What is covariance and colinearity?

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Covariance measures the relationship between two variables, while colinearity refers to the situation where two or more predictors in a multiple regression model are highly correlated.

Covariance is a measure of how two variables change together. A positive covariance indicates that the variables tend to increase or decrease together, while a negative covariance indicates that one variable tends to increase as the other decreases.
Colinearity occurs when two or more predictors in a regression model are highly correlated. This can lead to issues such as unstable parameter estimates and difficulty in interpreting the model.
High colinearity can be detected using methods like variance inflation factor (VIF) or correlation matrix.
Example: In a study examining the relationship between height and weight, covariance would measure how changes in height correspond to changes in weight. Colinearity may occur if both height and leg length are included as predictors in the same regression model.

What is covariance and colinearity?

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