correlation vs covariance in regression.

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Correlation measures the strength and direction of a linear relationship between two variables, while covariance measures the extent to which two variables change together.

Correlation ranges from -1 to 1, where -1 indicates a perfect negative linear relationship, 0 indicates no linear relationship, and 1 indicates a perfect positive linear relationship.
Covariance can take any value between negative infinity and positive infinity, where a positive value indicates a positive relationship and a negative value indicates a negative relationship.
Correlation is dimensionless and standardized, making it easier to interpret compared to covariance.
In regression analysis, correlation is used to determine the strength and direction of the relationship between the independent and dependent variables, while covariance is used to measure the extent to which changes in one variable predict changes in another variable.

correlation vs covariance in regression.

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