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R values provide a normalized measurement of the linear relation of two variables in data samples.
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The relationship between exposure and other variables was evaluated by Pearson's correlation coefficient, which reflects the degree of linear relation between two sets of data.
R 2 of the linear relation between two series of concentrations is 0.981, and RMSE of these calibration data is 5.060 mg/L.
R 2 of the linear relation between two series of concentrations is 0.999, and root mean square error (RMSE) of these calibration data is 1.114 mg/L, which reflects the perfect accuracy of the model.
Co-localization channels were calculated using Imaris (Bitplane) based on the correlation of the strength of linear relation between the two channels.
Pearson r[ 33] can vary in magnitude from −1 to 1, with −1 indicating a perfect negative linear relation, 1 indicating a perfect positive linear relation, and 0 indicating no linear relation between two variables.
Pearson and Spearman correlations were used for the linear relation between two numerical dates.
The linear relation between the two sets of locations tells us how the current ages map onto the norming sample ages and vice versa.
The linear relation among probabilities of two consecutive values is achieved when ν=1, i.e. given data equi-dispersion associated with the Poisson model.
The figure demonstrates lack of a strong linear relation between these two metrics when considering evaluations for both the top L/5 predictions (Fig. 4A) and for the top L predictions (Fig. 4B).
Red lines indicate the linear relation between the two parameters in each panel.
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