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

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.6K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.6K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

1.6K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
1.6K
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Kendall's Tau Test01:16

Kendall's Tau Test

671
Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value...
671
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

778
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
778
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.6K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Association of systemic lupus erythematosus with 90-day readmission following heart failure hospitalization: A national readmission database (NRD) analysis, 2016-2017.

Lupus·2026
Same author

From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.

Digital health·2026
Same author

Effect of Fourth-Line Antihypertensive Therapy on Clinic Systolic Blood Pressure in Resistant Hypertension: A Systematic Review and Meta-Analysis.

Blood pressure·2026
Same author

Trends and Disparities in Mortality due to Pulmonary Embolism Among Adults With Atrial Fibrillation From 1999 to 2020: Insights From the CDCWONDER Database.

Journal of arrhythmia·2026
Same author

Impact of OMEGA-3 fatty acid in patients undergoing hemodialysis: a systematic review and meta-analysis.

BMC nephrology·2026
Same author

Slowing the curve: a single-arm meta-analysis of mFARS outcomes following omaveloxolone treatment in Friedreich ataxia.

BMJ neurology open·2026

Related Experiment Video

Updated: Jun 28, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Correlation coefficients for T-spherical fuzzy sets and their applications in pattern analysis and multi-attribute

Muhammad Saad1, Ayesha Rafiq1

  • 1Department of Applied Mathematics & Statistics, Institute of Space Technology Islamabad, Islamabad, Pakistan.

Granular Computing
|April 16, 2024
PubMed
Summary

New correlation coefficients for T-spherical fuzzy sets (TSFS) effectively measure associations. These coefficients offer advantages in pattern recognition and decision-making, including COVID-19 mask selection.

Keywords:
Correlation coefficientsMulti-attribute decision-makingPattern recognitionT-spherical fuzzy sets

More Related Videos

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Related Experiment Videos

Last Updated: Jun 28, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Area of Science:

  • Fuzzy Set Theory
  • Decision Science
  • Pattern Recognition

Background:

  • T-spherical fuzzy sets (TSFS) are effective for handling vagueness and uncertainty, especially with multiple circumstances.
  • Correlation coefficients are crucial for quantifying the association between fuzzy sets, with applications in science, management, and engineering.
  • Existing methods for TSFS lack comprehensive association analysis.

Purpose of the Study:

  • To introduce novel correlation coefficients for T-spherical fuzzy sets.
  • To demonstrate the application of these coefficients in pattern recognition and decision-making.
  • To highlight the advantages of the proposed coefficients over existing methods.

Main Methods:

  • Development of new correlation coefficients specifically for T-spherical fuzzy sets.
  • Application of proposed coefficients to pattern analysis tasks.
  • Utilizing the coefficients in a multi-attribute decision-making (MADM) problem for COVID-19 mask selection.

Main Results:

  • The proposed correlation coefficients provide a complete measure of association between TSFS.
  • Demonstrated effectiveness in pattern analysis and a practical MADM problem.
  • Comparative analysis shows superiority over existing correlation coefficients for TSFS.

Conclusions:

  • The newly introduced correlation coefficients for T-spherical fuzzy sets offer enhanced capabilities for analyzing associations.
  • These coefficients are valuable tools for pattern recognition and complex decision-making scenarios, such as selecting appropriate COVID-19 masks.
  • The proposed methods present a significant improvement over existing techniques for T-spherical fuzzy set analysis.