The most precise model of mean height was developed using textural attributes and had a coefficient of determination of 0.93 (Shamsoddini et al. 2013).
GPR-based estimates of peat volume and detailed soil maps were then used to develop a geospatial model of mean peat thickness (L*) in cranberry bogs.
To do this we fit a model of mean expected lifespan to our laboratory flask experiment (Methods: Environmental effects on foundress lifespans).
A linear model of mean transcript accumulation across F1 plants as a function of the mean of parental samples explained a high proportion of F1 transcript variance (p < 0.0001, R = 0.98 (reduced data), p < 0.0001, R = 0.96 (full data)).
The multiple regression analysis resulted in a 2010 model of mean stage of −0.57 + 0.02*Degree Days + 0.33*I (Valley) − 0.01*Degree Days*I Valley), where I Valley) is 1 when the outcome comes from the Willamette Valley and 0 otherwise.
This form facilitates separate modelling of mean and variance trends (Huang and Oosterlee 2008).
To achieve sufficient accuracy in this relation, focus is given to modelling of mean stress relaxation.
Little attention is paid on its configurations, modeling of mean time to system failure, availability, cost benefit and comparisons of parallel and series parallel designs.
Values of RMSE for the final models of mean top height, green crown height, stem volume, mean diameter, basal area and stand density were, respectively, 1.02 m, 0.427 m, 20.2 m3 ha-1, 13.9 mm, 3.81 m2 and 355 stems ha-1.
Age featured in the final model for four of the six stand characteristics while stand density was a significant contributor to the models of mean diameter and basal area.
