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JMP Inspire || Multivariate

 

Analytical methods have progressively evolved from univariate approaches, focused on the study of a single variable, toward multivariate analyses capable of capturing interactions between multiple parameters simultaneously.

In this context, correlation matrices play a central role by highlighting relationships and hidden dependencies among variables. These analyses make it possible to identify the most influential factors within complex systems and to better understand how variables interact with one another.

Building on these insights, optimization techniques can then be applied to improve performance, refine predictive models, or support decision-making processes.

Such multivariate analytical approaches are particularly valuable for uncovering hidden structures and extracting meaningful information from large and complex datasets.

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