The Correlation Coefficient: An Overview

By A. Asuero, A. Sayago, Antonio G. González. 2006

1,356 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/80ccd0790a6889ea4517b378d7ddf5eb49db5da9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. David Daffos Ruiz de Adana
2. Julia Martín
3. A. Asuero
4. Bin Zhao
5. Feng Ming
6. Anmin Zeng
7. Yuanxi Yang

In Food Science

1. David Daffos Ruiz de Adana
2. Julia Martín
3. A. Asuero

In Statistics and Probability

1. David Daffos Ruiz de Adana
2. Julia Martín
3. A. Asuero
4. Bin Zhao
5. Feng Ming
6. Anmin Zeng
7. Yuanxi Yang

Citations in the corpus

• Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study (2020, methodology): “A pairwise distance matrix is then computed using the Pearson Correlation Coefficient (PCC) [53] as a distance measure between magnitude spectra.”
• Fitting Models to Data: Residual Analysis, a Primer (2017, methodology): “The residuals are geometrically the distances calculated in the y-direction [1, 2, 11, 12] (vertical distances) between the points and the regression line (error free in the independent variable)”
• Spatiotemporal filtering for regional GPS network in China using independent component analysis (2017, background): “It should be noted that Pearson’s correlation reflects the degree of linear relationship between two datasets, but that it is inappropriate when either or both datasets are not normally distributed (Asuero et al. 2006).”