Multicollinearity is used to describe the problem of using variables that strongly correlate with one another in a multiple linear regression model.
Multiple linear regression refers to a statistical technique that is used to predict the outcome of a variable based on the value of two or more variables. It is sometimes known simply as multiple regression, and it is an extension of linear regression. The variable that we want to predict is known as the dependent variable, while the variables we use to predict the value of the dependent variable are known as independent or explanatory variables.
Multicollinearity, also known as collinearity, is a phenomena in statistics when one predictor variable in a multiple regression model can be linearly predicted from the others with a high level of accuracy. In this case, minor adjustments to the model or the data may cause the multiple regression's coefficient estimates to fluctuate unpredictably. Multicollinearity only impacts calculations pertaining to specific predictors; it has no impact on the predictive capability or reliability of the model as a whole, at least within the sample data set. In other words, a multivariate regression model with collinear predictors can show how well the entire set of predictors predicts the outcome variable, but it might not provide accurate information about any particular predictor or about which predictors are redundant with respect to another predictor.
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