Spearman Rank Correlation/Spearman's rho/Spearman Correlation - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Spearman's rank correlation coefficient, often referred to as Spearman's rho or simply the Spearman correlation, is a non-parametric measure of statistical dependence between two variables. It assesses the strength and direction of the monotonic relationship between two variables, which means it is used to determine whether there is a consistent, systematic association between the variables without assuming a linear relationship. Spearman rank correlation works in the way below:
Where:
Here is how it works:
The resulting Spearman correlation coefficient can range from -1 to 1:
Spearman's rank correlation is useful when the assumptions of parametric correlation methods (such as Pearson's correlation) are not met, or when dealing with ordinal or ranked data. It's also less sensitive to outliers than some other correlation measures, making it robust in certain situations. ============================================ The script below compares datasets in FolderOne with datasets in other folders, calculates the Spearman Rank Correlation coefficients to measure their relationships, identifies the best matches, and provides an overall correlation value for each folder. The focus is on finding the most correlated pairs of datasets and assessing the overall correlation between datasets in different folders. Code: ============================================
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