A financial analyst is conducting exploratory data analysis on a dataframe that contains the daily percentage returns of 25 different asset classes. She needs one visualization that simultaneously provides a quantitative, at-a-glance view of the strength and direction of linear relationships between every possible pair of return series so she can spot potential multicollinearity issues before feature engineering. Which chart should she create?
A correlation plot-often shown as a color-coded heat map of the correlation matrix-encodes each pairwise Pearson (or Spearman) coefficient as both a position and a color scale in a single chart. This makes the magnitude (strength) and sign (direction) of linear dependence between all variable pairs immediately visible, allowing the analyst to detect clusters of highly correlated assets that could lead to multicollinearity. A scatter plot matrix can reveal relationships as well, but it requires visually comparing dozens of individual panels and does not provide the exact correlation values. Box-and-whisker plots and violin plots each display the distribution of a single variable at a time and offer no direct information about relationships between pairs of variables, so they are unsuitable for detecting inter-variable correlations.
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What is multicollinearity and why is it a concern in data analysis?
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How is the Pearson or Spearman coefficient calculated in a correlation plot?
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Why is a correlation plot preferred over a scatter plot matrix for detecting relationships?