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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

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, and...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
Correlation and Regression00:53

Correlation and Regression

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 negative...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...

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incor: a computer program for testing differences among independent correlations.

N C Silver1, H Zaikina, J B Hittner

  • 1Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Pkwy, Las Vegas, NV 89154-5030, USA Department of Psychology, College of Charleston, Charleston, SC 29424, USA.

Molecular Ecology Resources
|May 19, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces INCOR, an interactive Fortran program for testing if multiple independent population correlations are equal. The software also facilitates pairwise correlation comparisons.

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Area of Science:

  • Statistics
  • Psychometrics
  • Computational Statistics

Background:

  • Comparing multiple correlation coefficients is crucial in various scientific fields.
  • Existing methods for testing the equivalence of more than two correlations can be complex.
  • A need exists for accessible tools to perform these statistical tests.

Purpose of the Study:

  • To introduce and describe the INCOR program.
  • To implement the Paul (1989) procedure for testing the null hypothesis of equal population correlations.
  • To provide functionality for post-hoc pairwise correlation comparisons.

Main Methods:

  • Development of an interactive Fortran program named INCOR.
  • Implementation of the Paul (1989) statistical procedure.
  • Inclusion of algorithms for range tests to compare all pairwise correlations.

Main Results:

  • The INCOR program successfully performs the Paul (1989) test for equivalence of multiple correlations.
  • The program enables efficient execution of subsequent range tests for pairwise comparisons.
  • Provides a user-friendly interface for complex statistical analyses.

Conclusions:

  • INCOR offers a valuable computational tool for researchers.
  • Facilitates rigorous statistical testing of correlation equivalence.
  • Aids in understanding the relationships between multiple variables.