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Short summary of some common types of correlation:

Pearson Correlation: Pearson correlation coefficient measures the linear relationship between two continuous variables. It ranges from -1 to +1, where -1 indicates a perfect negative linear correlation, +1 indicates a perfect positive linear correlation, and 0 indicates no linear correlation.

Spearman Correlation: Spearman correlation coefficient is a non-parametric measure that assesses the monotonic relationship between variables. It is based on the ranks of the data rather than the actual values. Like Pearson correlation, it ranges from -1 to +1, with similar interpretations.

Kendall's Tau: Kendall's tau coefficient is another non-parametric measure that quantifies the strength and direction of the ordinal association between variables. It also ranges from -1 to +1, where -1 indicates a perfect negative association, +1 indicates a perfect positive association, and 0 indicates no association.

Point-Biserial Correlation: Point-biserial correlation coefficient is used when one variable is continuous and the other is binary. It measures the association between a continuous variable and a dichotomous variable.

Phi Coefficient: Phi coefficient is a correlation measure used for categorical data in a 2x2 contingency table. It is similar to point-biserial correlation but is used for two categorical variables.

Cramer's V: Cramer's V is a measure of association between two nominal variables. It is an extension of the phi coefficient and is suitable for larger contingency tables.

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