Spurious Correlation
Spurious Correlation are correlations that are due mostly to the influences of other variables. Although you cannot prove causal relations based on correlation coefficients you can still identify so-called spurious correlations
For example, there is a correlation between the total number of losses in a fire and the number of firemen that were putting out the fire. However, this correlation does not indicate that if you call fewer firemen, then you would lower the losses. A third variable,the initial size of the fire, influences both the number of losses and the number of firemen. If you control for this variable (for instance, consider only fires of a fixed size), then the correlation will either disappear or perhaps even change its sign.
The main problem with spurious correlations is that we typically do not know what the hidden agent is. However, in cases where we know where to look, we can use partial correlations that control for (or partial out) the influence of specified variables. In Statistica, partial correlation procedures are included as part of the Multiple Regression module.