**Multicollinearity, **

### 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