What is Multicollinearity? Multicollinearity occurs when two or more explanatory variables in the regression model are highly correlated with each other making it difficult to isolate their individual effects on the dependent variable. Multicollinearity has two types: Perfect Multicollinearity Imperfect Multicollinearity.
In the previous post, we discussed how to estimate a sample regression model, i.e., and . by applying the OLS method on sample data, both in simple and multiple linear regression models. You can read these posts here: A Numerical
Goodness of Fit So far, we have no way of measuring how well the explanatory or independent variable, X, explains the dependent variable, Y. In other words, we must know how well the OLS regression line fits the data. Are
Simple Linear Regression Model Simple Linear Regression Model is used to estimate the relationship between one dependent and one independent variable. It is also called bivariate or two variable regression model. For example, regression of consumption on disposable income, regression
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