In a Simple Regression Y = B₀ + B₁X Where

question 13

Multiple Choice

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)

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