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Correlation and regression are tools for exploring and modelling the relationship between two (or more) quantitative variables. Correlation measures the strength and direction of a linear relationship, while regression provides a predictive equation.
The first step in studying the relationship between two variables is to create a scatter plot. Each point represents one observation plotted as (x, y).
Patterns to look for:
The Pearson correlation coefficient measures the strength and direction of a linear relationship:
r = Σ[(xᵢ − x̄)(yᵢ − ȳ)] / √[Σ(xᵢ − x̄)² × Σ(yᵢ − ȳ)²]
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