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Table 1 Parameter estimates \( \widehat{\alpha} \) and \( \widehat{\beta} \) associated standard errors (\( ste\left(\widehat{\alpha}\right), ste\Big(\widehat{\beta} \))) found by fitting the model of Eq. (1). Nonlinear regression estimates associate to the homoscedastic case of the model of Eqs. (5) and (6) (see Appendix 1). Values of \( \widehat{\rho} \) give an evaluation of reproducibility strength of the proxy of Eq. (1)

From: On the suitability of an allometric proxy for nondestructive estimation of average leaf dry weight in eelgrass shoots I: sensitivity analysis and examination of the influences of data quality, analysis method, and sample size on precision

Analysis method Data \( \widehat{\beta} \) \( ste\left(\widehat{\beta}\right) \) \( \widehat{\alpha} \) \( ste\left(\widehat{\alpha}\right) \) \( \widehat{\rho} \)
Log-linear Transformation
Raw
1.3674x10−5 2.9355 × 10− 7 1.023 3.662 × 10− 3 0.8910
Nonlinear Regression
Raw
8.718x10−6 3.530 × 10−7 1.104 5.101 × 10−3 0.9307
Log-linear Transformation
Processed
1.142 x10−5 2.0831 × 10−7 1.046 3.035 × 10−3 0.9455 
Nonlinear Regression
Processed
6.956 x10−6 2.200 × 10−7 1.132 3.954 × 10−3 0.9777