Page 78 - Statistics and Data Analysis in Geology
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Statistics and Data Analysis  in  Geology - Chapter 3

             are on the diagonal. As a consequence of  these special conditions, the eigenvalues
             will always be real numbers  that  are equal to or  greater than  zero.  As you can
             verify by checlung these examples, the sum of the eigenvalues of a matrix is always
             equal to the sum of  the diagonal elements, or the  trace, of  the original matrix.
             In a correlation matrix, the diagonal elements are all equal to one, so the trace is
             simply the number of variables. The product of the eigenvalues will be equal to the
             determinant of  the original matrix. Most (but not all) of the eigenvalue operations
             we will consider later will be applied to correlation or covariance matrices, so these
             special results will hold true in most instances. The methods just developed can be
             extended directly to n x n matrices, although the procedure becomes increasingly
             cumbersome with larger matrices.


              E igenvect ors

             We can examine the correlation matrices we calculated for the Istrian vineyard data
             to gain some insight into the geometrical nature of eigenvectors. First, consider the
             2 x 2 matrix



             with eigenvalues
                                         A1  = 1.28   A2  = 0.72
                 Substituting the first eigenvalue into the original matrix gives

                                  1 - 1.28   -0.28  ] = [ -0.28  -0.28  1
                                   -0.28   1 - 1.28     -0.28  -0.28

             whose solution is the eigenvector
                                             [ 4 = [ -:]




                 In Figure 3-1,  we can interpret this eigenvector as the slope of  the major semi-
             axis of  the enclosing ellipse. If we regard the elements of  the eigenvector as coor-
             dinates, the first eigenvector defines an axis whch extends from the center of  the
             ellipse into the second quadrant at an angle of  135". The length is equal to the first
             eigenvalue, or 1.28.
                 Turning to the second eigenvalue, A2  = 0.72, the equation set is


                                  1 - 0.72   -0.28  ] = [ 0.28   -0.28  1
                                   -0.28   1 - 0.72     -0.28   0.28

             whose solution gives the second eigenvector:
                                             [::I   = [ :]




             In Figure 3-1,  ths will plot as the vector drection l/l = 45", perpendicular to the
             major semiaxis of  the ellipse. Its magnitude or length is 0.72.

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