correlation coefficient - définition. Qu'est-ce que correlation coefficient
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Qu'est-ce (qui) est correlation coefficient - définition


correlation coefficient         
NUMERICAL MEASURE OF SOME TYPE OF CORRELATION
Coefficient of correlation; Correlation Coefficient
¦ noun Statistics a number between +1 and -1 calculated so as to represent the linear interdependence of two variables or sets of data.
Pearson correlation coefficient         
  • Several sets of (''x'', ''y'') points, with the correlation coefficient of ''x'' and ''y'' for each set. The correlation reflects the strength and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case the correlation coefficient is undefined because the variance of ''Y'' is zero.
  • Critical values of Pearson's correlation coefficient that must be exceeded to be considered significantly nonzero at the 0.05 level.
  • 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''.
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TYPE OF COEFFICIENT
Pearson r; Pearsonr; Pearson's correlation coefficient; Pearson's r; Pearson's correlation; Product-moment correlation coefficient; Pearson product moment correlation coefficient; Product moment correlation coefficient; Pearson coefficient; Bivariate correlation; Pearson’s correlation coefficient; Pearson's product; Pearson product-moment correlation; Pearson product-moment; Product-moment correlation; Pearson Product Moment Correlation Coefficient; Pearson's coefficient of correlation; Pearson product–moment correlation coefficient; PPMCC; Pearson product-moment correlation coefficient; Pearson correlation; Pearson’s Correlation Coefficient; Pearson's linear correlation coefficient; Circular correlation coefficient; Pearson's product-moment correlation
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.
Spearman's rank correlation coefficient         
  • Chart of the data presented. It can be seen that there might be a negative correlation, but that the relationship does not appear definitive.
NONPARAMETRIC MEASURE OF RANK CORRELATION
Spearman's ρ; Spearman's rho; Spearman's rank; Spearman correlation; Spearman rank correlation; Spearman's correlation; Spearman’s rank correlation; Spearman rank correlation coefficient; Spearman rank test; Spearmans rank correlation; Spearman coefficient of rank correlation; Spearman rho metric; Spearmans Rank; Spearman's r; Spearman's rank correlation; Spearman Rho; Rank difference correlation; Spearman's Rank Correlation Coeficient; Spearman's rank correlation coeficient; Spearman rank; Spearman coefficient; Spearman’s Rank Correlation Test; Spearman-ρ
In statistics, Spearman's rank correlation coefficient or Spearman's ρ, named after Charles Spearman and often denoted by the Greek letter \rho (rho) or as r_s, is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables). It assesses how well the relationship between two variables can be described using a monotonic function.

Wikipédia

Correlation coefficient
A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables.}} The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution.