Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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...
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying that as one...
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unraveling allosteric signaling of G protein-coupled receptors (GPCRs) by single-molecule fluorescence.

Biophysical reviews·2026
Same author

Determining the Hydrodynamic Size of Lipid Nanoparticles by Fluorescent Universal Lipid Labeling for Microfluidic Diffusional Sizing (FULL-MDS).

Methods in molecular biology (Clifton, N.J.)·2026
Same author

Structural plasticity of the membrane-bound protein degradation assembly supports bacterial adaptation to stress.

Cell reports·2026
Same author

Ten questions on membrane biophysics.

Biochimica et biophysica acta. Biomembranes·2026
Same author

Capturing G protein-coupled receptors into native lipid-bilayer nanodiscs using new diisobutylene/maleic acid (DIBMA) copolymers.

Methods (San Diego, Calif.)·2025
Same author

Structural Plasticity of the Membrane-Bound Protein Degradation Assembly Supports Bacterial Adaptation to Stress.

bioRxiv : the preprint server for biology·2025
Same journal

iMUT-seq mapping of DSB-induced mutations with high sensitivity at single-nucleotide resolution.

Nature protocols·2026
Same journal

An assay to quantify sexual commitment and stage conversion in the human malaria parasite Plasmodium falciparum.

Nature protocols·2026
Same journal

Author Correction: Direct inoculation of bioreactor-controlled stirred suspension culture with cryopreserved human pluripotent stem cells.

Nature protocols·2026
Same journal

High-throughput measurements of protein domain functions using magnetic separation.

Nature protocols·2026
Same journal

Inducing physiological polarity and performing gene editing using CRISPR-Cas9 in human trophoblast organoids.

Nature protocols·2026
Same journal

Photocatalytic low-temperature defluorination of PTFE.

Nature protocols·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Nonlinear least-squares data fitting in Excel spreadsheets.

Gerdi Kemmer1, Sandro Keller

  • 1Leibniz Institute of Molecular Pharmacology FMP, Berlin, Germany.

Nature Protocols
|February 6, 2010
PubMed
Summary
This summary is machine-generated.

This study presents an easy-to-use method for analyzing experimental data using nonlinear least-squares fitting (NLSF) in Microsoft Excel. The protocol allows users to obtain and assess best-fit parameters without specialized software, applicable to various datasets.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

Related Experiment Videos

Last Updated: Jun 16, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

Area of Science:

  • Biochemistry
  • Computational Biology
  • Data Analysis

Background:

  • Nonlinear least-squares fitting (NLSF) is crucial for analyzing experimental data.
  • Specialized software and programming knowledge are often required for NLSF.
  • A need exists for accessible and cost-effective NLSF methods.

Purpose of the Study:

  • To describe an intuitive and rapid procedure for NLSF using Microsoft Excel.
  • To enable users without prior programming or data-fitting experience to perform NLSF.
  • To provide a cost-free alternative to specialized NLSF software.

Main Methods:

  • Inputting experimental x/y data and regression equation data into an Excel worksheet.
  • Plotting data and calculating the sum of squared residuals.
  • Utilizing the Excel Solver add-in to minimize residuals and obtain best-fit parameters.

Main Results:

  • Successful implementation of NLSF for experimental data analysis within Excel.
  • Visualization and assessment of confidence in best-fit parameter values.
  • Demonstration using the Michaelis-Menten equation for enzyme kinetics.

Conclusions:

  • The described Excel-based protocol offers an accessible and efficient method for NLSF.
  • The procedure is adaptable to various datasets and regression equations with minor modifications.
  • This approach empowers a wider range of users to perform sophisticated data analysis.