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# Correlation and dependence

In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related.
Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a limited supply product and its price.

Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).

Formally, random variables are dependent if they do not satisfy a mathematical property ofprobabilistic independence. In informal parlance, correlation is synonymous with dependence. However, when used in a technical sense, correlation refers to any of several specific types of relationship betweenmean values. There are severalcorrelation coefficients, often denotedor, measuring the degree of correlation. The most common of these is thePearson correlation coefficient, which is sensitive only to a linear relationship between two variables (which may be present even when one variable is a nonlinear function of the other). Other correlation coefficients have been developed to be morerobustthan the Pearson correlation – that is, more sensitive to nonlinear relationships.[1][2][3]Mutual informationcan also be applied to measure dependence between two variables.

## Pearson's product-moment coefficient

### Definition

The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient, or "Pearson's correlation coefficient", commonly called simply "the correlation coefficient". It is obtained by dividing the covariance of the two variables by the product of their standard deviations. Karl Pearson developed the coefficient from a similar but slightly different idea by Francis Galton.[4]

The population correlation coefficientbetween tworandom variablesandwithexpected valuesandandstandard deviationsandis defined as
whereis theexpected valueoperator,meanscovariance, andis a widely used alternative notation for the correlation coefficient. The Pearson correlation is defined only if both standard deviations are finite and positive. An alternative formula purely in terms of moments is

### Symmetry property

The correlation coefficient is symmetric:. This is verified by the commutative property of multiplication.

### Correlation and independence

It is a corollary of theCauchy–Schwarz inequalitythat theabsolute valueof the Pearson correlation coefficient is not bigger than 1. The correlation coefficient is +1 in the case of a perfect direct (increasing) linear relationship (correlation), −1 in the case of a perfect decreasing (inverse) linear relationship (anticorrelation),[5] and some value in theopen intervalin all other cases, indicating the degree oflinear dependencebetween the variables. As it approaches zero there is less of a relationship (closer to uncorrelated). The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.

If the variables are independent, Pearson's correlation coefficient is 0, but the converse is not true because the correlation coefficient detects only linear dependencies between two variables.

For example, suppose the random variableis symmetrically distributed about zero, and. Thenis completely determined by, so thatandare perfectly dependent, but their correlation is zero; they areuncorrelated. However, in the special case whenandarejointly normal, uncorrelatedness is equivalent to independence.

### Sample correlation coefficient

Given a series ofmeasurements of the pairindexed by, the sample correlation coefficient can be used to estimate the population Pearson correlationbetweenand. The sample correlation coefficient is defined as
whereandare the samplemeansofand, andandare thecorrected sample standard deviationsofand.
Equivalent expressions forare
whereandare theuncorrected sample standard deviationsofand.
Ifandare results of measurements that contain measurement error, the realistic limits on the correlation coefficient are not −1 to +1 but a smaller range.[6] For the case of a linear model with a single independent variable, thecoefficient of determination (R squared)is the square of, Pearson's product-moment coefficient.

## Example

Consider the joint probability distribution ofandgiven in the table below.

For this joint distribution, the marginal distributions are:

This yields the following expectations and variances:

Therefore:

## Rank correlation coefficients

Rank correlation coefficients, such as Spearman's rank correlation coefficient and Kendall's rank correlation coefficient (τ) measure the extent to which, as one variable increases, the other variable tends to increase, without requiring that increase to be represented by a linear relationship. If, as the one variable increases, the other decreases, the rank correlation coefficients will be negative. It is common to regard these rank correlation coefficients as alternatives to Pearson's coefficient, used either to reduce the amount of calculation or to make the coefficient less sensitive to non-normality in distributions. However, this view has little mathematical basis, as rank correlation coefficients measure a different type of relationship than the Pearson product-moment correlation coefficient, and are best seen as measures of a different type of association, rather than as alternative measure of the population correlation coefficient.[7][8]

To illustrate the nature of rank correlation, and its difference from linear correlation, consider the following four pairs of numbers:
(0, 1), (10, 100), (101, 500), (102, 2000).
As we go from each pair to the next pairincreases, and so does. This relationship is perfect, in the sense that an increase inis always accompanied by an increase in. This means that we have a perfect rank correlation, and both Spearman's and Kendall's correlation coefficients are 1, whereas in this example Pearson product-moment correlation coefficient is 0.7544, indicating that the points are far from lying on a straight line. In the same way ifalways decreases whenincreases, the rank correlation coefficients will be −1, while the Pearson product-moment correlation coefficient may or may not be close to −1, depending on how close the points are to a straight line. Although in the extreme cases of perfect rank correlation the two coefficients are both equal (being both +1 or both −1), this is not generally the case, and so values of the two coefficients cannot meaningfully be compared.[7] For example, for the three pairs (1, 1) (2, 3) (3, 2) Spearman's coefficient is 1/2, while Kendall's coefficient is 1/3.

## Other measures of dependence among random variables

The information given by a correlation coefficient is not enough to define the dependence structure between random variables.[9] The correlation coefficient completely defines the dependence structure only in very particular cases, for example when the distribution is a multivariate normal distribution. (See diagram above.) In the case of elliptical distributions it characterizes the (hyper-)ellipses of equal density; however, it does not completely characterize the dependence structure (for example, a multivariate t-distribution's degrees of freedom determine the level of tail dependence).

Distance correlation[10][11] was introduced to address the deficiency of Pearson's correlation that it can be zero for dependent random variables; zero distance correlation implies independence.

The Randomized Dependence Coefficient[12] is a computationally efficient, copula-based measure of dependence between multivariate random variables. RDC is invariant with respect to non-linear scalings of random variables, is capable of discovering a wide range of functional association patterns and takes value zero at independence.

For two binary variables, theodds ratiomeasures their dependence, and takes range non-negative numbers, possibly infinity:. Related statistics such asYule's YandYule's Qnormalize this to the correlation-like range. The odds ratio is generalized by thelogistic modelto model cases where the dependent variables are discrete and there may be one or more independent variables.

The correlation ratio, entropy-based mutual information, total correlation, dual total correlation and polychoric correlation are all also capable of detecting more general dependencies, as is consideration of the copula between them, while the coefficient of determination generalizes the correlation coefficient to multiple regression.

## Sensitivity to the data distribution

The degree of dependence between variablesanddoes not depend on the scale on which the variables are expressed. That is, if we are analyzing the relationship betweenand, most correlation measures are unaffected by transformingto a + bX andto c + dY, where a, b, c, and d are constants (b and d being positive). This is true of some correlation statistics as well as their population analogues. Some correlation statistics, such as the rank correlation coefficient, are also invariant tomonotone transformationsof the marginal distributions ofand/or.
Most correlation measures are sensitive to the manner in whichandare sampled. Dependencies tend to be stronger if viewed over a wider range of values. Thus, if we consider the correlation coefficient between the heights of fathers and their sons over all adult males, and compare it to the same correlation coefficient calculated when the fathers are selected to be between 165 cm and 170 cm in height, the correlation will be weaker in the latter case. Several techniques have been developed that attempt to correct for range restriction in one or both variables, and are commonly used in meta-analysis; the most common are Thorndike's case II and case III equations.[13]

Various correlation measures in use may be undefined for certain joint distributions of X and Y. For example, the Pearson correlation coefficient is defined in terms of moments, and hence will be undefined if the moments are undefined. Measures of dependence based on quantiles are always defined. Sample-based statistics intended to estimate population measures of dependence may or may not have desirable statistical properties such as being unbiased, or asymptotically consistent, based on the spatial structure of the population from which the data were sampled.

Sensitivity to the data distribution can be used to an advantage. For example, scaled correlation is designed to use the sensitivity to the range in order to pick out correlations between fast components of time series.[14] By reducing the range of values in a controlled manner, the correlations on long time scale are filtered out and only the correlations on short time scales are revealed.

## Correlation matrices

The correlation matrix ofrandom variablesis thematrix whoseentry is. If the measures of correlation used are product-moment coefficients, the correlation matrix is the same as thecovariance matrixof thestandardized random variablesfor. This applies both to the matrix of population correlations (in which caseis the population standard deviation), and to the matrix of sample correlations (in which casedenotes the sample standard deviation). Consequently, each is necessarily apositive-semidefinite matrix. Moreover, the correlation matrix is strictlypositive definiteif no variable can have all its values exactly generated as a linear function of the values of the others.
The correlation matrix is symmetric because the correlation betweenandis the same as the correlation betweenand.

A correlation matrix appears, for example, in one formula for the coefficient of multiple determination, a measure of goodness of fit in multiple regression.

In statistical modelling, correlation matrices representing the relationships between variables are categorized into different correlation structures, which are distinguished by factors such as the number of parameters required to estimate them. For example, in an exchangeable correlation matrix, all pairs of variables are modelled as having the same correlation, so all non-diagonal elements of the matrix are equal to each other. On the other hand, an autoregressive matrix is often used when variables represent a time series, since correlations are likely to be greater when measurements are closer in time. Other examples include independent, unstructured, M-dependent, and Toeplitz.

## Uncorrelatedness and independence of stochastic processes

Similarly for two stochastic processesand: If they are independent, then they are uncorrelated.[15]:p. 151

## Common misconceptions

### Correlation and causality

The conventional dictum that "correlation does not imply causation" means that correlation cannot be used by itself to infer a causal relationship between the variables.[16] This dictum should not be taken to mean that correlations cannot indicate the potential existence of causal relations. However, the causes underlying the correlation, if any, may be indirect and unknown, and high correlations also overlap with identity relations (tautologies), where no causal process exists. Consequently, a correlation between two variables is not a sufficient condition to establish a causal relationship (in either direction).

A correlation between age and height in children is fairly causally transparent, but a correlation between mood and health in people is less so. Does improved mood lead to improved health, or does good health lead to good mood, or both? Or does some other factor underlie both? In other words, a correlation can be taken as evidence for a possible causal relationship, but cannot indicate what the causal relationship, if any, might be.

### Correlation and linearity

The Pearson correlation coefficient indicates the strength of a linear relationship between two variables, but its value generally does not completely characterize their relationship.[17] In particular, if theconditional meanofgiven, denoted, is not linear in, the correlation coefficient will not fully determine the form of.
The adjacent image showsscatter plotsofAnscombe's quartet, a set of four different pairs of variables created byFrancis Anscombe.[18] The fourvariables have the same mean (7.5), variance (4.12), correlation (0.816) and regression line (y = 3 + 0.5x). However, as can be seen on the plots, the distribution of the variables is very different. The first one (top left) seems to be distributed normally, and corresponds to what one would expect when considering two variables correlated and following the assumption of normality. The second one (top right) is not distributed normally; while an obvious relationship between the two variables can be observed, it is not linear. In this case the Pearson correlation coefficient does not indicate that there is an exact functional relationship: only the extent to which that relationship can be approximated by a linear relationship. In the third case (bottom left), the linear relationship is perfect, except for oneoutlierwhich exerts enough influence to lower the correlation coefficient from 1 to 0.816. Finally, the fourth example (bottom right) shows another example when one outlier is enough to produce a high correlation coefficient, even though the relationship between the two variables is not linear.

These examples indicate that the correlation coefficient, as a summary statistic, cannot replace visual examination of the data. The examples are sometimes said to demonstrate that the Pearson correlation assumes that the data follow a normal distribution, but this is not correct.[4]

## Bivariate normal distribution

If a pairof random variables follows abivariate normal distribution, the conditional meanis a linear function of, and the conditional meanis a linear function of. The correlation coefficientbetweenand, along with themarginalmeans and variances ofand, determines this linear relationship:
whereandare the expected values ofand, respectively, andandare the standard deviations ofand, respectively.

• Autocorrelation

• Canonical correlation

• Coefficient of determination

• Cointegration

• Concordance correlation coefficient

• Cophenetic correlation

• Correlation function

• Correlation gap

• Covariance

• Covariance and correlation

• Cross-correlation

• Ecological correlation

• Fraction of variance unexplained

• Genetic correlation

• Goodman and Kruskal's lambda

• Illusory correlation

• Interclass correlation

• Intraclass correlation

• Lift (data mining)

• Mean dependence

• Modifiable areal unit problem

• Multiple correlation

• Point-biserial correlation coefficient

• Spurious correlation

• Statistical arbitrage

• Subindependence

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