Spearman Correlation - Simply Explained

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[@sophiechambers8300]

You explained this so well! I've been trying to understand this topic for a week and I FINALLY feel confident. Thank you!

[@SeverSava]

Thanks for taking the time to explain it. I do appreciate more when the equation is presented in all its details! 🙏

[@ritvikmath]

Glad it was helpful!

[@aelloro]

Dear author, thank you so much for your hard work and time! It helps many of us!

[@aelloro]

Also waited for examples of usage, but only few words about it were given =)

[@galenseilis5971]

I recently wrote a blog post that discussed a similar point of view. Sometimes we are more interested monotonic relationships in general rather than linear relationships in particular.

[@ritvikmath]

Awesome to hear others have been thinking about this

[@norabelkhayatte7161]

Thank you for the clear and unassuming teaching style, I really understand clearly now. Greatings from Turkey.

[@tksnail6837]

exceptional explanation, I love how you explain the issue before explaining the "solution"

[@tmpcox]

Such an elegant explanation!❤

[@asjsingh]

Great explanation. To put it another way, spearman rank correlation is great for hypothesis testing only. I.e. if you just want to know the strength of correlation. However, it won't tell you what bowling score someone will have if you know their basketball score i.e. estimates are on the ranks, not the actual values.

[@adebayofati7745]

That which you say is what Linear regression is. They are closely related. The aim of Correlation usually is to assign a numerical value between -1 and 1 showing linearity. Regression is to predict what bowling score someone will have if you know their basketball score, while correlation is to verify how closely related they are. Think of it like a Corr. is used to check if we can use Ln. Reg. on this data.

[@josejames7065]

Nice videos. Please dont ever compromise the mathematical rigour, even for 50k extra views. Can you please consider a video on how you handle multicollinearity, also what you do when you encounter multiple inter-correlated features.

[@ritvikmath]

Thanks for the kind words and suggestion!

[@Andy-qi5nh]

Best explanations

[@lolikpof]

Next make a video about Kendall's Tau Rank Correlation ;)

[@ritvikmath]

Hey great idea!

[@galenseilis5971]

@@ritvikmath If you want a deeper dive, look into Kendall's general correlation coefficient. It generalizes some of these familiar correlation coefficients. Not sure if it is practical enough to mention for your channel, but you might enjoy seeing the pieces come together.

[@Hobbies_forkids]

Simple thought: it can be nice to see spearman correlation if let's say the ranking on the bowling was out of complete match with the basketball, i.e. 13245....

[@kenkoonwong2166]

Is the formula provided in the video the same as 1 - 6∑ i=1 -> n (d^2) / ( n / (n^2-1)) ? Where d: rank(x_i) - rank(y_i). thanks for the video!​

[@adebayofati7745]

How can I get your email please