simply say: Covariance: tells if two variables are positively or negatively correlated Correlation: tells how strongly variables are correlated and in which direction
THANK YOU! I am NOT a data or math person and have been trying to wrap my head around this statistics test and what it does for a communications methodology course. All I could find is how to do it, but not how to figure out if it was the right test for my data. Now I understand and can see if it works better than the Chi-Square for my data.
superb.But one thing is if the correlation between two independent variables is 1 rhen they can be treated as 1 variable and hence we can drop one og the .So this is the importance of Pearson correlation.This is the importance of Correlation in data science. Did not know that.Now its clear.Thanks Krish
This is one super good explanation of the relationship between Features. Its a beauty of you sessions, talking about Stats w.r.t to its play in ML Algorithms (feature engineering). You are guru to me in Data Sciences.
Hi, your explanation about concept is nice. One suggestion from my side is explain how to implement those concept with python after the theory part. Please make video on tests used to find nonlinear relationship and how to find relationship between categorical dependent variable and multiple categorical and continuous independent variables. This would help many people I guess. Thanks for useful content.
Your explanation are clear, to the point and short … great job 👏… You have a gift in teaching… please make more of these short videos about different topics
Great video. Thanks for posting such lucid material. +1 for your expressions It will be great if you could also give a proof on why dividing by standard deviation gives the strength. I found it a little bit difficult to understand the logical reasoning. In the covariance video, you clearly explained for a +ve relation why does covariance increase and vice versa.
Great One. Would like to see you give more brief example or just statement on how and which theory used in a particular AI/Machine Learning/Tech field and why each theory/concept is important for each field i.e., why Pearson CC is important for ML and how it is used
So happy to come across this video. I have been trying to understand what the Pearson correlation and covariance are and how to use them. Thank you for uploading this video.
i see Superb strength in you as well along with Pearson correlation. If you have time please prepare videos on (t test z test anova chi square) to evaluate ML models. Thank you very much.
Sir in the last example you said X and Y are independent variables and Z is the dependent variable ,then can there be a correlation between X and Y as you said, since they are independent?
it would be better if you compared the correlation with the Pearson Correlation coefficient (PCC) as that has the same range -1 0 +1. I am not expert but It does not make sense with covariance which may have the comparison with variance.
We loved the way u are explaining..Thanks for sharing knowledge 👍..can you also do a playlist on calculus so that it would be helpful for new learners like me??
thw glitter in his eyes and the slight smile when he completes explaining a concept shows his hardwork and dedication
simply say:
Covariance: tells if two variables are positively or negatively correlated
Correlation: tells how strongly variables are correlated and in which direction
You are in love with the word "particular" :)
7:40 what u wrote initially was correct, X increases Y decreases.
yes, but both are actually same lol.
@@saagerbabung5652 you are correct bro😂. It same as saying it's not water it's H₂O.
I just hearing your English.....so clear explanation ❤
THANK YOU! I am NOT a data or math person and have been trying to wrap my head around this statistics test and what it does for a communications methodology course. All I could find is how to do it, but not how to figure out if it was the right test for my data. Now I understand and can see if it works better than the Chi-Square for my data.
U r a gr8 teacher I have searched so many statistics teachers but r u different ways to teach thank u .u hv cleard my confusion God bless u
Sir Thank you so much... I really never understood statistics. You made it so simple to learn..
Kudos to you..
you are one of my favourite math teachers in all time I swear!
I failed to understand what my college professor was trying to deliver!!!..thank you it's so much clear now
Excellent Sir,, nobody can explain much simple than this way, Thank you so much
Feeling good to have mentor like you
I watch ur every video.... when u prepared all this??? Great....
@@therealaspirant1882 No, who told you?
can we compute correlation between two rows of a single matrix??
@@mrunalsrivastava2015 yes. do take transpose first
@@viveknikam5981 yes same I did. Thanks
superb.But one thing is if the correlation between two independent variables is 1 rhen they can be treated as 1 variable and hence we can drop one og the .So this is the importance of Pearson correlation.This is the importance of Correlation in data science. Did not know that.Now its clear.Thanks Krish
Awesome explanation..i am using this test in my research work, for which i need to know everything about this test..uh explained well.
This is one super good explanation of the relationship between Features. Its a beauty of you sessions, talking about Stats w.r.t to its play in ML Algorithms (feature engineering). You are guru to me in Data Sciences.
are you working in the field of data science
Excellent video and explanation - Thanks for making and uploading it.
Very useful and well explained sir because of ur videos I am interested to learn more stat thk u so much sir
Thanks for uploading...was looking in the playlist since morning.
Could you please make a video on sql,how much knowledge should one possess abt sql for data analyst role. Pls help.
sir thank you alot because of you i am able to learn ML easily. Thank you sir .
Thanks for the Video, easily explained the concept and its use.
Good explanation and finally well connected with how to select feature for dropping..
@krish Naik in second graph of correlation X increases and Y decreases.
Thank u so much for explaining this typical topic in simplest way.... Now it's clear to me
1000likes..extra ordinary..very simple explanation
Thanks sir, i am really enjoying the journey to Data Science ,You are awesome
Very very thanks sir . Today I have got lots of money from this video.
really superb way of conveying concepts , Great job sir
Sir your English speaking soft skill superb 👌👌👌👌
please make some videos on reinforcement learning
Hi, your explanation about concept is nice. One suggestion from my side is explain how to implement those concept with python after the theory part. Please make video on tests used to find nonlinear relationship and how to find relationship between categorical dependent variable and multiple categorical and continuous independent variables. This would help many people I guess. Thanks for useful content.
kranthi kumar yes it will be very useful
I showed this video to 4th grade student 👩🎓 now he is teaching exactly same thing for me 😊
Your explanation are clear, to the point and short … great job 👏… You have a gift in teaching… please make more of these short videos about different topics
Thank u Krish. Great explanation.
One kind request.Please post a video on which statistical tests are used in different cases
thank u sir ...great explanation ......u were right at 07:32 too.
Yahh !!
I got it.....this video is very helpful for me 👍👍👍👍👍👍
The way you explained this hell topic was sooooo amazing sir awesome video 👍👍👍👍
wow u need a Nobel price in machine learning
Better than my teachers who done phd's
Wow, thank you. its a clear explanation in short time. God bless you.
Concept clear simply.. thanks you Sir
You lectures are super exciting and the way you explain is amazing.
Hello sir
Can you upload a video on spearman rank correlation coefficient
There is no video belongs to spearman rank correlation coefficient
Krish bhai ... explained beautifully... great 👍
Man I love your work, in particular and general👌
Great video. Thanks for posting such lucid material. +1 for your expressions
It will be great if you could also give a proof on why dividing by standard deviation gives the strength. I found it a little bit difficult to understand the logical reasoning. In the covariance video, you clearly explained for a +ve relation why does covariance increase and vice versa.
sir if youve found an ecplanation.. could you let me know?
Great simple explanation. Thank you.
Dude, thanks for such crystal clear explanations, making the concepts easier to comprehend.
Great One. Would like to see you give more brief example or just statement on how and which theory used in a particular AI/Machine Learning/Tech field and why each theory/concept is important for each field i.e., why Pearson CC is important for ML and how it is used
Good stuff. Generally, Persons Corr can only caputre linear correlations and tend to miss non-linear correlations
What can capture non-linear correlations?
So happy to come across this video. I have been trying to understand what the Pearson correlation and covariance are and how to use them. Thank you for uploading this video.
Hi Sir, you give valuable details of stats and their application which gives an idea how to see the data and implement algorithms .Thanks a lot!
I wish you were my stats prof! Thank you for this simple explanation!
Great sir , absolutely amazing
God of Machine Learning
Hi krish can you make video on t test and p value in regression path
Dude your videos are very very understanding. Thank you a lot ! cheers mate
brilliant explanation krish
Again an amazing explaination. Thank you Sir
Awesome video...
Wht if correlation value of two independent variables is -1 then also can we exclude one variable?
thanks more we like your teaching approache continue
Awesome explanation sir
sir happy new year
love ur teaching sir
great explanation sir, it helped me a lot.
Brilliant explanation mate
Really excited lecture boss...
awesome. thank you so much for the explanation
Awesome stuff Krish Naik.. You make these concepts so easy to understand. Thanks a lot brother.
Thank you!
Good stuff sir and explained in understandable manner 🤝
Thanks , well explained
understood clearly thanks bro
Thank! Great explaination!
Sir for the video @7:44 You can say as the X increases Y decreases. On The Board Its not Much Clear So.
Sir please make a video on power BI...
Love it... I'm am physics man.
Explained beautifully
Thanks bro, I can see the passion and hardwork you put in 👌🏻
Awesome Elaboration
nice one @Krish Naik
Great stuff. Succinct and clear!
thank you so much for this video !!!
i see Superb strength in you as well along with Pearson correlation.
If you have time please prepare videos on (t test z test anova chi square) to evaluate ML models.
Thank you very much.
Sir the formula you're using is it for the population covariance? If yes then it should be capital N rather than a small letter n.
Nicely explained
Thank You Sir...videos are very easy to understand. Can you please share the link to Spearmen Rank CC video...
so much helpful 👍👍
Sir in the last example you said X and Y are independent variables and Z is the dependent variable ,then can there be a correlation between X and Y as you said, since they are independent?
Covarince of say A and B also depends on the arrangement ?
Sir, then why do we measure covariance & is it right to always use Pearson Correlation over covariance ??
Thank you soo much sir😊
it would be better if you compared the correlation with the Pearson Correlation coefficient (PCC) as that has the same range -1 0 +1. I am not expert but It does not make sense with covariance which may have the comparison with variance.
Can you please do a video on Jaccard similarity for user-item matrix for collaborative filtering?
Sir after a specific point of time.. I laughed on your face expression 😂...
We loved the way u are explaining..Thanks for sharing knowledge 👍..can you also do a playlist on calculus so that it would be helpful for new learners like me??
Can someone explain the logic behind how we are getting the coefficient value when we divide the covariance by standard deviation.
www.statisticshowto.com/probability-and-statistics/correlation-coefficient-formula/
above link may help you in understanding
@@sukhmeetkaur7511 Thanks a lot
nice explanation.
Very helpful
u are awesome sir keep doing this