Key points:
* Correlation does not imply causation: Just because two variables are correlated doesn't mean one causes the other. There may be other factors at play.
* Strength and direction: Correlation is measured by a coefficient, which ranges from -1 to +1.
* A coefficient of +1 indicates a perfect positive correlation (variables increase together).
* A coefficient of -1 indicates a perfect negative correlation (variables decrease together).
* A coefficient of 0 indicates no correlation.
* Types of correlation:
* Positive correlation: As one variable increases, the other also increases.
* Negative correlation: As one variable increases, the other decreases.
* No correlation: No relationship between the variables.
Examples:
* Positive correlation: The more hours a student studies, the higher their grades tend to be.
* Negative correlation: The more hours a person works, the less free time they have.
* No correlation: There is no relationship between the number of people wearing hats and the temperature outside.
In summary, correlation is a statistical measure that describes the relationship between variables, indicating the extent to which they change together. It does not necessarily imply causation.