In a simple regression Y = b0 + b1X where Y = number of robberies in a city (thousands of robberies),X = size of the police force in a city (thousands of police),and n = 45 randomly chosen large U.S.cities in 2008,we would be least likely to see which problem?
A)Autocorrelated residuals (because this is time-series data)
B)Heteroscedastic residuals (because we are using totals uncorrected for city size)
C)Nonnormal residuals (because a few larger cities may skew the residuals)
D)High leverage for some observations (because some cities may be huge)

Question

Quizplus · Accepted Answer

Autocorrelated residuals would be least likely to be a problem in this case because the data is cross-sectional (not time-series), so there is no temporal order to the observations. Heteroscedastic residuals could be a problem because the number of robberies and size of police force could vary more in larger cities, but the question specifically states that the data is uncorrected for city size, making this less of a concern. Nonnormal residuals could be a problem if a few larger cities have particularly high or low numbers of robberies or police force, causing the residuals to be skewed, but this is not a guaranteed issue. High leverage for some observations could be a problem if there are a few extremely large or small cities, which could disproportionately influence the regression line, making this a more likely issue.

In a Simple Regression Y = B0 + B1X Where