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Threshold detection by fitting segmented regression models in Microsoft Excel.

Amy J Hopper1, Angus M Brown1,2

  • 1School of Life Sciences, University of Nottingham, Nottingham NG7 2UH, UK.

Methodsx
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a user-friendly segmented regression analysis method using Microsoft Excel's SOLVER. This approach simplifies threshold detection in experimental data without requiring advanced programming skills.

Keywords:
Least squaresMicrosoft ExcelRegressionSolver

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Area of Science:

  • Biostatistics
  • Data Analysis
  • Scientific Computing

Background:

  • Segmented regression analysis is crucial for identifying transition points in data but often requires specialized programming knowledge.
  • Existing tools in Matlab and R are inaccessible to many researchers due to their complexity.

Purpose of the Study:

  • To present a generally applicable and accessible method for segmented regression analysis.
  • To enable researchers to detect thresholds and fit experimental data with distinct functions using readily available software.
  • To demonstrate the flexibility of the method for incorporating various function types.

Main Methods:

  • Utilized Microsoft Excel's SOLVER add-in for iterative least squares fitting.
  • Developed a spreadsheet template for easy input and analysis of experimental data.
  • Applied the method to fit data with two distinct segmented linear functions and estimated transition points.

Main Results:

  • Successfully demonstrated a method for segmented regression analysis using Microsoft Excel's SOLVER.
  • The method effectively estimates transition points in experimental data.
  • The approach was expanded to include combinations of linear and non-linear functions.

Conclusions:

  • The presented method offers a user-friendly alternative for segmented regression analysis, suitable for researchers without specialist programming expertise.
  • This approach facilitates rapid data processing and threshold detection in experimental studies.
  • The method's flexibility allows for modifications to accommodate diverse functional relationships in data analysis.