Table 6 The table orders the eigenvalues from the larger to small.

From: Biochemical and reproductive biomarker analysis to study the consequences of heavy metal burden on health profile of male brick kiln workers

PC

Eigenvalue

% variance

1

7.86

29.1

2

4.23

15.6

3

2.14

7.95

4

1.51

5.6

5

1.48

5.49

6

1.26

4.66

7

1.11

4.12

8

1.08

4.01

9

0.922

3.41

10

0.838

3.10

11

0.715

2.65

12

0.653

2.42

13

0.578

2.14

14

0.521

1.93

15

0.430

1.59

16

0.394

1.45

17

0.288

1.06

18

0.261

0.968

19

0.210

0.778

20

0.142

0.525

21

0.120

0.447

22

0.096

0.357

23

0.081

0.300

24

0.033

0.125

25

0.002

0.008

26

0.001

0.005

27

0.000

0.0018

  1. *PC Principal component.
  2. (This shows an ideal pattern as a steep curve, followed by a curve bend and then going to a straight line. The first three components have eigenvalues greater than 2 that explain > 50% of the variation in the data. These first three components are not adequate to explain the amount of variation in the data, so we took the first 8 components that have an eigenvalue greater than 1 as it explains approximately 80% of the variation in the data that is adequate to understand the variation in data).
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