Two numerical variables exhibit a strong Pearson correlation in the observed sample. What may be concluded from that fact alone?
Correct Answer
B) They show a strong observed linear association, not causation
Strong Pearson correlation indicates a strong linear association in the observed sample, possibly positive or negative. It does not identify a causal mechanism, show that both variables belong in a model or guarantee persistence. A third influence, sample selection or chance may contribute; assess the evidence and model applicability.
Why This Is the Correct Answer
Option B is correct because a strong Pearson correlation indicates a strong observed linear association, which may be positive or negative, without identifying a causal mechanism. A third influence, sample selection or chance may contribute to the pattern. Correlation implies association, but not causation.
Why the Other Options Are Wrong
Option A: One of the two variables causes the other one
Option A claims one variable causes the other, but correlation alone cannot establish causation. A third factor, sample selection or chance could produce the same association.
Option C: Both variables belong in any pricing model that is built
Option C states both variables belong in any pricing model, but correlation does not establish that a variable is appropriate for a model. Model specification depends on market reasoning, redundancy and diagnostics, not correlation alone.
Option D: The relationship must persist in all future time periods
Option D says the relationship must persist in all future periods, which correlation cannot guarantee. Observed sample relationships can change as market conditions change.
Together is not because
Variables that move together are associated; that does not mean one moves because of the other.
How to use: Use this whenever a question asks what a correlation coefficient alone proves.
Exam Tip
Pick the answer limited to association in the observed data; answers claiming cause, required model inclusion or permanence go beyond what correlation shows.
Common Mistakes to Avoid
- -Concluding that a strong correlation proves causation
- -Assuming a correlated variable automatically belongs in a model
- -Expecting an observed correlation to hold in all future periods
Concept Deep Dive
Analysis
This question tests the classic distinction between correlation and causation. A strong Pearson correlation shows that two variables move together in a linear pattern in the observed sample, either positively or negatively. It does not by itself reveal why, establish model inclusion, or guarantee the pattern will continue.
Background Knowledge
Pearson's correlation coefficient measures the strength and direction of a linear relationship between two numerical variables, ranging from −1 to +1. It describes association in the sample, not cause and effect. Appraisers use it as one diagnostic tool while relying on market reasoning to interpret relationships.
Real-World Application
An appraiser notices that pool ownership and sale price are strongly correlated in a sample. Before attributing a value contribution to pools, the appraiser considers that larger, newer homes on bigger lots are more likely to have pools, which could explain much of the association.
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