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Kernel regression is a non-parametric statistical technique used to estimate the relationship between variables without making strict assumptions about the underlying data structure. Instead of fitting a single rigid line (like standard linear regression), it calculates a weighted average of nearby data points, where points closer to your query location have the greatest influence.

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