Principal component regression (PCR) - explained

1. Introduction
2. Collinearity (01:07)
3. How PCR works (03:46)
4. Predict (08:30)
5. Extract components(08:50)
6. PCR vs PLS (13:17)

[asiyazhao]

Thank you very much for the video! Very clearly explained!

[georgeyandem]

You are the best in my opinion. And I'm not bluffing

[tedransom]

Thank you so much for this video!
It really helped me to understand PCR.

[mipchen]

Thank you very much; very well explained!

[ToanNguyenVan-reoz]

thank you so much!
It really helped me <3

[younique]

Thank you! I know PCA regression is an alternative of a multivariate regression to deal with multicollinearity. So, is PCA regression always better for prediction than a standard linear regression?

[MIZRAIM]

Thanks a lot. I have got a question: that's right, PCR is designed to resolve regression tasks. And what about classification? For instance, two classes are assigned as an output. The variables are reduced to the principal components, ok. Is it correct to use the obtained PCs for classification instead of original variables afterwards?
Thanks for consultation!

[mustafahelal]

If PC1 is assumed as explatory variable and use least square then
B0 is -75 and B1 is 1.85

How you get B0 = -83.9 and B1 = 1.932

[bbnn]

Here bellow is my model obtained after standardization ((X-mean)/stdev) of your sample data and PC1 and PC2 calculation:

Y= -3.75Chol+5.20Age+375

[mingxiuwang]

If I want to fit a linear regression model after pca, do I need to regress y on centered score or centered y on centered score? I have been confused about this question.

[manishpanchasara]

I tried to purchase books (ePDFs) but it seems that the payment system is not working properly.