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Multicollinearity occurs in a regression model when two or more independent variables are highly correlated with each other. This condition leads to issues in estimating the individual effect of each independent variable on the dependent variable, as the independent variables are not providing unique information. The problem of multicollinearity can have several consequences, and addressing it is crucial for ensuring the reliability and interpretability of regression results. One of the primary consequences of multicollinearity is that it inflates the standard errors of the estimated coefficients. When the independent variables are highly correlated, it becomes difficult for the model to distinguish the individual effects of each predictor. This inflation of standard errors leads to wider confidence intervals for the coefficients, which means that the estimated coefficients become less precise. As a result, we may fail to reject null hypotheses for individual predictors even when they are actually significant, thus leading to Type II errors (falsely accepting a false hypothesis). Another consequence is that the signs and magnitudes of the coefficients may become unstable. A small change in the data can cause large changes in the estimated coefficients. This sensitivity makes the model unreliable, as the estimated relationships between the dependent and independent variables can change drastically with minor variations in the data. This instability complicates the interpretation of the model and undermines the validity of any conclusions drawn from it. Multicollinearity also distorts the goodness-of-fit statistics, such as the _______ ______ _______ ____ __________ ________ __________.
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