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In statistics, the pearson correlation coefficient (pcc) [a] is a correlation coefficient that measures linear correlation between two sets of data Pearson correlation coefficient or pearson’s correlation coefficient or pearson’s r is defined in statistics as the measurement of the strength of the relationship between two variables and their association with each other. 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. Pearson’s correlation (also called pearson’s r) is a correlation coefficient commonly used in linear regression. The pearson correlation coefficient (r) is the most common way of measuring a linear correlation
Pearson’s correlation coefficient, a measurement quantifying the strength of the association between two variables
Pearson’s correlation coefficient r takes on the values of −1 through +1 Values of −1 or +1 indicate a perfect linear relationship between the two variables, whereas a value of 0 Pearson correlation coefficient (pcc) is used for measuring the strength and direction of a linear relationship between two variables It is important in fields like data science, finance, healthcare, and social sciences, where understanding relationships between different factors is important.
The pearson correlation coefficient measures the degree of linear relationship between x and y and 1 ≤ r p ≤ + 1, so that r p is a unitless quantity, i.e., when you construct the correlation coefficient the units of measurement that are used cancel out. In terms of strength, the correlation coefficient reflects how closely the data points cluster around a line of best fit Correlation coefficients are used to measure how strong a relationship is between two variables There are several types of correlation coefficient, but the most popular is pearson’s
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