In statistics, correlation and dependence are any of a broad class of statistical relationships between two or more random variables or observed data values.
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 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. Correlations can also suggest possible causal, or mechanistic relationships; however statistical dependence is not sufficient to demonstrate the presence of such a relationship.
Formally, dependence refers to any situation in which random variables do not satisfy a mathematical condition of probabilistic independence. In general statistical usage, correlation or corelation can refer to any departure of two or more random variables from independence, but most commonly refers to a more specialized type of relationship between mean values. There are several correlation coefficients, often denoted ρ or r, measuring the degree of correlation. The most common of these is the Pearson correlation coefficient, which is mainly sensitive to a linear relationship between two variables. Other correlation coefficients have been developed to be more robust than the Pearson correlation, or more sensitive to nonlinear relationships.^{[1]}^{[2]}^{[3]}
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The most familiar measure of dependence between two quantities is the Pearson productmoment correlation coefficient, or "Pearson's correlation." 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 ρ_{X,Y} 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:
where E is the expected value operator and cov means covariance. A widely used alternative notation for Pearson's correlation is corr(X,Y).
The Pearson correlation is defined only if both of the standard deviations are finite and both of them are nonzero. It is a corollary of the Cauchy–Schwarz inequality that the correlation cannot exceed 1 in absolute value. The correlation coefficient is symmetric: corr(X,Y) = corr(Y,X).
The Pearson correlation is 1 in the case of an increasing linear relationship, −1 in the case of a decreasing linear relationship, and some value between 1 and 1 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, 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 X is symmetrically distributed about zero, and Y = X^{2}. Then Y is completely determined by X, so that X and Y are perfectly dependent, but their correlation is zero; they are uncorrelated. However, in the special case when X and Y are jointly normal, uncorrelatedness is equivalent to independence.
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 sample correlation coefficient, can be used to estimate the population Pearson correlation between X and Y. The sample correlation coefficient is written
where x and y are the sample means of X and Y, s_{x} and s_{y} are the sample standard deviations of X and Y.
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 increase, 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 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.^{[5]}^{[6]}
To illustrate the nature of rank correlation, and its difference from linear correlation, consider the following four pairs of numbers (x, y):
As we go from each pair to the next pair x increases, and so does y. This relationship is perfect, in the sense that an increase in x is always accompanied by an increase in y. 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's product moment correlation coefficient is 0.456, indicating that the points are far from lying on a straight line. In the same way if y always decreases when x increases, the rank correlation coefficients will be −1, while the 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 in general so, and values of the two coefficients cannot meaningfully be compared. 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.^{[5]}
The information given by a correlation coefficient is not enough to define the dependence structure between random variables. 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 elliptic distributions it characterizes the (hyper)ellipses of equal density, however, it does not completely characterize the dependence structure (for example, the a multivariate tdistribution's degrees of freedom determine the level of tail dependence).
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 the entropybased mutual information/total correlation which is capable of detecting even more general dependencies. The latter are sometimes referred to as multimoment correlation measures, in comparison to those that consider only 2nd 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 degree of dependence between variables X and Y should not depend on the scale on which the variables are expressed. Therefore, most correlation measures in common use are invariant to location and scale transformations of the marginal distributions. That is, if we are analyzing the relationship between X and Y, most correlation measures are unaffected by transforming X to a + bX and Y to c + dY, where a, b, c, and d are constants. This is true of most 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 X and/or Y.
Most correlation measures are sensitive to the manner in which X and Y 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 165cm and 170cm in height, the correlation will be weaker in the latter case.
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 structure of the population from which the data were sampled.
The correlation matrix of n random variables X_{1}, ..., 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. This applies to both the matrix of population correlations (in which case "SD" is the population standard deviation), and to the matrix of sample correlations (in which case "SD" denotes the sample standard deviation). Consequently, each is necessarily a positivesemidefinite matrix.
The correlation matrix is symmetric because the correlation between X_{i} and X_{j} is the same as the correlation between X_{j} and X_{i}.
The conventional dictum that "correlation does not imply causation" means that correlation cannot be used to infer a causal relationship between the variables.^{[7]} 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, 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). For example, one may observe a correlation between an ordinary alarm clock ringing and daybreak, though there is no causal relationship between these phenomena.
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.
The Pearson correlation coefficient indicates the strength of a linear relationship between two variables, but its value generally does not completely characterize their relationship. In particular, if the conditional mean of Y given X, denoted E(YX), is not linear in X, the correlation coefficient will not fully determine the form of E(YX).
The image on the right shows scatterplots of Anscombe's quartet, a set of four different pairs of variables created by Francis Anscombe.^{[8]} The four y variables have the same mean (7.5), standard deviation (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 the individual 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.^{[9]}
If a pair (X, Y) of random variables follows a bivariate normal distribution, the conditional mean E(XY) is a linear function of Y, and the conditional mean E(YX) is a linear function of X. The correlation coefficient r between X and Y, along with the marginal means and variances of X and Y, determines this linear relationship:
where EX and EY are the expected values of X and Y, respectively, and σ_{x} and σ_{y} are the standard deviations of X and Y, respectively.
If a population or dataset is characterised 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.

In statistics, correlation (often measured as a correlation coefficient, ρ) indicates the strength and direction of a linear relationship between two random variables. That is in contrast with the usage of the term in colloquial speech, which denotes any relationship, not necessarily linear. In general statistical usage, correlation or corelation refers to the departure of two random variables from independence. In this broad sense there are several coefficients, measuring the degree of correlation, adapted to the nature of the data.
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A number of different coefficients are used for different situations. The best known is the Pearson productmoment correlation coefficient, which is obtained 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.^{[1]}
The correlation coefficient ρ_{X, Y} 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:
where E is the expected value operator and cov means covariance. A widely used alternative notation is
Since μ_{X} = E(X), σ_{X}^{2} = E[(X  E(X))^{2}] = E(X^{2}) − E^{2}(X) and likewise for Y, and since $E[(XE(X))(YE(Y))]=E(XY)E(X)E(Y)$, we may also write
The correlation is defined only if both of the standard deviations are finite and both of them are 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 dependent, but their correlation is zero; they are uncorrelated. However, in the special case when X and Y are jointly normal, uncorrelatedness is equivalent to independence.
A correlation between two variables is diluted in the presence of measurement error around estimates of one or both variables, in which case disattenuation provides a more accurate coefficient.
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". The Pearson correlation coefficient is then the best estimate of the correlation of X and Y . The Pearson correlation coefficient is written:
r_{xy}=\frac{\sum (x_i\bar{x})(y_i\bar{y})}{(n1) s_x s_y},
where $\backslash bar\{x\}$ and $\backslash bar\{y\}$ are the sample means of X and Y , s_{x} and s_{y} are the sample standard deviations of X and Y and the sum is from i = 1 to n. As with the population correlation, we may rewrite this as
r_{xy}=\frac{\sum x_iy_in \bar{x} \bar{y}}{(n1) s_x s_y}=\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}}.
Again, as is true with the population correlation, the absolute value of the sample correlation must be less than or equal to 1. Though the above formula conveniently suggests a singlepass algorithm for calculating sample correlations, it is notorious for its numerical instability^{[citation needed]}.
The square of the sample correlation coefficient, which is also known as the coefficient of determination, 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
where s_{yx}^{2} is the square of the error of a linear regression of x_{i} on y_{i} by the equation y = a + bx:
and s_{y}^{2} is just the variance of y:
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 y_{i} to x_{i} :
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
The distribution of the correlation coefficient has been examined by R. A. Fisher^{[2]}^{[3]} and A. K. Gayen.^{[4]}
For centered data (i.e., data which have been shifted by the sample mean so as to have an average of zero), the correlation coefficient can also be viewed as the cosine of the angle between the two vectors of samples drawn from the two random variables.
Some practitioners prefer an uncentered (nonPearsoncompliant) correlation coefficient. See the example below for a comparison.
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 5element 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 between two vectors (see dot product), the uncentered correlation coefficient is:
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
as expected.
Another motivation for correlation comes from inspecting the method of simple linear regression. As above, X is the vector of independent variables, $x\_i$, and Y of the dependent variables, $y\_i$, and a simple linear relationship between X and Y is sought, through a leastsquares method on the estimate of Y:
Then, the equation of the leastsquares line can be derived to be of the form:
(Y  \bar{Y}) = \frac{n\sum x_iy_i\sum x_i\sum y_i} {n\sum x_i^2(\sum x_i)^2} (X  \bar{X})
which can be rearranged in the form:
(Y  \bar{Y})=\frac{r s_y}{s_x} (X\bar{X})
where r has the familiar form mentioned above
Correlation  Negative  Positive 

Small  −0.3 to −0.1  0.1 to 0.3 
Medium  −0.5 to −0.3  0.3 to 0.5 
Large  −1.0 to −0.5  0.5 to 1.0 
Several authors have offered guidelines for the interpretation of a correlation coefficient. Cohen (1988),^{[5]} has observed, however, that all such criteria are in some ways arbitrary and should not be observed too strictly. This is because the interpretation of a correlation coefficient depends on the context and purposes. A correlation of 0.9 may be very low if one is verifying a physical law using highquality instruments, but may be regarded as very high in the social sciences where there may be a greater contribution from complicating factors.
Along this vein, it is important to remember that "large" and "small" should not be taken as synonyms for "good" and "bad" in terms of determining that a correlation is of a certain size. For example, a correlation of 1.0 or −1.0 indicates that the two variables analyzed are equivalent modulo scaling. Scientifically, this more frequently indicates a trivial result than a profound one. For example, consider discovering a correlation of 1.0 between how many feet tall a group of people are and the number of inches from the bottom of their feet to the top of their heads.
The population version of Pearson's correlation coefficient is defined in terms of moments, and exists for any bivariate probability distribution for which the population covariance is defined and the marginal population variances are defined and nonzero. In the case of the bivariate normal distribution, the correlation coefficient characterizes the joint distribution as long as the marginal means and variances are known. For most other bivariate distributions this is not true. Nevertheless, the correlation coefficient is highly informative about the degree of linear dependence between two random quantities regardless of whether their joint distribution is normal.
The sample correlation coefficient is the maximum likelihood estimate of the population correlation coefficient for bivariate normal data, 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 data are normal and the sample size is moderate or large. For nonnormal populations, the sample correlation coefficient remains approximately unbiased, but may not be efficient. 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).
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 nonparametric 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. A stratified analysis is one way to accommodate a lack of bivariate normality due to clustering, assessing the effect of a risk factor on outcome while holding another variable constant.^{[6]}
Correlation measures other than Pearson's correlation have their own sensitivities to the data distribution. The population versions of correlation measures based on quantiles or ranks are always defined. Their samplebased estimates will be consistent as long as the underlying sample quantiles are consistent.
Most correlation measures in common use are invariant to location and scale transformations of the marginal distributions. That is, if we are analyzing the relationship between X and Y, the correlation is unaffected by transforming X to a+bX and Y to c+dY, where a, b, c, and d are constants. This is true of most correlation statistics as well as their population analogues.
The sample correlation coefficient is not robust, meaning that it is sensitive to outliers in a data set. Nonparametric correlation coefficients, such as Chisquare, Point biserial correlation, Spearman's ρ, Kendall's τ, and Goodman and Kruskal's lambda may perform better than the sample correlation coefficient when outliers are present. These methods are often less precise than the sample correlation if no outliers are present. Note that in general these nonparametric statistics have different expected values from each other, and from the Pearson correlation coefficient, even for large samples. Since they estimate different population parameters, in general they cannot be directly compared. They generally should be viewed as alternative measures of association, rather than as alternative estimators of the population correlation coefficient.
The information given by a correlation coefficient is not enough to define the dependence structure between random variables. The correlation coefficient completely defines the dependence structure only in very particular cases, for example when the cumulative distribution functions are the multivariate normal distributions. (See diagram above.) In the case of elliptic distributions it characterizes the (hyper)ellipses of equal density, however, it does not completely characterize the dependence structure (for example, the a multivariate tdistribution's degrees of freedom determine the level of tail dependence).
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 the entropybased mutual information/total correlation which is capable of detecting even more general dependencies. The latter are sometimes referred to as multimoment correlation measures, in comparison to those that consider only 2nd 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 correlation matrix of n random variables X_{1}, ..., 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 positivesemidefinite matrix.
The correlation matrix is symmetric because the correlation between $X\_i$ and $X\_j$ is the same as the correlation between $X\_j$ and $X\_i$.
The conventional dictum that "correlation does not imply causation" means that correlation cannot be used to infer a causal relationship between the variables.^{[7]} This dictum should not be taken to mean that correlations cannot indicate causal relations. However, the causes underlying the correlation, if any, may be indirect and unknown. 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.
The Pearson correlation coefficient indicates the strength of a linear relationship between two variables, but its value generally does not completely characterize their relationship. In particular, if the conditional mean of Y given X, denoted E(YX), is not linear in X, the correlation coefficient will not fully determine the form of E(YX).
The image on the right shows scatterplots of Anscombe's quartet, a set of four different pairs of variables created by Francis Anscombe.^{[8]} The four $y$ variables have the same mean (7.5), standard deviation (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, and the Pearson correlation coefficient is not relevant. 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 0.81. 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 the individual 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.^{[9]}
If a pair (X, Y) of random variables follows a bivariate normal distribution, the conditional mean E(XY) is a linear function of Y, and the conditional mean E(YX) is a linear function of X. The correlation coefficient r between X and Y, along with the marginal means and variances of X and Y, determines this linear relationship:
E(YX) = EY + r\sigma_y\frac{XEX}{\sigma_x},
where EX and EY are the expected values of X and Y, respectively, and σ_{x} and σ_{y} are the standard deviations of X and Y, respectively.
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In statistics and probability theory, correlation means how closely related two sets of data are.
Correlation does not always mean that one causes the other. It is very possible that there is a third factor involved.
Correlation usually has one of two directions. These are positive or negative. If it is positive, then the two sets go up together. If it is negative, then one goes up while the other goes down.
Lots of different measurements of correlation are used for different situations. For example on a scatter graph, people draw a line of best fit to show the direction of the correlation.Contents 
Strong and weak are words used to describe correlation. If there is strong correlation, then the points are all close together. If there is weak correlation, then the points are all spread apart. There are ways of making numbers show how strong the correlation is. These measurements are called correlation coefficients. The best known is the Pearson productmoment correlation coefficient. You put in data into a formula and it gives you a number. If the number is 1 or 1, then there is strong correlation. If the answer is 0, then there is no correlation.^{[1]} Another kind of correlation coefficient is Spearman's rank correlation coefficient.
Correlation does not always mean that one thing causes the another thing (causation), because a something else might have caused it. For example, on hot days people buy ice cream, and people also go to the beach where some are eaten by sharks. There is a correlation between ice cream sales and shark attacks (they both go up as the temperature goes up in this case). But just because ice cream sales go up does not cause (causation) more shark attacks.
Because correlation does not imply causation scientists, economists, etc will test out theories by creating isolated environments where only one factor is changed (this is not always possible though). However economists, scientists, and especially politicians and salesmen will sometimes say that correlation implies causation for personal gain or out of stupidity.
