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Related Concept Videos

Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
Correlations02:20

Correlations

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...
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:
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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 correlation by...
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...

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Related Experiment Video

Updated: Jul 24, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
22:27

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

13. PET and CT Correlation. An Important Explanation for Discordant Findings.

Hoffman1

  • 1Torrance Memorial Medical Center, Torrance, CA, USA

Clinical Positron Imaging : Official Journal of the Institute for Clinical P.E.T
|January 11, 2001
PubMed
Summary

When positron emission tomography (PET) shows an abnormality not visible on computed tomography (CT), further investigation is crucial. This can reveal motion artifacts or new disease progression not yet apparent on prior CT scans.

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Area of Science:

  • Nuclear Medicine
  • Radiology
  • Oncology

Background:

  • Discordant findings between PET and CT can complicate oncologic imaging interpretation.
  • High-quality PET and CT scans are essential for accurate diagnosis.

Purpose of the Study:

  • To compare and explain discordant findings between PET and CT scans.
  • To investigate cases where PET abnormalities lack corresponding CT findings.

Main Methods:

  • Review of forty sequential oncologic cases.
  • Examination of helical post-contrast CT and attenuated/unattenuated PET images.
  • Classification of discordance into Type A (PET abnormality, no CT abnormality) and Type B (CT abnormality, no PET abnormality).

Main Results:

  • Four cases of Type A discordance were identified.
  • One case involved a PET-CT mis-registration artifact due to motion.
  • Three cases showed new CT findings on repeat scans, corresponding to PET abnormalities, within weeks of the initial PET exam.

Conclusions:

  • Conspicuous hypermetabolic PET findings without corresponding CT anatomy warrant further evaluation.
  • Potential causes for discordance include technical artifacts and rapid disease progression leading to new CT findings.