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6. The following regression equation examines the relation between hotel prices (in dollars per night) and the number of short-term rental listings (e.g., Airbnb) in the city. hoteTprce = 150-0018 × ST-rentals
Suppose that a hotel in a city with 1,500 short term listings costs $110 dollars. What is the error in the predicted hotel price? e) f) Can you conclude from this regression that short-term rentals cause a erm rentals cause a decrease in hotel prices? Explain your answer. Next, you regress the natural log of hotel prices on the natural log of the number of short- term rentals and obtain the regression equation shown below: In(hotel price5.2-0.046 x In(ST rentals) g) Interpret the coefficient on short-term rentals in a sentence. h) What is the predicted hotel price (in dollars per night) in a city with 1,000 short-term listings?
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6. The regression is given as widehat{hotelprice} = 150 - 0.018*widehat{STrentals} .

(e) The predicted hotel price for 1500 short term listings would be widehat{hotelprice} = 150 - 0.018*1500 = 123 . The error in predicted hotel price would be hot elprice-hotel price = 110-123=-13 (dollars).

(f) Assuming the inferential statistics are significant, we may conclude that short term rentals causes a deacrease in hotel prices on average. As the sign on coefficient in widehat{hotelprice} = 150 - 0.018*widehat{STrentals} is negative, it implies there is a negative association between hotelprice and STrenals, meaning that if STrental increases, hotelprice would decrease. The coefficient is the amount by which hotelprice would decrease on average for a unit increase in STrentals.

The new regression is In(hot elprice) = 5.2-0.046* ln(STrentals) .

(g) The coefficient is the elasticity of hotelprice with respect to STrentals, which is the percentage change in hotelprice on average for an increase in unit percent of STrentals.

We can see it as d(In (hotelprice] = d52-0046 * d|ln(STrentals or rac{mathrm{d} (widehat{hotelprice})}{widehat{hotelprice}} = - 0.046rac{mathrm{d} (widehat{STrentals})}{widehat{STrentals}} or rac{mathrm{d} (widehat{hotelprice}) / widehat{hotelprice}}{mathrm{d} (widehat{STrentals}) / widehat{STrentals}} = - 0.046 , which is the elasticity of hotelprice with respect to STrentals.

(h) For 1000 STrentals, we have ln(widehat{hotelprice}) = 5.2 - 0.046*ln(1000) or ln(widehat{hotelprice}) = 4.882243257 or widehat{hotelprice} = antilog(4.882243257) = e^{4.882243257} or widehat{hotelprice} = 131.926276738 or $131.93 (approx) on average.

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    6. The following regression equation examines the relation between hotel prices (in dollars per night) and the number of short-term rental listings (e.g., Airbnb) in the city hotel price 150 0.018 x ST rentals a) Interpret the coefficient on the number of short-term rentals. b) How would these coefficients change if the number of rentals was expressed in thousands? c) What is the predicted hotel price in a city with 1,000 short-term listings? d) How many listings would there need...

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