Correlation coefficient

Correlation coefficient is a statistical measure that calculates the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement.

Overview[edit | edit source]

A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation. A correlation of 0.0 shows no linear relationship between the movement of the two variables.

Types of Correlation Coefficients[edit | edit source]

There are several types of correlation coefficients, but the most popular is the Pearson Correlation Coefficient. This measures the strength and direction of the linear relationship between two variables.

Pearson Correlation Coefficient[edit | edit source]

The Pearson correlation coefficient, also called Pearson's r, is a measure of the strength and direction of association between two continuous variables. It is defined as the covariance of the two variables divided by the product of their standard deviations.

Spearman's Rank Correlation Coefficient[edit | edit source]

Spearman's Rank Correlation Coefficient is a non-parametric test that is used to measure the degree of association between two variables. It is the statistical dependence between the rankings of two variables.

Kendall Rank Correlation Coefficient[edit | edit source]

The Kendall Rank Correlation Coefficient is a measure of ordinal association. It is a statistic used to measure the ordinal association between two measured quantities.

Applications[edit | edit source]

Correlation coefficients are used in statistics to measure how strong a relationship is between two variables. There are several types of correlation coefficient: Pearson’s correlation (also called Pearson’s R) is a correlation coefficient commonly used in linear regression.

References[edit | edit source]

Correlation coefficient Resources
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