Interpreting Correlations

An Interactive Visualization

Created by Kristoffer Magnusson


Correlation is one of the most widely used tools in statistics. The correlation coefficient summarizes the association between two variables. In this visualization I show a scatter plot of two variables with a given correlation. The variables are samples from the standard normal distribution, which are then transformed to have a given correlation by using Cholesky decomposition. By moving the slider you will see how the shape of the data changes as the association becomes stronger or weaker. You can also look at the Venn diagram to see the amount of shared variance between the variables. It is also possible drag the data points to see how the correlation is influenced by outliers.


Correlation: 0.00

Shared variance: 0%

y = 100.00 + 0.00*x

Mean(y) = 100.00

Mean(x) = 100.00

SD(y) = 3.00

SD(x) = 5.00

Loading visualization

Written by Kristoffer Magnusson a researcher in clinical psychology. You should follow him on Twitter and come hang out on the open science discord Git Gud Science.


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You can load your own data from a CSV file. The CSV file must have this structure:

The first row is the column names, where the first column is assumed to be the x variable and the second the y variable - all other columns are ignored.

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This site performs no server-side calculations, and the data is only loaded in your browser and not uploaded to my server.

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Cite this page according to your favorite style guide. The references below are automatically generated and contain the correct information.


Magnusson, K. (2020). Interpreting Correlations: An interactive visualization (Version 0.6.4) [Web App]. R Psychologist.


Please report errors or suggestions by opening an issue on GitHub, if you want to ask a question use GitHub discussions

No, it will be fine. The app runs in your browser so the server only needs to serve the files.

Yes, go ahead! I did not invent plotting two overlapping Gaussian distributions. This visualization is dedicated to the public domain, which means “you can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission” (see Creative common’s CC0-license). Although, attribution is not required it is always appreciated!


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