![]() ![]() ![]() This often results in excessive error at the bottom of the curve. However, because the absolute variation (as opposed to %-error) is larger for higher concentrations, the data at the high end of the calibration curve tend to dominate the calculation of the linear regression. When a least-squares linear regression is used to fit experimental data to a linear calibration curve, equal emphasis is given to the variability of data points throughout the curve. This also is an appropriate topic, because it addresses questions submitted by several readers asking why I didn't weight the curves in the discussion of whether or not the calibration curve passed through the origin (1). ![]() This month will focus on a more specialized topic, the use of curve weighting. We have looked at questions related to whether or not the calibration curve should pass through the origin (1), how to determine method limits (2), the use of %-error plots to highlight potential problems (3), and which calibration model to use (4). This is the fifth and final installment in a series of "LC Troubleshooting" columns about various aspects of calibration of liquid chromatography (LC) methods and problems related to calibration. ![]()
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