Correlation refers to the statistical measure that describes the degree to which two variables move in relation to each other. Its value ranges between -1 and 1, indicating the strength and direction of the relationship.
Covariance is a statistical measure that indicates the extent to which two variables change together. A positive covariance suggests that the variables tend to increase or decrease in tandem, whereas a negative covariance indicates that as one variable increases, the other tends to decrease.
Multicollinearity refers to the presence of independent variables in regression analysis that are associated with each other, having some degree of correlation. This phenomenon can complicate the interpretation of model coefficients and lead to unreliable results.
Regression analysis is a statistical technique used to establish the relationship between a dependent variable and one or more independent variables. It is widely used in various fields to predict future values and measure the significance of different factors.
