HEY In a simple regression Y= bo + bX where Y= number of robberies in a city (th

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HEY

In a simple regression Y= bo + bX 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 problen? Autocarrlstediuals betause ths s tine seris data) Nonnormal residuals (because a few larger cities may skew the residuals) Heteroscedastic residuals (because we are using totals uncorrected for city size O Some observations flagged as influential points (because some cities may be huge)

Explanation / Answer

Ans:

we will be less likely to see Autocorrelated residuals, because it is not time series data.

(first option is correct)

Rest of the charcterstics may occur i.e nonnormality,heteroscendic residuals and influential points due to data structure.

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