Using data from 1988 for houses sold in Andover, Massachusetts, from Kiel and McClain (1995), the following equation relates housing price (price) to the distance from a recently built garbage incinerator (dist): log1price2 5 9.40 1 0.312 log1dist2 n 5 135, R2 5 0.162. (i) Interpret the coefficient
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Using data from 1988 for houses sold in Andover, Massachusetts, from Kiel and McClain (1995),
the following equation relates housing price (price) to the distance from a recently built garbage incinerator (dist):
log1price2 5 9.40 1 0.312 log1dist2
n 5 135, R2 5 0.162.
(i) Interpret the coefficient on log(dist). Is the sign of this estimate what you expect it to be?
(ii) Do you think simple regression provides an unbiased estimator of the ceteris paribus
elasticity of price with respect to dist? (Think about the city’s decision on where to put
the incinerator.)
(iii) What other factors about a house affect its price? Might these be
the incinerator?
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