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Linear regression equations were adapted.
Using the above spectrofluorimetric methods, linear regression equations were obtained.
Values recorded by the accelerometers were converted into acceleration (m/s2) with linear regression equations.
As such we used the simpler multiple linear regression equations for our landscape extrapolations.
Multiple linear regression equations were developed for predicting soil erosion from the experimental factors.
Best-fit least square simple and multiple linear regression equations were developed to predict some dimensions.
R statistical analysis program was also used to develop the set of linear regression equations.
Multiple linear regression equations were developed for predicting wash erosion from the experimental factors.
In Phase 1 significant linear regression equations were found between OMI and fCP in each phenological stage (P<0.05).
Table 2 shows concentration levels, linear regression equations, and coefficients of determination for all ANDs under study.
The linear regression equations relating the peak areas to the concentration (ng/mL) were derived for each analyte.
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