(e) Under the assumptions of the SLR model, what proportion of the sample variation in Inprice is "explained" by variation in Indistance? (f) Do you think a causal interpretation of B, is appropriate in this example? In other words, do you think the assumptions of the SLR model are plausible in this application? In particular, think about the city's decision on where to put the incinerator. (c) is the sign of the estimate for B1 what you would expect it to be (based on intuition, experience, and/or basic economic theory)? (d) Using the information given, calculate the sample correlation coefficient between Inprice and Indistance. The details of the underlying calculations can be found in our simple regression tutorial. Be as specific as you can about what it measures (i.e., the units) and assume all of the SLR model assumptions hold. This tutorial shows how to fit a simple regression model (that is, a linear regression with a single independent variable) using Stata. + Bi Indistance + u The estimated (sample) regression equation and related statistics are given by: Inprice = 9.40 + 0.312 Indistance n = 135, R2 = 0.162 (a) What does the "zero conditional mean" assumption of the SLR model imply in this context? (b) Interpret the coefficient on Indistance (the estimate for B1). A simple linear regression regression (SLR) model relating the log of housing prices (Inprice) to the log distance from a recently built garbage incinerator (Indistance) is estimated using OLS and cross-sectional data from a sample houses sold in the year after the incinerator was built. For this problem, you need to interpret simple linear regression estimates you do not need to use Stata or the underlying data.
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