Parallel lines with location

In real estate, a common mantra is that the three most important factors in determining the price of a property are "location, location, and location." If location drives up property values and rents, then we might imagine that location would increase a restaurant's costs, which would result in them having higher prices. In many parts of New York, the east side (east of 5th Avenue) is more developed and perhaps more expensive. [This is increasingly less true, but was more true at the time these data were collected.]

Let's expand our model into a parallel slopes model by including the East variable in addition to Food.

Use lm() to fit a parallel slopes model for Price as a function of Food and East. Interpret the coefficients and the fit of the model. Can you explain the meaning of the coefficient on East in simple terms? Did the coefficient on Food change from the previous model? If so, why? Did it change by a lot or just a little?

Identify the statement that is FALSE:

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Each additional rating point of food quality is associated with a $2.88 increase in the expected price of meal, after controlling for location.
The premium for an Italian restaurant in NYC associated with being on the east side of 5th Avenue is $1.46, after controlling for the quality of the food.
The change in the coefficient of food from $2.94 in the simple linear model to $2.88 in this model has profound practical implications for restaurant owners.

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Parallel lines with location

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