Multicollinearity is a frequent issue in regression analysis. It happens when the independent variables in an analysis are extremely correlated and cause instability when formulating the coefficients of a regression model. This can affect the accuracy and validity of the regression model which makes it vital for analysts and researchers to tackle multicollinearity efficiently. In this article, we will examine the root causes of multicollinearity, its effects, and the different ways to deal with it.
[b]Data Science Course in Pune[/b]
Understanding Multicollinearity:
Multicollinearity is when two or more variables of a regression model are extremely dependent. The correlation may be non-linear or linear, but it can be problematic since it makes the process more difficult to separate the distinct impact of every independent variable concerning the dependent variables. Multicollinearity in itself does not affect the predictive ability of the model, but it could affect the reliability and accuracy of the calculated coefficients.
Causes of Multicollinearity:
Several factors contribute to multicollinearity:
A high correlation between predictors If two or more variables from different sources are extremely in correlation, it can be difficult to determine their factors in that dependent variable.
Redundancy of data: In some cases the variables could offer redundant information, leading to multicollinearity. For instance, including the height of inches as well as centimeters of height in a model can create multicollinearity.
Measurement error: Imperfect measurements or inaccuracy in the data could cause multicollinearity because they can introduce noise that alters the relationship between variables.
