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Correlation matrix

As explained in Data Without Labels

A correlation matrix is a square table whose entries quantify pairwise statistical association between variables.

A correlation matrix is a square table created during bivariate analysis to show pairwise relationships and their strength across variables. Highly correlated variables can then be examined using business logic, with one potentially removed before dimensionality reduction. section 3.8

Use the matrix to investigate questions such as:

  • Whether subscribers who use more data spend more time on the network than subscribers who send more SMS. section 3.8
  • Whether online transactions generate more revenue than cash transactions. section 3.8
  • Whether gender or age relates to data usage. section 3.8

How Data Without Labels covers it

How deep it goes
Defines correlation matrices through practical questions and high-correlation review, but does not detail their calculation or implementation.
Examples
Uses subscriber data, network time, SMS, data usage, transaction mode, revenue, gender, and age as relationship questions.
What you can do after
Evaluate relationships and identify potentially redundant variables for removal before PCA or SVD.
How it connects
Connects bivariate analysis with business logic, feature selection, PCA, SVD, and dimensionality reduction.

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This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.