The results show no significant deviation from linearity and the mean ohmic channel conductance from the linear regression fit was 14.5 pS.
Linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P) are shown for each comparison.
Linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P), n = 10 independent biological samples.
The linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P) are shown for each comparison.
Input resistance was measured from the slope of a linear regression fit to the voltage-current relation in a voltage range hyperpolarized of membrane potential.
For multiple linear regression, the procedure can be repeated many times, each time producing a new dataset with the same multiple linear regression fit as the original data.
A linear regression fit was used to explain the observed time difference between when maximum flame height and maximum mass loss rate occur, as a function of fuel moisture content.
This contribution presents a simple and reliable formulation for the linear regression fit using the weighted total least squares (WTLS) problem, when both variables are subjected to different and possibly correlated noise.
The lines represent the linear regression fit to the data.
The solid line shows OLS linear regression fit.
The dashed line is a linear regression fit.
