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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

931
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
931
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.7K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.7K
Regression Analysis01:11

Regression Analysis

7.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.6K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.4K
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...
1.4K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
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...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Breathtaking heights: Lung mechanics and pulmonary extravascular fluid accumulation in female climbers during the K2 expedition.

Experimental physiology·2026
Same author

A reassessment of the energetic significance of blood lactate accumulation during exercise.

European journal of applied physiology·2026
Same author

A comparison of normobaric and hypobaric hypoxia effects on cerebrovascular response pre and post maximal exercise.

Experimental physiology·2026
Same author

Energetics of Underwater Swimming in Apnea - Corrigendum.

Medicine and science in sports and exercise·2026
Same author

Description of three dysfunctional breathing patterns in post-COVID dyspnea.

Respiratory physiology & neurobiology·2026
Same author

One night at 1,900 m prompts ventilatory acclimatization without altering cardiac autonomic regulation at 3,000 m in males with coronary artery disease.

Journal of applied physiology (Bethesda, Md. : 1985)·2025

Related Experiment Video

Updated: Dec 28, 2025

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

10.9K

A regression method for the power-duration relationship when both variables are subject to error.

Giovanni Vinetti1, Anna Taboni2, Guido Ferretti3,2

  • 1Department of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, 25123, Brescia, Italy. g.vinetti001@unibs.it.

European Journal of Applied Physiology
|February 21, 2020
PubMed
Summary

Geometric mean (GM) regression offers an alternative to weighted least squares (WLS) for modeling the power-duration relationship in exercise science. GM regression provides equivalent results to WLS and is preferable when power measurement error is a concern.

Keywords:
Curve fittingCyclingHyperbolic modelModel II nonlinear regressionReduced major axis regressionSport

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

Related Experiment Videos

Last Updated: Dec 28, 2025

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

10.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

Area of Science:

  • Exercise Physiology
  • Sports Science
  • Biomechanics

Background:

  • The power-duration relationship is fundamental in exercise science, typically modeling duration as the sole dependent variable.
  • Traditional models assume negligible error in power output measurements.
  • However, errors in power output can be significant at high intensities or with field-based power meters.

Purpose of the Study:

  • To investigate the utility of the geometric mean (GM) regression method for modeling the power-duration relationship.
  • To compare GM regression with the conventional weighted least squares (WLS) method in this context.
  • To assess GM regression's suitability when power output measurement error is present.

Main Methods:

  • Applied GM regression to established two- and three-parameter critical power models.
  • Compared GM regression parameter estimates against those derived from WLS procedures.
  • Utilized previously published experimental data for validation.

Main Results:

  • No significant differences were observed between parameter estimates obtained via WLS and GM regression.
  • Low bias and limits of agreement were found between the two methods.
  • High correlation coefficients (0.85-1.00) indicated strong agreement.

Conclusions:

  • GM regression provides results comparable to WLS for fitting critical power models.
  • GM regression is conceptually advantageous when power measurement precision is uncertain, such as with in-field power meters.
  • This method offers a robust alternative for analyzing the power-duration relationship under various measurement conditions.