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Finding Redundancies

One of the important things that principal component analysis can do is shrink redundancy in your dataset. In its simplest manifestation, redundancy occurs when two variables are correlated.

The Pearson correlation coefficient is a number between -1 and 1. Coefficients near zero indicate two variables are linearly independent, while coefficients near -1 or 1 indicate that two variables are linearly related.

The dataset combine has been loaded for you.

This exercise is part of the course

Linear Algebra for Data Science in R

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print the first 6 observations of the dataset
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