You are a life saver.. Such a beautiful explanation... Such a clarity... Broke my head to understand dimension reduction... You made it crystal clear. Thanks again.
I wish I could meet your Parents and say to them Thank You Very much for bring such a great person to this world!! May God Bless your Mother and your Father!
Wow! Thank you for this very informative presentation. I now feel i understand PCA a lot better. I can't believe you only have 18 views and one like. Saved in Favorites! Thank you for your effort!
Sorry for commenting again (last comment, i promise). How about similar videos on RDA and NMDS as multivariate flavours ? I think they're under explained on youtube and an excel tutorial like this one would be amazing! Thank you again!
great explanation! I have a question though... if my data is 500 by 960 and my eigen vectors matrix is 960 by 41 (because I'm reducing the dimension from 960 to 41), how do I do the multiplication? am I doing this incorrectly?
This is a terrible explanation. You have not shown how you actually calculated the eigenvectors nor the eigenvalues. Also, your correlation matrix has to be wrong.
You are a life saver.. Such a beautiful explanation... Such a clarity... Broke my head to understand dimension reduction... You made it crystal clear. Thanks again.
I wish I could meet your Parents and say to them Thank You Very much for bring such a great person to this world!!
May God Bless your Mother and your Father!
Wow! Thank you for this very informative presentation. I now feel i understand PCA a lot better. I can't believe you only have 18 views and one like. Saved in Favorites! Thank you for your effort!
Thank you so much for very excellent explanation. I had been worrying about PCA to apply for my research. I am fine with this now
Thank you Teacher. Truly practical way of explaining PCA....
Very good explanation. A suggestion, at 3:25, reducing from 8 to 6, instead of increasing 8 to 18 would make your point much clearer.
Love from Nepal 🇳🇵🇳🇵🇳🇵. Very Informative.
What a great explanation, it helps me alot sir, finally, i got clear idea of PCA and its working.
Hey I didn't get how you found the vectors value:
A: 0,42 B: 0,64 and C:0,64
etc
amazing explanation! you made it so simple.
I am happy to be the first to comment! Many more will come in time, i'm sure. Good luck!
Very nice sir. Big concept in simple language using excel is really commendable.
Thank you so much teacher for this video 😁
Sorry for commenting again (last comment, i promise). How about similar videos on RDA and NMDS as multivariate flavours ? I think they're under explained on youtube and an excel tutorial like this one would be amazing! Thank you again!
Thank you for your comments and suggestions.
Hi, What if your mean is not equal to 0 in step 2? What could be the cause of this?
A very nice explanation! Thanks a lot
Very helpful! Please upload videos on how to organise data in spreadsheet for polychoric pca on socioeconomic data.
What a fascinating and clear explanation/// Thank you so much.
Really good explanation, thanks a lot.
Excellent man, keep it up
thank you sooooo much! it's so clear
great explanation! I have a question though... if my data is 500 by 960 and my eigen vectors matrix is 960 by 41 (because I'm reducing the dimension from 960 to 41), how do I do the multiplication? am I doing this incorrectly?
Super Cool !!! 👌
This is very useful
I couldnt get a thing when he started talking about vectors
Thank you.
Can we do a row echelon form for solving the vector?
Amazing boy
Thank you for video! May I ask how did you calculated the eigenvectors?
great question
Pca require replicated data or mean values?
Wrong example. You made a basic mistake in step 3.
2nd row should be ba bb bc, 3rd row should be ca cb cc? the result of ba is equal to about so it doesn't matter?
hi can you make a pls/nipals version of pca in excel too?
i lost...how did u get 1.52 , 0.48 , and 0.00??
The value of 2nd and 3rd eigen vector is not correct
How did you calculate eigen values? Please explain it through excel
fairly straighforward. please refer to the last tab of the workbook
This is a terrible explanation. You have not shown how you actually calculated the eigenvectors nor the eigenvalues. Also, your correlation matrix has to be wrong.