A fitted fireplace coefficient is negative while independent credible market evidence suggests a positive contribution for comparable properties. What is the most appropriate next step?
Correct Answer
B) Investigate the data, specification and comparability of the evidence
A coefficient contrary to credible market evidence calls for investigating coding, sample composition, confounding, correlated predictors and model specification. The sign alone does not identify the cause or prove the model wrong; conditional effects may differ from unadjusted associations. Changing the sign solely to match an expectation would not be validation.
Why This Is the Correct Answer
Option B is correct because a coefficient that contradicts credible independent evidence calls for investigating coding errors, sample composition, confounding, correlated predictors and model specification, as well as whether the other evidence is truly comparable. The sign alone does not identify the cause. Model validation is a central step in model building, so investigation comes before any conclusion.
Why the Other Options Are Wrong
Option A: Conclude that fireplaces reduce value throughout the market
Option A concludes that fireplaces reduce value throughout the market, which overstates what one fitted coefficient can show. A conditional coefficient may reflect confounding, coding problems or correlated predictors, and it conflicts with credible evidence of a positive contribution.
Option C: Conclude the response variable was definitely entered incorrectly
Option C asserts that the response variable was definitely entered incorrectly, but a data-entry error is only one of several possible causes. Specification problems, sample composition or correlated predictors could also produce the unexpected sign, so certainty is not justified without investigation.
Option D: Reverse the coefficient sign so that it matches the expected contribution
Option D reverses the coefficient sign to match the expected contribution, which manipulates the result instead of validating it. Changing a sign solely to fit an expectation is not supportable analysis and would misrepresent what the model estimated.
Wrong Sign Means Investigate
Treat an unexpected coefficient sign like a check-engine light: it tells you to look under the hood, not what is broken.
How to use: Use this image when a question offers to accept, flip or blame a single cause for a surprising regression result.
Exam Tip
When a statistical result conflicts with credible market evidence, look for the answer that investigates data and specification rather than one that accepts or alters the result.
Common Mistakes to Avoid
- -Treating a regression coefficient as conclusive proof of market behavior
- -Editing a coefficient so it matches expectations
- -Assuming a single data-entry error is the only possible explanation
Concept Deep Dive
Analysis
This question tests how to respond when a regression coefficient conflicts with credible market evidence. The right reaction is diagnostic: a surprising sign is a signal to examine data coding, sample composition, correlated predictors and model specification. It is not proof of a market fact, and it is not something to be edited away.
Background Knowledge
Regression coefficients measure conditional associations given the other predictors, so they can differ from simple, unadjusted relationships. Unexpected signs often arise from multicollinearity, omitted variables, coding errors or unrepresentative samples, which is why model validation is essential before relying on any estimate.
Real-World Application
An appraiser's regression on 150 local sales shows a negative fireplace coefficient, yet paired sales and broker interviews suggest buyers pay more for fireplaces. Investigating, the appraiser finds that fireplaces are concentrated in older homes and that age is poorly specified in the model.
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.
People Also Study
Real Estate Market
13.6% of exam
Property Description
11.8% of exam
Land or Site Valuation
4.5% of exam
Sales Comparison Approach
16.4% of exam
Cost Approach
13.6% of exam
Previous Question
The assessment-to-sale ratios are 0.88, 0.92, 0.96, 0.98, 1.02, 1.04, 1.08, 1.12. 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.
Next Question
Multicollinearity in a regression model occurs when: