The correct answer is C. PCA works better if there is a linear structure in the data and if variables are scaled in the same unit.
PCA is a dimensionality reduction technique that projects data points onto a lower-dimensional space while preserving as much of the variance in the data as possible. This is done by finding a set of orthogonal axes (principal components) that maximize the variance of the data along each axis.
PCA works best when the data has a linear structure. This means that the data points should be clustered together in a way that can be represented by a straight line. If the data has a curved structure, PCA will not be as effective at reducing the dimensionality of the data.
PCA also works best when the variables are scaled in the same unit. This means that the values of each variable should be on the same scale. If the variables are not scaled in the same unit, PCA will not be able to accurately represent the variance in the data.
In conclusion, PCA works better if there is a linear structure in the data and if variables are scaled in the same unit.