Construction of an index using Principal Components Analysis

This video gives a detailed explanation on principal components analysis and also demonstrates how we can construct an index using principal component analysis.
Principal component analysis is a fast and flexible, unsupervised method for dimensionality reduction in data. It is also used for visualization, feature extraction, noise filtering, dimensionality reduction
The idea of PCA is to reduce the number of variables of a data set, while preserving as much information as possible.
This video also demonstrate how we can construct an index from three variables such as size, turnover and volume

[@nathanarban4332]

Hello Sir, I'm from indonesia. I'm currently doing my thesis research to create a new index. Your video really provides new knowledge and useful information. Thank you for this video

[@failedupscaspirant7938]

hey can you pls help me for the same

[@tebohomosikari4067]

thanks for simplifying this.😍😍

[@elviskwameofori4536]

great work bro

[@riyath5594]

Dear Sir
Thank you very much for this nice explanation.
I'm waiting for your next video

[@oluwagbangu]

Thanks for reaching out to me
My next video will be out this week.
I will be explaining how to analyse a survey using Nvivo
Thanks

[@deeg1385]

Thank you Sir

[@yapshen2076]

Thank you very useful. I want to construct an index using two-step PCA. This is the first step. Is it possible to shed light on 2 things:
1. How to carry out 2nd step when I get the results (prediction) from each dimension?
2. How to convert the result (prediction) to an index between 0 and 1

[@elviskwameofori4536]

(1). Will I have issues if I don't standardize my data? (2) Can I also directly use log values while generating my index

[@Mimi-nr6jx]

why did you choose the covariance matrix rather than the correlation matrix?

[@n_a05]

how does we know the factor scores in state?

[@barhom11eriqat98]

how we can I create index by PCA using SPSS

[@oluwagbangu]

Watch out for my next video