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In surveys, we find data that is "correlated", meaning one factor may have not directly interfered with another factor, yet the more of the X factor we have, the more of the Y factor we are likely to have (positive correlation).
For example, if a survey says that teenage girls who do not eat dinner with their family are more likely to become pregnant before age 17, then there is a positive correlation between teen girls not eating dinner with family & teen pregnancy.
Not eating dinner with one's family will not causepregnancy, but there is a strong relationship between the two. <--This is a positive correlation
To sum it up, correlation does not equal causation.
Relation, on the other hand, is cause and effect (almost the opposite of correlation).
Relation is cause & effect.
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