An analytics team is exploring a manufacturing sensor dataset with 15 continuous variables recorded every second. Before building any predictive models, the team needs a single visualization that lets them
inspect every pairwise combination of variables for linear and non-linear dependence,
see clusters or anomalies that might indicate multivariate outliers, and
quickly spot highly correlated feature pairs that could create multicollinearity.
Which chart type would best accomplish all three tasks in one view?
A scatter plot matrix (sometimes called a pair plot) displays a grid of scatter plots for every pair of numeric variables, making it possible to observe linear or curved relationships, visually detect outliers or clusters, and immediately spot strongly correlated pairs. A correlation heat map only reports a linear statistic (typically Pearson's r) and cannot reveal non-linear shapes or point-level anomalies. Parallel-coordinates plots portray many dimensions at once but do not show the detailed two-variable patterns needed for diagnosing pairwise collinearity or non-linearity. Faceted violin plots are strictly univariate; they show each variable's distribution but provide no information about relationships between variables.
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What is a scatter plot matrix, and how does it help identify relationships between variables?
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How does multicollinearity affect predictive modeling, and why is it important to address?
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What are the limitations of a correlation heat map compared to a scatter plot matrix?