Difference between revisions of "Correlate"
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==Origin== | ==Origin== | ||
Back-formation from co-rrelation | Back-formation from co-rrelation | ||
| − | *[ | + | *[https://en.wikipedia.org/wiki/17th_century 1643] |
==Definitions== | ==Definitions== | ||
*1: either of [[two]] [[things]] so [[related]] that one directly implies or is [[complementary]] to the other (as [[husband]] and [[wife]]) | *1: either of [[two]] [[things]] so [[related]] that one directly implies or is [[complementary]] to the other (as [[husband]] and [[wife]]) | ||
*2: a [[phenomenon]] that accompanies another phenomenon, is usually [[parallel]] to it, and is related in some way to it <precise electrical correlates of [[conscious]] [[thinking]] in the human [[brain]] | *2: a [[phenomenon]] that accompanies another phenomenon, is usually [[parallel]] to it, and is related in some way to it <precise electrical correlates of [[conscious]] [[thinking]] in the human [[brain]] | ||
==Description== | ==Description== | ||
| − | In [[statistics]], dependence refers to any statistical [[relationship]] between [[two]] [ | + | In [[statistics]], dependence refers to any statistical [[relationship]] between [[two]] [https://en.wikipedia.org/wiki/Random_variable random variables] or two sets of [[data]]. Correlation refers to any of a broad class of statistical relationships involving dependence. |
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]]. In this example there is a [[causal]] relationship, because [[extreme]] [[weather]] causes people to use more [[electricity]] for heating or cooling; however, statistical dependence is not sufficient to [[demonstrate]] the presence of such a causal relationship. | 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]]. In this example there is a [[causal]] relationship, because [[extreme]] [[weather]] causes people to use more [[electricity]] for heating or cooling; however, statistical dependence is not sufficient to [[demonstrate]] the presence of such a causal relationship. | ||
| − | [[Formally]], dependence refers to any situation in which [[random]] [[variables]] do not satisfy a [[mathematical]] condition of [[probabilistic]] independence. In loose usage, correlation can refer to any departure of [[two]] or more random variables from independence, but technically it refers to any of several more specialized [[types]] 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 [ | + | [[Formally]], dependence refers to any situation in which [[random]] [[variables]] do not satisfy a [[mathematical]] condition of [[probabilistic]] independence. In loose usage, correlation can refer to any departure of [[two]] or more random variables from independence, but technically it refers to any of several more specialized [[types]] 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 [https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient Pearson correlation coefficient], which is [[sensitive]] only to a [[linear]] relationship between two [[variables]] (which may exist even if one 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.[https://en.wikipedia.org/wiki/Correlate] |
[[Category: Statistics]] | [[Category: Statistics]] | ||
[[Category: Mathematics]] | [[Category: Mathematics]] | ||
Latest revision as of 23:43, 12 December 2020
Origin
Back-formation from co-rrelation
Definitions
- 1: either of two things so related that one directly implies or is complementary to the other (as husband and wife)
- 2: a phenomenon that accompanies another phenomenon, is usually parallel to it, and is related in some way to it <precise electrical correlates of conscious thinking in the human brain
Description
In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence.
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. In this example there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling; however, statistical dependence is not sufficient to demonstrate the presence of such a causal relationship.
Formally, dependence refers to any situation in which random variables do not satisfy a mathematical condition of probabilistic independence. In loose usage, correlation can refer to any departure of two or more random variables from independence, but technically it refers to any of several more specialized types 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 sensitive only to a linear relationship between two variables (which may exist even if one 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]
