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

The given data is not time-series data, so we would not expect to see autocorrelated residuals. The other potential problems mentioned could occur in this type of data.

In a Simple Regression Y = B0 + B1X Where

In a Simple Regression Y = B₀ + B₁X Where