The best known is the Pearson product-moment correlation coefficient.

The Pearson product-moment correlation coefficient is sometimes applied to finance correlations.

Pearson product-moment correlation coefficient, commonly referred to as "Pearson's r"

Third, a zero Pearson product-moment correlation coefficient does not necessarily mean independence, because only the two first moments are considered.

These take the same roles as the ordinary moments with corresponding names in the specification of the Pearson product-moment correlation coefficient.

Another approach parallels the use of the Fisher transformation in the case of the Pearson product-moment correlation coefficient.

Common measures include the Mutual Information, Pearson product-moment correlation coefficient, and the inter/intra class distance.

The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient, or "Pearson's correlation."

For such traits, more complicated calculations can be used or pairs of measures can be compared with a Pearson product-moment correlation coefficient.

In principle, any correlation coefficient could be used, but normally the Pearson product-moment correlation coefficient is used.

The value of the Pearson product-moment correlation coefficient is adjusted with the Spearman-Brown prediction formula to correspond to the correlation between two full-length tests.

The following algorithm (in pseudocode) will calculate Pearson product-moment correlation coefficient correlation with good numerical stabilityRonald A. Thisted (1988).

The Pearson product-moment correlation coefficient derived from an XY scatterplot of input behaviours and output states can quantify the effectiveness of various treatments.

Statistical methods include the Pearson product-moment correlation coefficient, the analysis of variance, multiple linear regression, logistic regression, structural equation modeling, and hierarchical linear modeling.

In case of a single regressor, fitted by least squares, R is the square of the Pearson product-moment correlation coefficient relating the regressor and the response variable.

The common measure of dependence between paired random variables is the Pearson product-moment correlation coefficient, while a common alternative summary statistic is Spearman's rank correlation coefficient.

While the Fisher transformation is mainly associated with the Pearson product-moment correlation coefficient for bivariate normal observations, it can also be applied to Spearman's rank correlation coefficient in more general cases.

The correlation between scores on the first test and the scores on the retest is used to estimate the reliability of the test using the Pearson product-moment correlation coefficient: see also item-total correlation.

The most common of these is the Pearson product-moment correlation coefficient, which is a similar correlation method to Spearman's rank, that measures the "linear" relationships between the raw numbers rather than between their ranks.

Main articles: Pearson product-moment correlation coefficient, Spearman's rank correlation coefficient Either Pearson's or Spearman's can be used to measure pairwise correlation among raters using a scale that is ordered.

The tetrachoric correlation should not be confused with the Pearson product-moment correlation coefficient computed by assigning, say, values 0 and 1 to represent the two levels of each variable (which is mathematically equivalent to the phi coefficient).

In the same way if y always decreases when x increases, the rank correlation coefficients will be 1, while the Pearson product-moment correlation coefficient may or may not be close to 1, depending on how close the points are to a straight line.

In statistics, the Pearson product-moment correlation coefficient (sometimes referred to as the PPMCC or PCC, or Pearson's r) is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and 1 inclusive.

Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, a measure of the strength and direction of the linear relationship between two variables that is defined as the (sample) covariance of the variables divided by the product of their (sample) standard deviations.

However, this view has little mathematical basis, as rank correlation coefficients measure a different type of relationship than the Pearson 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.