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

Correlations02:20

Correlations

35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
Correlation and Causation01:27

Correlation and Causation

42.3K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.3K
Correlation01:09

Correlation

14.8K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
14.8K
Correlation and Regression00:53

Correlation and Regression

3.2K
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...
3.2K
Coefficient of Correlation01:12

Coefficient of Correlation

8.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.5K
Correlation of Experimental Data01:23

Correlation of Experimental Data

480
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
480

You might also read

Related Articles

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

Sort by
Same author

Fibre phantom generation using FibreSimulator: an open-source Python tool.

Journal of synchrotron radiation·2026
Same author

Dual phase high temperature Si<sub>3</sub>N<sub>4</sub>/Al(Ti)N films with tunable thermal conductivity.

Nature communications·2025
Same author

Novel Focused Ion Beam Techniques for Enhanced Sample Preparation for In Situ Transmission Electron Microscopy Heating and Irradiation Experiments.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2025
Same author

The role of internal defects on anisotropic tensile failure of L-PBF AlSi10Mg alloys.

Scientific reports·2023
Same author

Inhibiting weld cracking in high-strength aluminium alloys.

Nature communications·2022
Same author

X-ray Micro-Computed Tomography: An Emerging Technology to Analyze Vascular Calcification in Animal Models.

International journal of molecular sciences·2020
Same journal

Publisher Correction: Ultralow-voltage electrochemical organic light-emitting transistors with pinned and wide lateral recombination.

Nature materials·2026
Same journal

High-Chern-number orbital magnetism in twisted rhombohedral graphene.

Nature materials·2026
Same journal

Programming local confinements in crystalline frameworks through reticular chemistry.

Nature materials·2026
Same journal

Single-crystal-like polymer semiconductors via self-templated gradient assembly for ultrahigh charge carrier mobility.

Nature materials·2026
Same journal

Fractional quantum anomalous Hall effect in moiré fractional Chern insulators.

Nature materials·2026
Same journal

Excitons in van der Waals magnetic materials.

Nature materials·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Intermediate Strain Rate Material Characterization with Digital Image Correlation
07:59

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Published on: March 1, 2019

7.6K

Completing the picture through correlative characterization.

T L Burnett1, P J Withers2

  • 1Henry Royce Institute for Advanced Materials, School of Materials, The University of Manchester, Manchester, UK.

Nature Materials
|June 19, 2019
PubMed
Summary
This summary is machine-generated.

Correlative characterization integrates multiple imaging techniques to map material properties across scales. This enables big data approaches for discovering and designing materials with tailored performance.

More Related Videos

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.8K
Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
11:29

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

Published on: April 20, 2019

10.3K

Related Experiment Videos

Last Updated: Jan 23, 2026

Intermediate Strain Rate Material Characterization with Digital Image Correlation
07:59

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Published on: March 1, 2019

7.6K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.8K
Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
11:29

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

Published on: April 20, 2019

10.3K

Area of Science:

  • Materials Science
  • Nanotechnology
  • Chemistry

Background:

  • Materials performance is dictated by complex hierarchical microstructures, from nanoscale to macroscale.
  • Understanding material properties requires integrated knowledge of microstructure, interfaces, chemistry, and crystallography.

Purpose of the Study:

  • To review recent advancements in correlative characterization techniques for materials science.
  • To highlight the importance of multimodal and multiscale correlated datasets for materials discovery.
  • To emphasize the role of automated data collection and machine learning in materials development.

Main Methods:

  • Correlative characterization combining multiple imaging modalities.
  • Mapping local chemistry, structure, and functionality.
  • Automated collection and digitization of multidimensional data.

Main Results:

  • Development of rich multimodal and multiscale correlated datasets is achievable.
  • These datasets facilitate the integration of diverse characterization data.
  • Automated data handling supports advanced computational approaches.

Conclusions:

  • Correlative characterization is key to understanding and predicting material performance.
  • Multiscale modeling and machine learning approaches benefit from comprehensive, correlated data.
  • This integrated approach accelerates the development of materials with bespoke properties.