Pearson's correlation coefficient, when applied to a population, is commonly represented by the Greek letter ρ (rho) and may be referred to as the population correlation coefficient or the population Pearson correlation coefficient. Given a pair of random variables \((X,Y)\) the formula for ρ is:

\(\rho_{X,Y} = \frac{{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}\)

where:

• \(cov\) is the covariance
• \(\sigma_{X}\) is the standard deviation of \(X\)
• \(\sigma_{Y}\) is the standard deviation of \(Y\)

The formula for \(\rho\) can be expressed in terms of mean and expectation. Since

\({cov}(X,Y) = \mathbb{E}\lbrack(X - \mu_{X})(Y - \mu_{Y})\rbrack,\) the formula for \(\rho\)[[ can also be written as

\(\rho_{X,Y} = \frac{\mathbb{E}\lbrack(X - \mu_{X})(Y - \mu_{Y})\rbrack}{\sigma_{X}\sigma_{Y}}\)

where:

• \(\sigma_{Y}\) and \(\sigma_{X}\) are defined as above
• \(\mu_{X}\) is the mean of \(X\)
• \(\mu_{Y}\) is the mean of \(Y\)
• \(\mathbb{E}\) is the expectation.

The formula for \(\rho\)[[ can be expressed in terms of uncentered moments. Since

\(\mu_{X} = \mathbb{E}\lbrack\, X\,\rbrack\) \(\mu_{Y} = \mathbb{E}\lbrack\, Y\,\rbrack\) \(\sigma_{X}^{2} = \mathbb{E}\lbrack\,\left( {X - \mathbb{E}\lbrack X\rbrack} \right)^{2}\,\rbrack = \mathbb{E}\lbrack\, X^{2}\,\rbrack - \left( {\mathbb{E}\lbrack\, X\,\rbrack} \right)^{2}\) \(\sigma_{Y}^{2} = \mathbb{E}\lbrack\,\left( {Y - \mathbb{E}\lbrack Y\rbrack} \right)^{2}\,\rbrack = \mathbb{E}\lbrack\, Y^{2}\,\rbrack - \left( {\,\mathbb{E}\lbrack\, Y\,\rbrack} \right)^{2}\) \(\mathbb{E}\lbrack\,\left( {X - \mu_{X}} \right)\left( {Y - \mu_{Y}} \right)\,\rbrack = \mathbb{E}\lbrack\,\left( {X - \mathbb{E}\lbrack\, X\,\rbrack} \right)\left( {Y - \mathbb{E}\lbrack\, Y\,\rbrack} \right)\,\rbrack = \mathbb{E}\lbrack\, X\, Y\,\rbrack - \mathbb{E}\lbrack\, X\,\rbrack\mathbb{E}\lbrack\, Y\,\rbrack\,,\) the formula for \(\rho\) can also be written as

\[\rho_{X,Y} = \frac{\mathbb{E}\lbrack\, X\, Y\,\rbrack - \mathbb{E}\lbrack\, X\,\rbrack\mathbb{E}\lbrack\, Y\,\rbrack}{\sqrt{\mathbb{E}\lbrack\, X^{2}\,\rbrack - \left( {\mathbb{E}\lbrack\, X\,\rbrack} \right)^{2}}\ \sqrt{\mathbb{E}\lbrack\, Y^{2}\,\rbrack - \left( {\mathbb{E}\lbrack\, Y\,\rbrack} \right)^{2}}}.\]

Pearson's correlation coefficient, when applied to a sample, is commonly represented by \(r_{xy}\) and may be referred to as the sample correlation coefficient or the sample Pearson correlation coefficient. We can obtain a formula for \(r_{xy}\) by substituting estimates of the covariances and variances based on a sample into the formula above. Given paired data \(\left\{ {(x_{1},y_{1}),\ldots,(x_{n},y_{n})} \right\}\) consisting of \(n\) pairs, \(r_{xy}\) is defined as:

\(r_{xy} = \frac{\sum\limits_{i = 1}^{n}(x_{i} - \overline{x})(y_{i} - \overline{y})}{\sqrt{\sum\limits_{i = 1}^{n}(x_{i} - \overline{x})^{2}}\sqrt{\sum\limits_{i = 1}^{n}(y_{i} - \overline{y})^{2}}}\)

where:

• \(n\) is sample size
• \(x_{i},y_{i}\) are the individual sample points indexed with i
• \(\overline{x} = \frac{1}{n}\sum\limits_{i = 1}^{n}x_{i}\) (the sample mean); and analogously for \(\overline{y}\)[[

Rearranging gives us this formula for \(r_{xy}\)

\(r_{xy} = \frac{n\sum x_{i}y_{i} - \sum x_{i}\sum y_{i}}{\sqrt{n\sum x_{i}^{2} - \left( {\sum x_{i}} \right)^{2}}\ \sqrt{n\sum y_{i}^{2} - \left( {\sum y_{i}} \right)^{2}}}.\) where \(n,x_{i},y_{i}\) are defined as above.

This formula suggests a convenient single-pass algorithm for calculating sample correlations, though depending on the numbers involved, it can sometimes be numerically unstable.

Rearranging again gives us this formula for \(r_{xy}\)

\(r_{xy} = \frac{\sum\limits_{i}x_{i}y_{i} - n\overline{x}\overline{y}}{\sqrt{\sum\limits_{i}x_{i}^{2} - n{\overline{x}}^{2}}\ \sqrt{\sum\limits_{i}y_{i}^{2} - n{\overline{y}}^{2}}}.\) where \(n,x_{i},y_{i},\overline{x},\overline{y}\) are defined as above.

An equivalent expression gives the formula for \(r_{xy}\) as the mean of the products of the standard scores as follows:

\(r_{xy} = \frac{1}{n - 1}\sum\limits_{i = 1}^{n}\left( \frac{x_{i} - \overline{x}}{s_{x}} \right)\left( \frac{y_{i} - \overline{y}}{s_{y}} \right)\) where:

• \(n,x_{i},y_{i},\overline{x},\overline{y}\) are defined as above, and \(s_{x},s_{y}\) are defined below
• \(\left( \frac{x_{i} - \overline{x}}{s_{x}} \right)\) is the standard score (and analogously for the standard score of \(y\)

Alternative formulae for \(r_{xy}\) are also available. For example. one can use the following formula for \(r_{xy}\)

\(r_{xy} = \frac{\sum x_{i}y_{i} - n\overline{x}\overline{y}}{(n - 1)s_{x}s_{y}}\) where:

• \(n,x_{i},y_{i},\overline{x},\overline{y}\) are defined as above and:
• \(s_{x} = \sqrt{\frac{1}{n - 1}\sum\limits_{i = 1}^{n}(x_{i} - \overline{x})^{2}}\) (the sample standard deviation); and analogously for \(s_{y}\)

Under heavy noise conditions, extracting the correlation coefficient between two sets of stochastic variables is nontrivial, in particular where Canonical Correlation Analysis reports degraded correlation values due to the heavy noise contributions. A generalization of the approach is given elsewhere.

In case of missing data, Garren derived the maximum likelihood estimator.

interpretation[edit]

:CUSTOM_ID: geometric-interpretationedit

[[//upload.wikimedia.org/wikipedia/commons/thumb/d/d1/Regression_lines.png/330px-Regression_lines.png]]

[[/wiki/][]]

Regression lines for y = g/_{/X}(x) [red] and x = g/_{/Y}(y) [blue]

For uncentered data, there is a relation between the correlation coefficient and the angle φ between the two regression lines, y = g/_{/X}(x) and x = g/_{/Y}(y), obtained by regressing y on x and x on y respectively. (Here, φ is measured counterclockwise within the first quadrant formed around the lines' intersection point if r > 0, or counterclockwise from the fourth to the second quadrant if r < 0.) One can show that if the standard deviations are equal, then r = sec φ − tan φ, where sec and tan are trigonometric functions.

For centered data (i.e., data which have been shifted by the sample means of their respective variables so as to have an average of zero for each variable), the correlation coefficient can also be viewed as the cosine of the angle θ between the two observed vectors in N-dimensional space (for N observations of each variable)

Both the uncentered (non-Pearson-compliant) and centered correlation coefficients can be determined for a dataset. As an example, suppose five countries are found to have gross national products of 1, 2, 3, 5, and 8 billion dollars, respectively. Suppose these same five countries (in the same order) are found to have 11%, 12%, 13%, 15%, and 18% poverty. Then let x and y be ordered 5-element vectors containing the above data: x = (1, 2, 3, 5, 8) and y = (0.11, 0.12, 0.13, 0.15, 0.18).

By the usual procedure for finding the angle θ between two vectors (see dot product), the uncentered correlation coefficient is:

\(\cos\theta = \frac{\mathbf{x} \cdot \mathbf{y}}{\left\| \mathbf{x} \right\|\left\| \mathbf{y} \right\|} = \frac{2.93}{\sqrt{103}\sqrt{0.0983}} = 0.920814711.\) This uncentered correlation coefficient is identical with the cosine similarity. Note that the above data were deliberately chosen to be perfectly correlated: y = 0.10 + 0.01 x. The Pearson correlation coefficient must therefore be exactly one. Centering the data (shifting x by ℰ(x) = 3.8 and y by ℰ(y) = 0.138) yields x = (−2.8, −1.8, −0.8, 1.2, 4.2) and y = (−0.028, −0.018, −0.008, 0.012, 0.042), from which

\(\cos\theta = \frac{\mathbf{x} \cdot \mathbf{y}}{\left\| \mathbf{x} \right\|\left\| \mathbf{y} \right\|} = \frac{0.308}{\sqrt{30.8}\sqrt{0.00308}} = 1 = \rho_{xy},\) as expected.

correlation[edit]

:CUSTOM_ID: interpretation-of-the-size-of-a-correlationedit

[[//upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Pearson_{correlationandpredictionintervals.svg}/200px-Pearson_{correlationandpredictionintervals.svg.png}]]

[[/wiki/][]]

This figure gives a sense of how the usefulness of a Pearson correlation for predicting values varies with its magnitude. Given jointly normal X, Y with correlation ρ, \(1 - \sqrt{1 - \rho^{2}}\) (plotted here as a function of ρ) is the factor by which a given prediction interval for Y may be reduced given the corresponding value of X. For example, if ρ = 0.5, then the 95% prediction interval of Y/|/X will be about 13% smaller than the 95% prediction interval of Y.

Several authors have offered guidelines for the interpretation of a correlation coefficient. However, all such criteria are in some ways arbitrary. The interpretation of a correlation coefficient depends on the context and purposes. A correlation of 0.8 may be very low if one is verifying a physical law using high-quality instruments, but may be regarded as very high in the social sciences, where there may be a greater contribution from complicating factors.

Permutation tests provide a direct approach to performing hypothesis tests and constructing confidence intervals. A permutation test for Pearson's correlation coefficient involves the following two steps:

1. Using the original paired data (x/_{/i}, y/_{/i}), randomly redefine the pairs to create a new data set (x/_{/i}, y/_{/i′}), where the i′ are a permutation of the set {1,…,/n/}. The permutation i′ is selected randomly, with equal probabilities placed on all n! possible permutations. This is equivalent to drawing the i′ randomly without replacement from the set {1, …, n}. In bootstrapping, a closely related approach, the i and the i′ are equal and drawn with replacement from {1, …, n};
2. Construct a correlation coefficient r from the randomized data.

To perform the permutation test, repeat steps (1) and (2) a large number of times. The p-value for the permutation test is the proportion of the r values generated in step (2) that are larger than the Pearson correlation coefficient that was calculated from the original data. Here "larger" can mean either that the value is larger in magnitude, or larger in signed value, depending on whether a two-sided or one-sided test is desired.

The bootstrap can be used to construct confidence intervals for Pearson's correlation coefficient. In the "non-parametric" bootstrap, n pairs (x/_{/i}, y/_{/i}) are resampled "with replacement" from the observed set of n pairs, and the correlation coefficient r is calculated based on the resampled data. This process is repeated a large number of times, and the empirical distribution of the resampled r values are used to approximate the sampling distribution of the statistic. A 95% confidence interval for ρ can be defined as the interval spanning from the 2.5th to the 97.5th percentile of the resampled r values.

[[//upload.wikimedia.org/wikipedia/commons/thumb/2/24/Critical_{correlationvs}._{samplesize.svg}/324px-Critical_{correlationvs}._{samplesize.svg.png}]]

[[/wiki/][]]

Critical values of Pearson's correlation coefficient that must be exceeded to be considered significantly nonzero at the 0.05 level.

For pairs from an uncorrelated bivariate normal distribution, the sampling distribution of a certain function of Pearson's correlation coefficient follows Student's t-distribution with degrees of freedom n − 2. Specifically, if the underlying variables have a bivariate normal distribution, the variable

\(t = r\sqrt{\frac{n - 2}{1 - r^{2}}}\) has a student's t-distribution in the null case (zero correlation). This holds approximately in case of non-normal observed values if sample sizes are large enough. For determining the critical values for r the inverse function is needed:

\(r = \frac{t}{\sqrt{n - 2 + t^{2}}}.\) Alternatively, large sample, asymptotic approaches can be used.

Another early paper provides graphs and tables for general values of ρ, for small sample sizes, and discusses computational approaches.

In the case where the underlying variables are not normal, the sampling distribution of Pearson's correlation coefficient follows a Student's t-distribution, but the degrees of freedom are reduced.

For data that follow a bivariates normal distribution, the exact density function f/(/r) for the sample correlation coefficient r of a normal bivariate is

\(f(r) = \frac{(n - 2)\,\Gamma(n - 1)(1 - \rho^{2})^{\frac{n - 1}{2}}(1 - r^{2})^{\frac{n - 4}{2}}}{\sqrt{2\pi}\,\Gamma(n - \frac{1}{2})(1 - \rho r)^{n - \frac{3}{2}}}{}_{2}F_{1}(\frac{1}{2},\frac{1}{2};\frac{1}{2}(2n - 1);\frac{1}{2}(\rho r + 1))\) where \(\Gamma\) is the gamma function and \({}_{2}F_{1}(a,b;c;z)\) is the Gaussian hypergeometric function.

In the special case when \(\rho = 0\) the exact density function f/(/r) can be written as:

\(f(r) = \frac{\left( {1 - r^{2}} \right)^{\frac{n - 4}{2}}}{B\left( {\frac{1}{2},\frac{1}{2}(n - 2)} \right)},\) where \(B\) is the beta function, which is one way of writing the density of a Student's t-distribution, as above.

Confidence intervals and tests can be calculated from a confidence distribution. An exact confidence density for ρ is

\[\pi(\rho|r) = \frac{\nu(\nu - 1)\Gamma(\nu - 1)}{\sqrt{2\pi}\Gamma(\nu + \frac{1}{2})}(1 - r^{2})^{\frac{\nu - 1}{2}} \cdot (1 - \rho^{2})^{\frac{\nu - 2}{2}} \cdot (1 - r\rho)^{\frac{1 - 2\nu}{2}}F\left( {\frac{3}{2}, - \frac{1}{2};\nu + \frac{1}{2};\frac{1 + r\rho}{2}} \right)\]

where \(F\) is the Gaussian hypergeometric function and \(\nu = n - 1 > 1\)

In practice, confidence intervals and hypothesis tests relating to ρ are usually carried out using the Fisher transformation, \(F\)

\(F(r) \equiv \frac{1}{2}\,\ln\left( \frac{1 + r}{1 - r} \right) = {artanh}(r)\) F/(/r) approximately follows a normal distribution with

\(\text{mean} = F(\rho) = {artanh}(\rho)\) standard error \(= \text{SE} = \frac{1}{\sqrt{n - 3}},\) where n is the sample size. The approximation error is lowest for a large sample size \(n\) and small \(r\) and \(\rho_{0}\) and increases otherwise.

Using the approximation, a z-score is

\(z = \frac{x - \text{mean}}{\text{SE}} = \lbrack F(r) - F(\rho_{0})\rbrack\sqrt{n - 3}\) under the null hypothesis that \(\rho = \rho_{0}\) given the assumption that the sample pairs are independent and identically distributed and follow a bivariate normal distribution. Thus an approximate p-value can be obtained from a normal probability table. For example, if z = 2.2 is observed and a two-sided p-value is desired to test the null hypothesis that \(\rho = 0\) the p-value is 2 Φ(−2.2) = 0.028, where Φ is the standard normal cumulative distribution function.

To obtain a confidence interval for ρ, we first compute a confidence interval for F/(\(\rho\)

\(100(1 - \alpha)\%\text{CI}:{artanh}(\rho) \in \lbrack{artanh}(r) \pm z_{\alpha/2}\text{SE}\rbrack\) The inverse Fisher transformation brings the interval back to the correlation scale.

\(100(1 - \alpha)\%\text{CI}:\rho \in \lbrack\tanh({artanh}(r) - z_{\alpha/2}\text{SE}),\tanh({artanh}(r) + z_{\alpha/2}\text{SE})\rbrack\) For example, suppose we observe r = 0.3 with a sample size of /n/=50, and we wish to obtain a 95% confidence interval for ρ. The transformed value is arctanh(r) = 0.30952, so the confidence interval on the transformed scale is 0.30952 ± 1.96/√47, or (0.023624, 0.595415). Converting back to the correlation scale yields (0.024, 0.534).

The population Pearson correlation coefficient is defined in terms of moments, and therefore exists for any bivariate probability distribution for which the population covariance is defined and the marginal population variances are defined and are non-zero. Some probability distributions such as the Cauchy distribution have undefined variance and hence ρ is not defined if X or Y follows such a distribution. In some practical applications, such as those involving data suspected to follow a heavy-tailed distribution, this is an important consideration. However, the existence of the correlation coefficient is usually not a concern; for instance, if the range of the distribution is bounded, ρ is always defined.

• If the sample size is moderate or large and the population is normal, then, in the case of the bivariate normal distribution, the sample correlation coefficient is the maximum likelihood estimate of the population correlation coefficient, and is asymptotically unbiased and efficient, which roughly means that it is impossible to construct a more accurate estimate than the sample correlation coefficient.
• If the sample size is large and the population is not normal, then the sample correlation coefficient remains approximately unbiased, but may not be efficient.
• If the sample size is large, then the sample correlation coefficient is a consistent estimator of the population correlation coefficient as long as the sample means, variances, and covariance are consistent (which is guaranteed when the law of large numbers can be applied).
• If the sample size is small, then the sample correlation coefficient r is not an unbiased estimate of ρ. The adjusted correlation coefficient must be used instead: see elsewhere in this article for the definition.
• Correlations can be different for imbalanced dichotomous data when there is variance error in sample.

Like many commonly used statistics, the sample statistic r is not robust, so its value can be misleading if outliers are present. Specifically, the PMCC is neither distributionally robust,^{[/citation needed/]} nor outlier resistant (see Robust statistics#Definition). Inspection of the scatterplot between X and Y will typically reveal a situation where lack of robustness might be an issue, and in such cases it may be advisable to use a robust measure of association. Note however that while most robust estimators of association measure statistical dependence in some way, they are generally not interpretable on the same scale as the Pearson correlation coefficient.

Statistical inference for Pearson's correlation coefficient is sensitive to the data distribution. Exact tests, and asymptotic tests based on the Fisher transformation can be applied if the data are approximately normally distributed, but may be misleading otherwise. In some situations, the bootstrap can be applied to construct confidence intervals, and permutation tests can be applied to carry out hypothesis tests. These non-parametric approaches may give more meaningful results in some situations where bivariate normality does not hold. However the standard versions of these approaches rely on exchangeability of the data, meaning that there is no ordering or grouping of the data pairs being analyzed that might affect the behavior of the correlation estimate.

A stratified analysis is one way to either accommodate a lack of bivariate normality, or to isolate the correlation resulting from one factor while controlling for another. If W represents cluster membership or another factor that it is desirable to control, we can stratify the data based on the value of W, then calculate a correlation coefficient within each stratum. The stratum-level estimates can then be combined to estimate the overall correlation while controlling for W.

The sample correlation coefficient r is not an unbiased estimate of ρ. For data that follows a bivariate normal distribution, the expectation E[/r/] for the sample correlation coefficient r of a normal bivariate is

\(\mathbb{E}\lbrack r\rbrack = \rho - \frac{\rho\left( {1 - \rho^{2}} \right)}{2n} + \cdots,\quad\) therefore r is a biased estimator of \(\rho.\) The unique minimum variance unbiased estimator /r/_adj is given by

\(r_{\text{adj}} = r\,{_{2}\mathbf{F}_{1}}\left( {\frac{1}{2},\frac{1}{2};\frac{n - 1}{2};1 - r^{2}} \right),\)

where:

• \(r,n\) are defined as above,
• \({_{2}\mathbf{F}_{1}}(a,b;c;z)\) is the Gaussian hypergeometric function.

An approximately unbiased estimator r/_adj can be obtained^{[citation needed/]} by truncating E[/r/] and solving this truncated equation:

\(r = \mathbb{E}\lbrack r\rbrack \approx r_{\text{adj}} - \frac{r_{\text{adj}}(1 - r_{\text{adj}}^{2})}{2n}.\)

An approximate solution^{[/citation needed/]} to equation (2) is:

\(r_{\text{adj}} \approx r\left\lbrack {1 + \frac{1 - r^{2}}{2n}} \right\rbrack,\)

where in (3):

• \(r,n\) are defined as above,
• r/_adj is a suboptimal estimator,^{[citation needed/][/clarification needed/]}
• r/_adj can also be obtained by maximizing log(/f/(/r)),
• /r/_adj has minimum variance for large values of n,
• r/_adj has a bias of order 1⁄(/n − 1).

Another proposed adjusted correlation coefficient is:^{[/citation needed/]}

\(r_{\text{adj}} = \sqrt{1 - \frac{(1 - r^{2})(n - 1)}{(n - 2)}}.\) Note that r/_adj ≈ /r for large values of n.

Suppose observations to be correlated have differing degrees of importance that can be expressed with a weight vector w. To calculate the correlation between vectors x and y with the weight vector w (all of length n),

• Weighted mean:

  \[m(x;w) = \frac{\sum\limits_{i}w_{i}x_{i}}{\sum\limits_{i}w_{i}}.\]

• Weighted covariance

  \[{cov}(x,y;w) = \frac{\sum\limits_{i}w_{i} \cdot (x_{i} - m(x;w))(y_{i} - m(y;w))}{\sum\limits_{i}w_{i}}.\]

• Weighted correlation

  \[{corr}(x,y;w) = \frac{{cov}(x,y;w)}{\sqrt{{cov}(x,x;w){cov}(y,y;w)}}.\]

coefficient[edit]

:CUSTOM_ID: reflective-correlation-coefficientedit

The reflective correlation is a variant of Pearson's correlation in which the data are not centered around their mean values.^{[/citation needed/]} The population reflective correlation is

\({corr}_{r}(X,Y) = \frac{\mathbb{E}\lbrack\, X\, Y\,\rbrack}{\sqrt{\mathbb{E}\lbrack\, X^{2}\,\rbrack \cdot \mathbb{E}\lbrack\, Y^{2}\,\rbrack}}.\) The reflective correlation is symmetric, but it is not invariant under translation:

\({corr}_{r}(X,Y) = {corr}_{r}(Y,X) = {corr}_{r}(X,bY) \neq {corr}_{r}(X,a + bY),\quad a \neq 0,b > 0.\) The sample reflective correlation is equivalent to cosine similarity:

\(rr_{xy} = \frac{\sum x_{i}y_{i}}{\sqrt{(\sum x_{i}^{2})(\sum y_{i}^{2})}}.\) The weighted version of the sample reflective correlation is

\(rr_{xy,w} = \frac{\sum w_{i}x_{i}y_{i}}{\sqrt{(\sum w_{i}x_{i}^{2})(\sum w_{i}y_{i}^{2})}}.\)

coefficient[edit]

:CUSTOM_ID: scaled-correlation-coefficientedit

Main article: Scaled correlation

Scaled correlation is a variant of Pearson's correlation in which the range of the data is restricted intentionally and in a controlled manner to reveal correlations between fast components in time series. Scaled correlation is defined as average correlation across short segments of data.

Let \(K\) be the number of segments that can fit into the total length of the signal \(T\) for a given scale \(s\)

\(K = {round}\left( \frac{T}{s} \right).\) The scaled correlation across the entire signals \({\overline{r}}_{s}\) is then computed as

\({\overline{r}}_{s} = \frac{1}{K}\sum\limits_{k = 1}^{K}r_{k},\) where \(r_{k}\) is Pearson's coefficient of correlation for segment \(k\)

By choosing the parameter \(s\) the range of values is reduced and the correlations on long time scale are filtered out, only the correlations on short time scales being revealed. Thus, the contributions of slow components are removed and those of fast components are retained.

distance[edit]

:CUSTOM_ID: pearsons-distanceedit

A distance metric for two variables X and Y known as Pearson's distance can be defined from their correlation coefficient as

\(d_{X,Y} = 1 - \rho_{X,Y}.\) Considering that the Pearson correlation coefficient falls between [−1, +1], the Pearson distance lies in [0, 2]. The Pearson distance has been used in cluster analysis and data detection for communications and storage with unknown gain and offset

Further information: Circular statistics

For variables X = {x/₁,…,/x/_{/n}} and Y = {y/₁,…,/y/_{/n}} that are defined on the unit circle [0, 2π), it is possible to define a circular analog of Pearson's coefficient. This is done by transforming data points in X and Y with a sine function such that the correlation coefficient is given as:

\(r_{\text{circular}} = \frac{\sum\limits_{i = 1}^{n}\sin(x_{i} - \overline{x})\sin(y_{i} - \overline{y})}{\sqrt{\sum\limits_{i = 1}^{n}\sin(x_{i} - \overline{x})^{2}}\sqrt{\sum\limits_{i = 1}^{n}\sin(y_{i} - \overline{y})^{2}}}\) where \(\overline{x}\) and \(\overline{y}\) are the circular means of X and Y. This measure can be useful in fields like meteorology where the angular direction of data is important.

correlation[edit]

:CUSTOM_ID: partial-correlationedit

Main article: Partial correlation

If a population or data-set is characterized by more than two variables, a partial correlation coefficient measures the strength of dependence between a pair of variables that is not accounted for by the way in which they both change in response to variations in a selected subset of the other variables.