"Explore and understand various correlation measures such as Pearson, Spearman, and Kendall through detailed explanations, mathematical derivations, and practical examples.
Welcome to the Understanding Correlation Measures repository! This repository provides explanations and practical insights into three common correlation measures: Pearson, Spearman, and Kendall. Each measure has its own characteristics, significance, and use cases, making it suitable for different types of data and analytical goals.
- Significance: Measures the strength and direction of the linear relationship between two continuous variables.
- Use Case: Useful when analyzing linear relationships between variables, such as height and weight.
- Example: If the Pearson correlation coefficient is close to 1, it indicates a strong positive linear relationship. If it's close to -1, it indicates a strong negative linear relationship. A value near 0 suggests no linear relationship.
- Significance: Measures the monotonic relationship between variables, regardless of the linearity.
- Use Case: Suitable for ordinal or non-linear relationships where the data may not meet the assumptions of Pearson correlation.
- Example: If the Spearman rank correlation coefficient is close to 1, it indicates a strong monotonic relationship. A value near 0 suggests no monotonic relationship.
- Significance: Measures the ordinal association between variables based on the number of concordant and discordant pairs.
- Use Case: Suitable for smaller datasets or ordinal data where the ranks of observations are important.
- Example: If the Kendall Tau correlation coefficient is close to 1, it indicates a strong agreement in the ordering of observations. A value near 0 suggests no agreement in the ordering.