Definition of Multicollinearity:
The measurement of a dependent variable existing with two different independent variables.
The existence of a perfect or nearly perfect linear correlation between a set of variables when the regression of some dependent variable on them is being investigated; an instance of this.
Multicollinearity is the occurrence of high intercorrelations among independent variables in a multiple regression model. Multicollinearity can lead to skewed or misleading results when a researcher or analyst attempts to determine how well each independent variable can be used most effectively to predict or understand the dependent variable in a statistical model. In general, multicollinearity can lead to wider confidence intervals and less reliable probability values for the independent variables. That is, the statistical inferences from a model with multicollinearity may not be dependable.
Statistical analysts use multiple regression models to predict the value of a specified dependent variable based on the values of two or more independent variables. The dependent variable is sometimes referred to as the outcome, target, or criterion variable. An example is a multivariate regression model that attempts to anticipate stock returns based on items like price-to-earnings ratios, market capitalization, past performance, or other data. The stock return is the dependent variable and the various bits of financial data are the independent variables.
How to use Multicollinearity in a sentence?
- It is better to use independent variables that are not correlated or repetitive when building multiple regression models that use two or more variables.
- Multicollinearity among independent variables will result in less reliable statistical inferences.
- Multicollinearity is a statistical concept where independent variables in a model are correlated.
Meaning of Multicollinearity & Multicollinearity Definition