and p = 6 variables was analysed to reduce its dimensionality. As part of Principal Component Analysis, the following varia 399.019 49.66 -1.793 49.66 38.529 -6.952 -1.793 -6.952 36.051 1.7 8.333 16.583 1.733 2.409 7.873 12.142 -4.986 2.841 1.7 1.733 12.142 37.132 -4.093 -0.04 8.333 2.409 -4.986 -4.093 41.277 -3.757 16.583 7.873 2.841 -0.04 -3.757 45.062, the eigenvalue ₁ of Σ. This eigenvalue is located in the position (4, 4) of the matrix A and is simultaneously the sample v variability explained by the Principal component PC4. The number you write should be between 0 and 100 and you should i ated to da, one singular eigenvalue of the data matrix X. Compute and write the value of d4. s been set at 80\%. How many principal components must you select? Write your answer.

and p = 6 variables was analysed to reduce its dimensionality. As part of Principal Component Analysis, the following varia 399.019 49.66 -1.793 49.66 38.529 -6.952 -1.793 -6.952 36.051 1.7 8.333 16.583 1.733 2.409 7.873 12.142 -4.986 2.841 1.7 1.733 12.142 37.132 -4.093 -0.04 8.333 2.409 -4.986 -4.093 41.277 -3.757 16.583 7.873 2.841 -0.04 -3.757 45.062, the eigenvalue ₁ of Σ. This eigenvalue is located in the position (4, 4) of the matrix A and is simultaneously the sample v variability explained by the Principal component PC4. The number you write should be between 0 and 100 and you should i ated to da, one singular eigenvalue of the data matrix X. Compute and write the value of d4. s been set at 80\%. How many principal components must you select? Write your answer.