6. The regression is given as
.
(e) The predicted hotel price for 1500 short
term listings would be
. The error in predicted hotel price would be
(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
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
.
(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
or
or
, which is the elasticity of hotelprice with respect to
STrentals.
(h) For 1000 STrentals, we have
or
or
or
or $131.93 (approx) on average.
6. The following regression equation examines the relation between hotel prices (in dollars per night) and...
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...