The coefficients are numbers from the regression line of the Bias versus Reference Value plot.
The general form of this least squares regression line is:
The term, b, represents the constant coefficient. It indicates where the fitted line crosses the y-axis.
The term, a, represents the slope coefficient. The slope of a line indicates the steepness of the line and is the change in the y-axis over the change in the x-axis.
When the slope coefficient, a, is very small, the slope is near horizontal. Thus, bias is relatively constant across reference values, and linearity is not a significant problem. Larger absolute values of the slope coefficient, |a|, indicate a steeper slope of the line. If the p-value of the slope is less than alpha, then linearity is significant.
In the absence of significant linearity, larger absolute values of the constant coefficient, |b|, indicate larger bias. When significant linearity is present, you must look at the individual bias values.