A regression fitted to homes between 1,200 and 2,400 square feet is used to value a 4,000 square foot house. What is the concern?
Correct Answer
D) The relationship may not hold beyond the data
Why this is correct: Using a regression model fitted to data from 1,200-2,400 sq ft to value a 4,000 sq ft house is extrapolation. The linear relationship (price per sq ft) observed within the data range may not hold outside it. For larger homes, the price per sq ft often decreases, so extrapolation risks significant overvaluation. Why the other choices are wrong: "The model has too few independent variables in it" is wrong; the core issue is extrapolation, not the number of variables. "R-squared cannot be computed outside the sample" is wrong; R-squared is a measure of fit for the model on the sample data, not a barrier to computation. "The subject must be added to the data set first" is wrong; you cannot add the subject's value to the data set before estimating it. Exam tip: Extrapolation (predicting outside the data range) is risky and often invalid. Stick to the range of your comparable data.
Why This Is the Correct Answer
Why this is correct: Using a regression model fitted to data from 1,200-2,400 sq ft to value a 4,000 sq ft house is extrapolation. The linear relationship (price per sq ft) observed within the data range may not hold outside it. For larger homes, the price per sq ft often decreases, so extrapolation risks significant overvaluation. Why the other choices are wrong: "The model has too few independent variables in it" is wrong; the core issue is extrapolation, not the number of variables. "R-squared cannot be computed outside the sample" is wrong; R-squared is a measure of fit for the model on the sample data, not a barrier to computation. "The subject must be added to the data set first" is wrong; you cannot add the subject's value to the data set before estimating it. Exam tip: Extrapolation (predicting outside the data range) is risky and often invalid. Stick to the range of your comparable data.
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