Related Experiment Video
Updated: Nov 2, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
Published on: September 7, 2019
Evaluating Uncertainty of Nonlinear Microwave Calibration Models with Regression Residuals
Dylan Williams1, Benjamin Jamroz1, Jacob D Rezac1
1National Institute of Standards and Technology, Boulder, CO 80305 USA.
None:
We investigate the performance of a recently developed algorithm that evaluates the uncertainty of nonlinear multivariate microwave calibration models using regression residuals. We apply the algorithm to synthetic data consisting of both random and systematic errors and show that the algorithm can account for both types of errors even in the absence of accurate models for the random errors. We also verify the algorithm with measured data.
More Related Videos
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
10:00Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
Published on: June 2, 2020
Related Concept Videos
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Uncertainty in Measurement: Accuracy and Precision
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Calibration Curves: Correlation Coefficient
Uncertainty in Measurement: Reading Instruments