Comp. Statistics Quiz 2

PCA reduces the dimension by finding a few_

• Hexagonal linear combination
• Orthogonal linear combinations
• Octagonal linear combination
• All of these

________ basically known as characteristic roots. It basically measures the variance in all variables which is accounted for by that factor

• Eigen value
• Linear value
• Eigen vector
• None of these

If P1 and P2 are first and second principal components respectively then

• Variance P1 is higher than P2
• Variance of P2 is higher tha P1
• Both variance are equal
• variance cannot be compared

Euclidean distance measure is

• A stage of the KDD process in which new data is added to the existing selection.
• The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them
• The distance between two points as calculated using the Pythagoras theorem
• None of these

The total variation explained by Principal Components is

• The total variation explained by original variables
• Sum of eigen values
• Sum of eigen vector
• Sum of eigen values+Sum of eigen vector

In 5th principal component, which of the following is true

• cov(y1,y5)=0
• cov(y1,y2)=0
• cov(y1,y3)=0
• cov(y5,y2)=

Two random Variables are uncorrelated then

• In single linkage method average distance is considered
• In single linkage method min distance is considered
• In complete linkage method max distance is considered
• None of these

Which of the following are distance measure

• All of these
• Euclidean
• Dice Distance
• Canbero Distance

Assumptions of orthogonal factor model are

• Expectation of F is zero
• cov(F) is an identity matrix
• expectation of error matrix is zero.
• all of these

Types of factor rotations are

• Oblique Rotation
• Orthogonal Rotation
• Varimax Rotation
• All of these