dollars? Explain.
Exercise 11.58
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). Evaluate the overall adequacy of the straight-line model relating a state’s average annual number of public corruption convictions (y) to the state’s average annual FEMA relief (x). Use 
 = .01.
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 
 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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