Question

During exploratory data analysis on a housing dataset, an analyst computes a covariance matrix between 'square_footage' (measured in thousands of square feet) and 'lot_size' (measured in acres). The covariance value is 45.2, and the analyst wants to determine the strength of the linear relationship between the two features to decide whether one is redundant. Why is the correlation coefficient a more appropriate metric than the raw covariance value here?

Accepted Answer

Correlation is normalized to a range between -1 and 1, making it comparable across features with different units and scales, whereas covariance magnitude depends on the units of measurement.

Answer

Correlation can detect non-linear relationships between features, whereas covariance only measures linear relationships.

Answer

Correlation handles missing values automatically, while covariance requires complete cases and will error on nulls.

Answer

Covariance is always positive and therefore cannot indicate the direction of a relationship, while correlation can.