| |
Changes in actual bending stiffness between weeks
|
Coefficients of linear regression analysis of logarithmic data
|
---|
Target weeks
|
Parameters
|
2 to 3
|
3 to 4
|
4 to 5
|
2 to 4
|
Intercept
|
Slope
|
10
|
r
|
0.473
|
0.710
|
0.765
|
0.721
|
0.718
|
0.419
|
|
r2
| |
0.504
|
0.585
|
0.519
|
0.515
| |
|
t
|
n.s.
|
3.189
|
3.752
|
3.287
|
3.260
|
n.s.
|
|
p
| |
0.010
|
0.004
|
0.008
|
0.009
| |
7
|
r
|
0.131
|
0.928
|
0.873
|
0.826
|
0.702
|
0.364
|
|
r2
| |
0.862
|
0.763
|
0.682
|
0.492
| |
|
t
| |
7.058
|
5.073
|
4.140
|
2.785
| |
|
p
|
n.s.
|
< 0.001
|
< 0.001
|
0.003
|
0.024
| |
- Statistical data obtained from linear regression analysis. Future development of bending stiffness can be estimated from the third week on. The best predictive value at the seventh week is obtained from the change in bending stiffness between the third and the fourth week. Prediction of stiffness at the tenth week can best be estimated from change in fracture stiffness between the fourth and fifth weeks. Linear regression analysis of logarithmic transformed stiffness data versus time revealed that predictive values can be obtained from the intercept of the fitted line rather than from the slope.