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Related Experiment Video

Updated: Jun 1, 2026

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

Assessing the accuracy of analytical methods using linear regression with errors in both axes.

J Riu¹, F X Rius

¹Departament de Química, Universitat Rovira i Virgili, Pl. Imperial Tàrraco, 1, 43005-Tarragona, Catalonia, Spain.

Analytical Chemistry

|May 31, 2011

Summary

A new linear regression technique enhances analytical method accuracy assessment by using bivariate least-squares regression (BLS) to calculate joint confidence intervals, accounting for uncertainties in both axes.

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Last Updated: Jun 1, 2026

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

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Area of Science:

Analytical Chemistry
Statistical Modeling

Background:

Assessing analytical method accuracy is crucial for reliable scientific results.
Traditional methods often use ordinary or weighted least-squares regression, which may not fully account for uncertainties.

Purpose of the Study:

To introduce a novel technique for evaluating analytical method accuracy using linear regression.
To develop a test based on the joint confidence interval for slope and intercept, incorporating uncertainties from both axes.

Main Methods:

Bivariate least-squares regression (BLS) was employed to calculate slope, intercept, and associated variances.
The new technique was validated using simulated data (Monte Carlo simulations with 100,000 datasets) and real-world datasets.

Main Results:

The BLS-based joint confidence interval method was validated for correctness.
Application to real data revealed differences compared to ordinary and weighted least-squares regression methods.
The new technique demonstrated improved detection of discrepancies in analytical method accuracy.

Conclusions:

The proposed BLS-based joint confidence interval technique offers a more robust assessment of analytical method accuracy.
This method provides a valuable alternative for validating analytical procedures by accounting for dual-axis uncertainties.