Why should an appraiser plot the data before running a regression?
Correct Answer
C) A plot reveals curvature, clusters and outliers
Why this is correct: A scatter plot visually reveals the data's pattern. It can show non-linear relationships, outliers that distort the model, or clusters indicating sub-markets—issues not apparent from summary statistics alone. Why the other choices are wrong: "Plotting is required before any model may be fitted" is too absolute; it's a best practice, not a universal requirement. "A plot computes the coefficients more accurately" is false; regression calculates coefficients. "Plotting eliminates the need to verify the sales" is incorrect; verification remains essential. Exam tip: Always plot data first to check for linearity and outliers. It's a quick diagnostic step.
Why This Is the Correct Answer
Why this is correct: A scatter plot visually reveals the data's pattern. It can show non-linear relationships, outliers that distort the model, or clusters indicating sub-markets—issues not apparent from summary statistics alone. Why the other choices are wrong: "Plotting is required before any model may be fitted" is too absolute; it's a best practice, not a universal requirement. "A plot computes the coefficients more accurately" is false; regression calculates coefficients. "Plotting eliminates the need to verify the sales" is incorrect; verification remains essential. Exam tip: Always plot data first to check for linearity and outliers. It's a quick diagnostic step.
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 α = 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?
An appraiser includes months elapsed since each sale as a variable in a price model. What is this intended to capture?
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