Correlation
From Academic Kids

 This article is about the correlation coefficient between two random variables. The term correlation can also mean the crosscorrelation of two functions or electron correlation in molecular systems.
In probability theory and statistics, correlation, also called correlation coefficient, is a numeric measure of the strength of linear relationship between two random variables. A number of different coefficients are used for different situations. The best known is the Pearson productmoment correlation coefficient, which is found by dividing the covariance of the two variables by the product of their standard deviations. Despite its name it was first introduced by Francis Galton.
Contents 
Productmoment coefficient
Mathematical properties
Correxample.png
The correlation ρ_{xy} between two random variables X and Y with expected values μ_{X} and μ_{Y} and standard deviations σ_{X} and σ_{Y} is defined as:
 <math>
\rho_{xy}={\mathrm{cov}(X,Y) \over \sigma_X \sigma_Y} ={E((X\mu_X)(Y\mu_Y)) \over \sigma_X\sigma_Y}.<math>
Since μ_{X}=E(X), σ_{X}^{2}=E(X^{2})E^{2}(X) and likewise for Y, we may also write:
 <math>\rho_{xy}=\frac{E(XY)E(X)E(Y)}{\sqrt{E(X^2)E^2(X)}~\sqrt{E(Y^2)E^2(Y)}}<math>
The correlation is defined only if both standard deviations are finite and at least one of them is nonzero. It is a corollary of the CauchySchwarz inequality that the correlation cannot exceed 1 in absolute value.
The correlation is 1 in the case of an increasing linear relationship, −1 in the case of a decreasing linear relationship, and some value in between in all other cases, indicating the degree of linear dependence between the variables. The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.
If the variables are independent then the correlation is 0, but the converse is not true because the correlation coefficient detects only linear dependencies between two variables. Here is an example: Suppose the random variable X is uniformly distributed on the interval from −1 to 1, and Y = X^{2}. Then Y is completely determined by X, so that X and Y are as far from being independent as two random variables can be, but their correlation is zero; they are uncorrelated. However, in the special case when X and Y are jointly normal, independence is equivalent to uncorrelatedness.
The sample correlation
If we have a series of n measurements of X and Y written as x_{i} and y_{i} where i = 1, 2, ..., n, then the Pearson productmoment correlation coefficient can be used to estimate the correlation of X and Y . The Pearson coefficient is also known as the "sample correlation coefficient". It is especially important if X and Y are both normally distributed. The Pearson correlation coefficient is then the best estimate of the correlation of X and Y . The Pearson correlation coefficient is written:
 <math>
r_{xy}=\frac{\sum (x_i\bar{x})(y_i\bar{y})}{(n1) s_x s_y} <math>
where <math>\bar{x}<math> and <math>\bar{y}<math> are the sample means of x_{i} and y_{i} , s_{x} and s_{y} are the sample standard deviations of x_{i} and y_{i} and the sum is from i = 1 to n. As with the population correlation, we may rewrite this as
 <math>
r_{xy}=\frac{n\sum x_iy_i\sum x_i\sum y_i} {\sqrt{n\sum x_i^2(\sum x_i)^2}~\sqrt{n\sum y_i^2(\sum y_i)^2}}. <math>
Again, as is true with the population correlation, the absolute value of the sample correlation must be less than or equal to 1.
The sample correlation coefficient is the fraction of the variance in y_{i} that is accounted for by a linear fit of x_{i} to y_{i} . This is written
 <math>r_{xy}^2=1\frac{\sigma_{yx}^2}{\sigma_y^2}<math>
where σ_{yx}^{2} is the square of the error of a linear fit of y_{i} to x_{i} by the equation y = a + bx.
 <math>\sigma_{yx}^2=\sum_{i=1}^n (y_iabx_i)^2<math>
and σ_{y}^{2} is just the variance of y
 <math>\sigma_y^2=\sum_{i=1}^n (y_i\bar{y})^2<math>
Note that since the sample correlation coefficient is symmetric in x_{i} and y_{i} , we will get the same value for a fit of x_{i} to y_{i} :
 <math>r_{xy}^2=1\frac{\sigma_{xy}^2}{\sigma_x^2}<math>
This equation also gives an intuitive idea of the correlation coefficient for higher dimensions. Just as the above described sample correlation coefficient is the fraction of variance accounted for by the fit of a 1dimensional linear submanifold to a set of 2dimensional vectors (x_{i} , y_{i} ), so we can define a correlation coefficient for a fit of an mdimensional linear submanifold to a set of ndimensional vectors. For example, if we fit a plane z = a + bx + cy to a set of data (x_{i} , y_{i} , z_{i} ) then the correlation coefficient of z to x and y is
 <math>r^2=1\frac{\sigma_{zxy}^2}{\sigma_z^2}.\,<math>
Nonparametric correlation coefficients
Pearson's correlation coefficient is a parametric statistic, and it may be less useful if the underlying assumption of normality is violated. Nonparametric correlation methods, such as Spearman's ρ and Kendall's τ may be useful when distributions are not normal; they are a little less powerful than parametric methods if the assumptions underlying the latter are met, but are less likely to give distorted results when the assumptions fail.
Other measures of dependence among random variables
To get a measure for more general dependencies in the data (also nonlinear) it is better to use the correlation ratio which is able to detect almost any functional dependency, or mutual information which detects even more general dependencies. To fully capture the dependence between random variables we must consider the copula between them.
Correlation matrices
The correlation matrix of n random variables X_{2}, ..., X_{n} is the n × n matrix whose i,j entry is corr(X_{i}, X_{j}). If the measures of correlation used are productmoment coefficients, the correlation matrix is the same as the covariance matrix of the standardized random variables X_{i} /SD(X_{i} for i = 1, ..., n. Consequently it is necessarily a nonnegative definite matrix.
"Correlation does not imply causation"
The conventional dictum that "correlation does not imply causation" is treated in the article titled spurious relationship. See also correlation implies causation (logical fallacy). However, correlations have causes.
External links
 Statsoft Electronic Textbook (http://www.statsoft.com/textbook/stathome.html)
 Pearson's Correlation Coefficient (http://www.vias.org/tmdatanaleng/cc_corr_coeff.html)  How to calculate it fastde:Korrelation
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