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			<gallery>
			File:Correlation_examples2.svg\|Scatter plots showing different types of correlation
			File:Pearson_Correlation_Coefficient_and_associated_scatterplots.png\|Pearson Correlation Coefficient and associated scatterplots
			File:correlation_range_dependence.svg\|Correlation range dependence
			File:Anscombe's_quartet_3.svg\|Anscombe's quartet
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Prab: CSV import

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'''Correlation''' is a statistical measure that describes the degree to which two variables move in relation to each other. In the field of [[statistics]], correlation is used to measure the strength and direction of the relationship between two variables.

== Definition ==

Correlation is defined as the statistical relationship between two or more variables that change together, though not necessarily at the same rate. The correlation coefficient is a measure of the strength of this relationship. It is represented by the letter 'r' and can range from -1 to 1.

== Types of Correlation ==

There are three types of correlation: positive, negative, and zero correlation.

* '''Positive Correlation''': When the values of both variables increase or decrease together, the correlation is said to be positive.
* '''Negative Correlation''': When the value of one variable increases while the other decreases, the correlation is said to be negative.
* '''Zero Correlation''': When there is no relationship between the variables, the correlation is said to be zero.

== Correlation vs Causation ==

It is important to note that correlation does not imply [[causation]]. Just because two variables correlate does not mean that one causes the other to occur. They may be related due to a third, unseen factor, known as a confounding variable.

== Correlation in Medical Research ==

In [[medical research]], correlation is used to determine if a relationship exists between two health-related variables, for example, smoking and lung cancer. This can help identify risk factors for diseases and guide public health interventions.

== Calculating Correlation ==

The correlation between two variables can be calculated using the Pearson correlation coefficient formula. This formula takes into account the standard deviation and mean of both variables.

== Limitations of Correlation ==

While correlation can indicate a possible relationship between two variables, it does not prove a cause-and-effect relationship. Correlation also cannot measure non-linear relationships, only linear ones.

== See Also ==

* [[Regression analysis]]
* [[Pearson correlation coefficient]]
* [[Spearman's rank correlation coefficient]]

[[Category:Statistics]]
[[Category:Medical research]]
[[Category:Epidemiology]]

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Correlation - Revision history

Prab: CSV import

Prab: CSV import