What is Rank Correlation? | Spearman's Rank Correlation | Theory + Hands-on | Assumptions

📈 In this video, we'll explore the principles of Spearman's rank correlation, its practical applications, and how to calculate it both manually and using Python.
We begin by discussing the cases where Spearman's Rank correlation is supposed to be used instead of the popular Pearson's correlation. We'll guide you through the step-by-step process of manually computing Spearman's rank correlation coefficient, providing you with a hands-on experience to deepen your understanding. 📝
Next, we'll transition to the practical implementation of Spearman's rank correlation in Python, demonstrating how to utilize the functionality provided by libraries like scipy to calculate the coefficient efficiently. Witness the seamless integration of statistical theory with Python programming as we showcase the practical application of Spearman's rank correlation. 🖥️
We'll also discuss the assumptions underlying Spearman's rank correlation, emphasizing the conditions necessary for valid interpretation of results. By the end of this insightful journey, you'll have a solid grasp of Spearman's rank correlation and the skills to apply it confidently in your analyses. 🧠
Happy Learning 🌟

[@matthewsheeran]

Interesting. I was always looking for a non-linear correlation. And ill be waiting for the next video!

[@prosmartanalytics]

Thank you! Stay tuned 😊