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 in common usage it most often refers to how close two variables are to having a linear relationship with each other. 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 of probabilistic 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 between mean values.^{[clarification needed]} There are several correlation coefficients, often denoted or , measuring the degree of correlation. The most common of these is the Pearson 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 more robust than the Pearson correlation – that is, more sensitive to nonlinear relationships.^{[1]}^{[2]}^{[3]} Mutual information can also be applied to measure dependence between two variables.
Contents
 1 Pearson's productmoment coefficient
 2 Example
 3 Rank correlation coefficients
 4 Other measures of dependence among random variables
 5 Sensitivity to the data distribution
 6 Correlation matrices
 7 Uncorrelatedness and independence of stochastic processes
 8 Common misconceptions
 9 Bivariate normal distribution
 10 See also
 11 References
 12 Further reading
 13 External links
Pearson's productmoment coefficient[edit]
Definition[edit]
The most familiar measure of dependence between two quantities is the Pearson productmoment 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 coefficient between two random variables and with expected values and and standard deviations and is defined as

where is the expected value operator, means covariance, and is a widely used alternative notation for the correlation coefficient. The Pearson correlation is defined only if both of the standard deviations are finite and nonzero.
Symmetry property[edit]
The correlation coefficient is symmetric: .
Correlation and independence[edit]
It is a corollary of the Cauchy–Schwarz inequality that the correlation cannot exceed 1 in absolute value. The Pearson correlation 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 the open interval in all other cases, indicating the degree of linear dependence between 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 variable is symmetrically distributed about zero, and . Then is completely determined by , so that and are perfectly dependent, but their correlation is zero; they are uncorrelated. However, in the special case when and are jointly normal, uncorrelatedness is equivalent to independence.
Sample correlation coefficient[edit]
If we have a series of measurements of and written as and for , then the sample correlation coefficient can be used to estimate the population Pearson correlation between and . The sample correlation coefficient is written as
where and are the sample means of and , and and are the corrected sample standard deviations of and .
Equivalent expressions for are
where and are the uncorrected sample standard deviations of and .
If and are 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, the coefficient of determination (R squared) is the square of , Pearson's productmoment coefficient.
Example[edit]
Consider the joint probability distribution of and given in the table below.
For this joint distribution, the marginal distributions are:
This yields the following expecations and variances:
Therefore:
Rank correlation coefficients[edit]
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 nonnormality in distributions. However, this view has little mathematical basis, as rank correlation coefficients measure a different type of relationship than the Pearson productmoment 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 pair increases, and so does . This relationship is perfect, in the sense that an increase in is 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 productmoment correlation coefficient is 0.7544, indicating that the points are far from lying on a straight line. In the same way if always decreases when increases, the rank correlation coefficients will be −1, while the Pearson productmoment 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[edit]
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 tdistribution'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, copulabased measure of dependence between multivariate random variables. RDC is invariant with respect to nonlinear scalings of random variables, is capable of discovering a wide range of functional association patterns and takes value zero at independence.
The correlation ratio is able to detect almost any functional dependency,^{[citation needed]}^{[clarification needed]} and the entropybased mutual information, total correlation and dual total correlation are capable of detecting even more general dependencies. These are sometimes referred to as multimoment correlation measures,^{[citation needed]} in comparison to those that consider only second moment (pairwise or quadratic) dependence.
The polychoric correlation is another correlation applied to ordinal data that aims to estimate the correlation between theorised latent variables.
One way to capture a more complete view of dependence structure is to consider a copula between them.
The coefficient of determination generalizes the correlation coefficient for relationships beyond simple linear regression to multiple regression.
Sensitivity to the data distribution[edit]
The degree of dependence between variables and does not depend on the scale on which the variables are expressed. That is, if we are analyzing the relationship between and , most correlation measures are unaffected by transforming to a + bX and to 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 to monotone transformations of the marginal distributions of and/or .
Most correlation measures are sensitive to the manner in which and are 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 metaanalysis; 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. Samplebased 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[edit]
The correlation matrix of random variables is the matrix whose entry is . If the measures of correlation used are productmoment coefficients, the correlation matrix is the same as the covariance matrix of the standardized random variables for . This applies both to the matrix of population correlations (in which case is the population standard deviation), and to the matrix of sample correlations (in which case denotes the sample standard deviation). Consequently, each is necessarily a positivesemidefinite matrix. Moreover, the correlation matrix is strictly positive definite if 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 between and is the same as the correlation between and .
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 nondiagonal 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, Mdependent, and Toeplitz.
[edit]
Similarly for two stochastic processes and : If they are independent, then they are uncorrelated.^{[15]}^{:p. 151}
Common misconceptions[edit]
Correlation and causality[edit]
The conventional dictum that "correlation does not imply causation" means that correlation cannot be used 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, establishing 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[edit]
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 the conditional mean of given , denoted , is not linear in , the correlation coefficient will not fully determine the form of .
The adjacent image shows scatter plots of Anscombe's quartet, a set of four different pairs of variables created by Francis Anscombe.^{[18]} The four variables 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 one outlier which 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. Note that 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[edit]
If a pair of random variables follows a bivariate normal distribution, the conditional mean is a linear function of , and the conditional mean is a linear function of . The correlation coefficient between and , along with the marginal means and variances of and , determines this linear relationship:
where and are the expected values of and , respectively, and and are the standard deviations of and , respectively.
See also[edit]
 Autocorrelation
 Canonical correlation
 Coefficient of determination
 Cointegration
 Concordance correlation coefficient
 Cophenetic correlation
 Copula
 Correlation function
 Correlation gap
 Covariance
 Covariance and correlation
 Crosscorrelation
 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
 Pointbiserial correlation coefficient
 Quadrant count ratio
 Spurious correlation
 Statistical arbitrage
 Subindependence
References[edit]
 ^ Croxton, Frederick Emory; Cowden, Dudley Johnstone; Klein, Sidney (1968) Applied General Statistics, Pitman. ISBN 9780273403159 (page 625)
 ^ Dietrich, Cornelius Frank (1991) Uncertainty, Calibration and Probability: The Statistics of Scientific and Industrial Measurement 2nd Edition, A. Higler. ISBN 9780750300605 (Page 331)
 ^ Aitken, Alexander Craig (1957) Statistical Mathematics 8th Edition. Oliver & Boyd. ISBN 9780050013007 (Page 95)
 ^ ^{a} ^{b} Rodgers, J. L.; Nicewander, W. A. (1988). "Thirteen ways to look at the correlation coefficient". The American Statistician. 42 (1): 59–66. doi:10.1080/00031305.1988.10475524. JSTOR 2685263.
 ^ Dowdy, S. and Wearden, S. (1983). "Statistics for Research", Wiley. ISBN 0471086029 pp 230
 ^ Francis, DP; Coats AJ; Gibson D (1999). "How high can a correlation coefficient be?". Int J Cardiol. 69 (2): 185–199. doi:10.1016/S01675273(99)000285.
 ^ ^{a} ^{b} Yule, G.U and Kendall, M.G. (1950), "An Introduction to the Theory of Statistics", 14th Edition (5th Impression 1968). Charles Griffin & Co. pp 258–270
 ^ Kendall, M. G. (1955) "Rank Correlation Methods", Charles Griffin & Co.
 ^ Mahdavi Damghani B. (2013). "The NonMisleading Value of Inferred Correlation: An Introduction to the Cointelation Model". Wilmott Magazine. 2013 (67): 50–61. doi:10.1002/wilm.10252.
 ^ Székely, G. J. Rizzo; Bakirov, N. K. (2007). "Measuring and testing independence by correlation of distances". Annals of Statistics. 35 (6): 2769–2794. arXiv:0803.4101. doi:10.1214/009053607000000505.
 ^ Székely, G. J.; Rizzo, M. L. (2009). "Brownian distance covariance". Annals of Applied Statistics. 3 (4): 1233–1303. arXiv:1010.0297. doi:10.1214/09AOAS312. PMC 2889501. PMID 20574547.
 ^ LopezPaz D. and Hennig P. and Schölkopf B. (2013). "The Randomized Dependence Coefficient", "Conference on Neural Information Processing Systems" Reprint
 ^ Thorndike, Robert Ladd (1947). Research problems and techniques (Report No. 3). Washington DC: US Govt. print. off.
 ^ Nikolić, D; Muresan, RC; Feng, W; Singer, W (2012). "Scaled correlation analysis: a better way to compute a crosscorrelogram". European Journal of Neuroscience. 35 (5): 1–21. doi:10.1111/j.14609568.2011.07987.x. PMID 22324876.
 ^ Park, Kun Il (2018). Fundamentals of Probability and Stochastic Processes with Applications to Communications. Springer. ISBN 9783319680743.
 ^ Aldrich, John (1995). "Correlations Genuine and Spurious in Pearson and Yule". Statistical Science. 10 (4): 364–376. doi:10.1214/ss/1177009870. JSTOR 2246135.
 ^ Mahdavi Damghani, Babak (2012). "The Misleading Value of Measured Correlation". Wilmott. 2012 (1): 64–73. doi:10.1002/wilm.10167.
 ^ Anscombe, Francis J. (1973). "Graphs in statistical analysis". The American Statistician. 27 (1): 17–21. doi:10.2307/2682899. JSTOR 2682899.
Further reading[edit]
 Cohen, J.; Cohen P.; West, S.G. & Aiken, L.S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Psychology Press. ISBN 9780805822236.
 Hazewinkel, Michiel, ed. (2001) [1994], "Correlation (in statistics)", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 9781556080104
 Oestreicher, J. & D. R. (February 26, 2015). Plague of Equals: A science thriller of international disease, politics and drug discovery. California: Omega Cat Press. p. 408. ISBN 9780963175540.
External links[edit]
Look up correlation or dependence in Wiktionary, the free dictionary. 
Wikimedia Commons has media related to Correlation. 
Wikiversity has learning resources about Correlation 
 MathWorld page on the (cross)correlation coefficient/s of a sample
 Compute significance between two correlations, for the comparison of two correlation values.
 A MATLAB Toolbox for computing Weighted Correlation Coefficients
 [1] ProofthattheSampleBivariateCorrelationhaslimitsplusorminus1
 Interactive Flash simulation on the correlation of two normally distributed variables by Juha Puranen.
 Correlation analysis. Biomedical Statistics
 RPsychologist Correlation visualization of correlation between two numeric variables