For PCA using the eigenvectors of the covariance matrix, what is the meaning of the eigenvalues?

When doing a PCA using the largest eigenvectors associated with the largest eigenvalues, what does the values of the eigenvalues means?

Example:

The 2 largest eigenvectors of my dataset are these:

1 - [ 6.62257875e-01 -1.63390189e-01 7.31243512e-01 -1.13386505e-04 -9.65364160e-05 1.02781966e-03]

2 - [ 3.31219165e-01 -8.11563370e-01 -4.81309165e-01 4.26282496e-04 3.70709031e-05 2.55801611e-04]

How can I associate these values with the large dispersion of the data in a plot?

[John D'Errico]

If you are using PCA, perhaps it is time to do some reading about it? After all, it takes a significant amount if time to write a complete tutorial on PCA, which you clearly need if you are asking basic questions.

So why not just read about PCA, and learn what it means, written by someone who has spent a great deal of time already doing exactly what you need?

https://en.wikipedia.org/wiki/Principal_component_analysis

[the cyclist]

In addition to the Wikipedia article that John linked, you might find this answer of mine helpful in understanding the basics of PCA better.

[caio amorim]

well, thank you for not being direct. not answering would be better

[John D'Errico]

Rather demanding are you? Sorry, but yours is not even a question about MATLAB. I gave you a link that explains PCA. Do you expect us to teach a statistics course in this forum? I'll give you an entire textbook on the subject. I've seen courses taught.

https://www.amazon.com/Users-Guide-Principal-Components/dp/0471471348

Do some reading.