How to use pca results for linear regression
In general, I would suggest to use a regularization technique for reducing the dimensionality ofa data set in linear regression cases. Please refer to L1 regularization.

If you want to decrease the number variables using PCA, you should look at the lambda values that describe the variations in the principle components, then, select the a few components with the largest corresponding lambda values (eg the first four). 

Note:


Do a scaling if necessary.
Sometimes the very first component is not very relevant and can be eliminated.