Suppose we selected 3 Principle Components from Principal Components Analysis on a 7
dimensional data cloud. We have decided not to scale the data. We have found the following 3
axes representing 90% of the variability. If the scores of a particular observations on these three
principle components are 0.7, 2.1, and 1.5, we expect the (centred) original species A value to be
close to:We frequently plot our principle component scores in various ways. Which of the following
would NOT be useful? An image plot of the correlation matrix of the principal component scores. A
pairs plot of the first several scores. A scatter plot of the first 2 scores using colors to represent a
categorical variable in our dataset. A rotatable 3D plot of the first three scores..
Suppose we selected 3 Principle Components from Principal Co.pdf
1. Suppose we selected 3 Principle Components from Principal Components Analysis on a 7
dimensional data cloud. We have decided not to scale the data. We have found the following 3
axes representing 90% of the variability. If the scores of a particular observations on these three
principle components are 0.7, 2.1, and 1.5, we expect the (centred) original species A value to be
close to:We frequently plot our principle component scores in various ways. Which of the following
would NOT be useful? An image plot of the correlation matrix of the principal component scores. A
pairs plot of the first several scores. A scatter plot of the first 2 scores using colors to represent a
categorical variable in our dataset. A rotatable 3D plot of the first three scores.
