what is multicollinearity in regression

Multicollinearity in regression occurs when independent variables in a regression model are correlated with each other. This correlation can cause problems when fitting the model and interpreting the results. It makes it difficult to interpret the coefficients and reduces the power of the model to identify statistically significant independent variables. Multicollinearity does not affect predictions, precision of predictions, or goodness-of-fit statistics, but it affects the coefficients and p-values. The severity of multicollinearity and the primary goal of the regression model determine the need to address it. Multicollinearity can lead to wider confidence intervals, less reliable probabilities, and less stable model results. It is important to detect and address multicollinearity to ensure the reliability of the regression model