How does Pearson's Correlation Coefficient differ from Spearman's Rank Correlation?
- Pearson's correlation coefficient cannot be negative, Spearman's can
- Pearson's correlation coefficient is non-parametric, Spearman's is parametric
- Pearson's correlation coefficient is used for ranked data, Spearman's is not
- Pearson's correlation coefficient measures linear relationships, Spearman's measures monotonic relationships
Pearson's correlation coefficient measures linear relationships, while Spearman's Rank Correlation measures monotonic relationships. Monotonic relationships are ones where the variables tend to change together, but not necessarily at a constant rate. Pearson's Correlation is used when the data is normally distributed, whereas Spearman's Rank Correlation is used when the data does not assume normal distribution.
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