Why should an appraiser plot the data before running a regression?
Correct Answer
C) A plot reveals curvature, clusters and outliers
Plots can reveal curvature, clusters, gaps and unusual observations that summary statistics may obscure. Those patterns require investigation rather than automatically proving separate markets or data errors. Plotting does not itself compute a more accurate coefficient, replace fact verification or constitute a universal prerequisite for every model fit.
Why This Is the Correct Answer
Option C is correct because a plot reveals curvature, clusters and outliers that summary statistics may obscure. Those patterns can show that a straight-line model is unsuitable or that some sales need investigation. Exploratory analysis helps reveal the shape and unusual features of the data before modeling.
Why the Other Options Are Wrong
Option A: Plotting is required before any model may be fitted
Option A states that plotting is required before any model may be fitted, which overstates its role. Plotting is a strongly recommended exploratory step, not a universal prerequisite for fitting a model.
Option B: A plot computes the coefficients more accurately
Option B claims a plot computes the coefficients more accurately, but a plot does not calculate anything. Coefficients come from the regression calculation; the plot only helps judge whether the model form fits the data.
Option D: Plotting eliminates the need to verify the sales
Option D claims plotting eliminates the need to verify the sales, which is wrong. A plot can flag questionable sales, but the appraiser still needs to verify transaction facts.
Look before you fit
A plot shows bends, bunches and strays before you fit a line. It informs the model but does not replace the math or the verification.
How to use: Use this to pick the choice about revealing patterns rather than computing or verifying.
Exam Tip
Be wary of choices that give plotting powers it lacks, such as computing coefficients or replacing verification.
Common Mistakes to Avoid
- -Believing plots improve coefficient accuracy directly
- -Treating a plotted outlier as proof of a data error without investigation
- -Skipping sale verification because the plot looks clean
Concept Deep Dive
Analysis
This question tests the purpose of exploratory plotting before regression. Plots reveal patterns such as curvature, clusters, gaps and unusual observations that summary statistics can hide; they do not compute coefficients or replace verification.
Background Knowledge
Exploratory data analysis, including scatter plots, helps reveal the shape of the data and unusual features before formal modeling. Curvature may call for a different model form, and clusters can suggest submarkets. Patterns found in a plot require investigation rather than automatically proving data errors or separate markets.
Real-World Application
Before running a regression of price on living area, an appraiser plots the sales and notices that total sale prices rise more slowly for the largest homes, so the points bend away from a straight line. This curvature prompts the appraiser to consider a different model form and to check whether those large-home sales form a separate segment.
More Statistics Questions
Sale prices are $270,000, $290,000, $300,000, $310,000, $330,000. Treat the listed observations as the complete population and divide by n when computing the standard deviation. Report to the nearest dollar.
A sales sampling frame systematically excludes a relevant part of the target market. What is the principal risk?
An appraiser estimates a market-wide price trend using only sales data from one brokerage that specializes in luxury homes, without checking how other segments are represented. What is the principal concern?
Compared with analyzing one matched sale pair for one feature, what can a suitably specified multiple regression do?
The base-period median sale price is $400,000 and the current-period median is $460,000. Compute the price relative defined as current ÷ base, as a decimal to three places.
The assessment-to-sale ratios are 0.8, 0.85, 0.9, 0.92, 0.95, 0.98, 1.02, 1.05, 1.08, 1.1, 1.15, 1.2. Calculate COD as [average absolute deviation from the median ÷ median]×100. For even n, average the two middle ratios. Report the COD as a percentage to two decimals.
Assessment-to-sale ratios are 0.94, 0.96, 0.98, 1, 1.01. Compute the arithmetic mean. Report as a decimal ratio to three places.
Market data show a first quartile sale price of $405,000 and a third quartile sale price of $495,000. What is the interquartile range, to the nearest dollar?
Assessment-to-sale ratios are 0.96, 0.98, 0.99, 1.01. Compute the arithmetic mean. Report as a decimal ratio to three places.
Given first quartile $400,000, median $440,000 and third quartile $500,000, compute the trimean (Q1+2×median+Q3)/4. Report to the nearest dollar.
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