Calculation of Principal Component Analysis

I am strugling with PCA stuff.

So for example I have :

    Data=100*3
    substractdata=data-mean (the size will be same 100*3)
    covariance=3*3
    EigenVector=3*3
    EigenValue=3*3

And to do reduction to our data, we have to eliminate the number of eigen value and eigen vector based on k

    For example k=2

so the number of

    EigenValue will become 2*2
    EigenVector = 2*2

    1st ques: is that right??

And then we have to project out matrix

    project=EigenVector (which is 2*2) *substractdata (100*3)

    2nd ques: How we can calculate this, because the size of EigenValue and substractdata   
    are different??

And another question,

    3rd ques: if we want to use the reduction data we should use the project?

    4th ques: And if we want to show the Principal Components (which is first and second  
    columns of eigen vector), we have to plot that Principal Components along with the Data   
    (initial data) or with substractdata??

Sorry if the questions are so much,, but I need to know that answer!!

Thanks alot for any helps