In statistics, a **spurious relationship** (or, sometimes, **spurious correlation** or **spurious regression**) is a mathematical relationship in which two events or variables have no direct causal connection, yet it may be wrongly inferred that they do, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "confounding factor" or "lurking variable"). Suppose there is found to be a correlation between A and B. Aside from coincidence, there are three possible relationships:

- A causes B,
- B causes A,
**OR**- C causes both A and B.

In the last case there is a spurious correlation between A and B. In a regression model where A is regressed on B but C is actually the true causal factor for A, this misleading choice of independent variable (B instead of C) is called specification error.

Because correlation can arise from the presence of a lurking variable rather than from direct causation, it is often said that "Correlation does not imply causation".

Read more about Spurious Relationship: General Example, Detecting Spurious Relationships

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**spurious**... Typically a linear

**relationship**such as is postulated, in which is the dependent variable (hypothesized to be the caused variable), for j=1...k is the jth ... in the regression, so that effect will not be picked up as a

**spurious**effect of the potentially causative variable of interest ...

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