What is the practical limit on how far statistical conclusions may be applied?
Correct Answer
C) To the population the sample was drawn from
Why this is correct: The practical limit is that statistical conclusions may be applied only to the population the sample was drawn from. You cannot generalize findings beyond the specific market segment or geographic area represented by your sample data. Why the other choices are wrong: Conclusions do not automatically apply to any property within the same state. They do not apply to any property of the same age or newer unless that was the sampled population. They cannot reliably predict future sales after the study. Exam tip: Define your population (e.g., '3-bedroom homes in Neighborhood X built after 1990') before sampling. Your conclusions are only valid for that defined group.
Why This Is the Correct Answer
Why this is correct: The practical limit is that statistical conclusions may be applied only to the population the sample was drawn from. You cannot generalize findings beyond the specific market segment or geographic area represented by your sample data. Why the other choices are wrong: Conclusions do not automatically apply to any property within the same state. They do not apply to any property of the same age or newer unless that was the sampled population. They cannot reliably predict future sales after the study. Exam tip: Define your population (e.g., '3-bedroom homes in Neighborhood X built after 1990') before sampling. Your conclusions are only valid for that defined group.
More appraisal-statistical-methods Questions
A price index rises from 100 to 121 over two years. What compound annual rate does this represent?
A sample of four sales drawn from a market with 200 annual transactions is:
A set of comparable sales has a mean of $250,000 and a standard deviation of $20,000. What is the coefficient of variation?
A property sold for $400,000 and resold three years later for $463,050 with no physical change. What compound annual rate does this indicate?
An appraiser includes both 'total room count' and 'bedroom count' as independent variables in a regression model estimating single-family home sale prices. The variance inflation factor (VIF) for 'bedroom count' is calculated as 12.3. What is the most appropriate appraisal action based on this result?
An appraiser runs a regression of sale price on GLA, age, and a binary variable for 'renovated' (1 = yes, 0 = no). The estimated coefficient for 'renovated' is $18,400 with a standard error of $6,200 and a t-statistic of 2.97. Assuming a two-tailed test at Ξ± = 0.05 and 42 degrees of freedom, what conclusion is supported regarding the market's recognition of renovations?
To validate the functional form of a regression model used for adjustments, an appraiser plots residuals against predicted values and observes a clear inverted-U pattern. What does this pattern indicate, and what is the most defensible corrective action?
A histogram of neighborhood sale prices shows two distinct peaks. What does this most likely mean?
What does it mean to validate a regression model?
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