Correlation and Regression

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Correlation

Correlation measures the strength and direction of a linear relationship between two variables. It helps us understand how changes in one variable relate to changes in another. The correlation coefficient, ranging from -1 to 1, provides insights into the nature of the association. Remember, correlation does not imply causation; it simply quantifies the degree of connection.

r=ni=1(XiˉX)(YiˉY)ni=1(XiˉX)2ni=1(YiˉY)2

Regression

Regression takes us a step further by modeling the relationship between a dependent variable and one or more independent variables. The resulting equation represents the best-fit line or curve, enabling us to predict the dependent variable’s value based on given independent variables. While correlation provides insights into association, regression equips us with a predictive tool.

Y=b0+b1X+ε

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