https://github.com/jhaayush2004/understanding_correlations
"Explore and understand various correlation measures such as Pearson, Spearman, and Kendall through detailed explanations, mathematical derivations, and practical examples.
https://github.com/jhaayush2004/understanding_correlations
kendall-correlation-coefficient pearson-correlation spearman-rank-correlation
Last synced: 11 months ago
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"Explore and understand various correlation measures such as Pearson, Spearman, and Kendall through detailed explanations, mathematical derivations, and practical examples.
- Host: GitHub
- URL: https://github.com/jhaayush2004/understanding_correlations
- Owner: jhaayush2004
- License: mit
- Created: 2024-06-10T12:15:50.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-10T12:48:19.000Z (about 2 years ago)
- Last Synced: 2025-02-16T00:26:15.592Z (over 1 year ago)
- Topics: kendall-correlation-coefficient, pearson-correlation, spearman-rank-correlation
- Homepage:
- Size: 5.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Understanding_Correlations
"Explore and understand various correlation measures such as Pearson, Spearman, and Kendall through detailed explanations, mathematical derivations, and practical examples.
# Understanding Correlation Measures
## Overview
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.
## Correlation Measures
### 1. Pearson Correlation Coefficient
- **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.
### 2. Spearman Rank Correlation
- **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.
### 3. Kendall Tau Correlation
- **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.
## Also Visit
#### Pearson Correlation Coefficient : https://shorturl.at/1NH1G
#### Spearman Rank Correlation Coefficient : https://shorturl.at/8Ydok
#### Kendall Tau Correlation Coefficient : https://shorturl.at/2oHP4