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PEARSON CORRELATION COEFFICIENT (PEARSON R)
DEFINITION
THE DEFINITION OF PEARSONS R
measures the strength of the linear relationship
between two variables
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name
other definitions
The Pearson correlation coefficient (PCC) also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC) or the bivariate correlation is a measure of the linear correlation between two variables X and Y. According to the Cauchy–Schwarz inequality it has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. It is widely used in the sciences.
DEVELOPED BY
It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s and for which the mathematical formula was derived and published by Auguste Bravais in 1844.
purpose
purpose
the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance.
Pearson's Correlation Coefficient. Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables.
Title
formulas
equation
1
substitute
N = number of pairs of scores
Exy = sum of the products of paired scores
Ex = sum of x scores
Ey = sum of y scores
Ex² = sum of squared x scores
Ey² = sum of squared y scores
interpretation
interpretation
The correlation coefficient ranges from −1 to 1. A value of 1 implies that a linear equation describes the relationship between X and Y perfectly, with all data points lying on a line for which Y increases as X increases. A value of −1 implies that all data points lie on a line for which Y decreases as X increases. A value of 0 implies that there is no linear correlation between the variables.