Question

total variance explained by each factor is represented by​

Answer 1

Answer:

Eigenvalues show how much variance can be accounted for overall by a particular primary component.

Explanation:

• Varying from the average or mean is measured statistically by variance.
• Though in theory they might be either positive or negative, in actuality they always explain positive variance.
• It's a favourable sign if the eigenvalues are bigger than zero.
• To calculate it, subtract the mean from each value in the data set, square each difference to make it positive, and then divide the sum of the squares by the total number of values in the data set.
• An indicator of how much of the common variance of the observable variables a factor explains is its eigenvalue.
• More variance is explained by any factor with an eigenvalue of one than by a single observable variable.

#SPJ3

Answer 2

Concept :

Varying is a measure of variability in a set of data. It is calculated by taking the average of squared deviations from the mean in a set of data.

In relation to the average in your data set, the variance tells you the extent to which the data is spread out. The more spread out the data is, the greater the variance is in relation to the average.

Explanation :

As the name implies, variance measures the degree of variation from the average or mean in statistics. The variance is calculated by taking the differences between each number in the data set, squaring the differences to make the differences positive, and finally dividing the sum of squares by the number of values in the data set.

Hence total variance explained by each factor is represented by​  Eigenvalues

#SPJ3