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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
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[Correlation: not all correlation entails causality].
Ivonne Roy-García1, Rodolfo Rivas-Ruiz, Marcela Pérez-Rodríguez
1Instituto Mexicano del Seguro Social, Centro Médico Nacional Siglo XXI, Centro de Adiestramiento en Investigación Clínica, Ciudad de México, México. ivonne3316@yahoo.com.mx.
Revista Alergia Mexico (Tecamachalco, Puebla, Mexico : 1993)
|October 14, 2019
Summary
Correlation analysis quantifies the relationship between two variables, aiding in predictions. This statistical test is vital in clinical settings for understanding associations and building predictive models.
Area of Science:
- Biostatistics
- Clinical Research Methodology
Background:
- Correlation analysis examines the relationship between two paired observations (X and Y).
- Understanding the trend of grouped variables is crucial in various research fields.
Purpose of the Study:
- To explain the concept and application of correlation tests.
- To highlight the utility of correlation in quantifying variable associations and enabling predictions.
Main Methods:
- The study describes the fundamental principles of correlation.
- It emphasizes the use of correlation tests to determine the magnitude and direction of association between variables.
Main Results:
- Correlation tests quantify the strength and direction of the relationship between two variables.
- Perfect correlation allows for the deduction of one variable's value from another.
Conclusions:
- Correlation is a frequently used statistical test in clinical practice.
- It serves as a foundation for predictive models like linear regression, logistic regression, and Cox proportional hazards models.
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