Statistical Exploration of Calibration Curve Modeling with STEM Students

Music City Center 

Linear regression is a topic that typically is part of a statistical education. One application of linear regression in science and engineering is through calibration curve modeling, for example, in chemistry. When creating a calibration curve, the technician creates multiple replicates of the response at fixed values of the predictor. Then, a technique such as least squares is utilized to estimate the calibration curve. This curve is estimated with error, where the error is utilized for other parts of the calibration analysis. Although not a recommended practice, sometimes the calibration curve is then fit utilizing averages of the response, instead of the original observations of the response. We discuss how to explore the differences in these approaches visually, through simulation, and theoretically with STEM students.

Section on Statistics and Data Science Education