Tradução e análise de palavras por inteligência artificial
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Pearson correlation coefficient - tradução para Inglês
![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''.](https://commons.wikimedia.org/wiki/Special:FilePath/Pearson correlation and prediction intervals.svg?width=200 "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''.")
Ex: The Pearson correlation coefficient has been calculated to find out the correlation and to test the null hypothesis that there is no correlation among publishing in journals, citing from journals and use of journals by a defined set of researchers.
Ex: The choice of call number and its correlation with physical documents is the responsibility of the cataloger.
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* bear + correlation with = tener correlación con
* correlation analysis = análisis de correlación
* correlation coefficient = coeficiente de correlación
* correlation study = estudio comparativo
* cosine correlation coefficient = coeficiente de la correlación de los cosenos
* cosine correlation function = función de la correlación de los cosenos
* Pearson correlation coefficient = coeficiente de correlación de Pearson
* Pearson's correlation = correlación de Pearson
* Pearson correlation coefficient = coeficiente de correlación de Pearson
* Pearson Product Moment Test = ?
* Pearson's correlation = correlación de Pearson
Definição
Wikipédia
In statistics, the Pearson correlation coefficient (PCC, pronounced ) ― also known as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), the bivariate correlation, or colloquially simply as the correlation coefficient ― is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations. As a simple example, one would expect the age and height of a sample of teenagers from a high school to have a Pearson correlation coefficient significantly greater than 0, but less than 1 (as 1 would represent an unrealistically perfect correlation).
