MULTICILLINEARITY IN A REGRESSION MODEL

special emphasis in the theoretical framework and in the model, then it is suggested in

the literature to use the collinear variable even by using Ridge regression with out

dropping the main variable.

which are theoretically and practically related to the model. If we do so, we will commit

model specification error. So keep those variables intact and look for other ways to

solve multicollinearity issue such as transform the variables into log and so on.

if you find multicollinearity, exclude the correlated variables and proceed the results.

remove variables, you'll loose information. Apply PCA (principle component Analysis).

this technique transform the correlated variable into uncorrelated variabl to perform

regression safely.

and wife jointly donate resources to build a house. It will be hard for the man to drive out

his wife out of the house they jointly built as this will cause problems. This is term sine

qua non in Latin clause. Also, multicollinearity means what God has joined together, let

no man put asunder.

correlations between two or more predictor variables. In other words, one predictor

variable can be used to predict the other. This creates redundant information, skewing

the results in a regression model. Examples of correlated predictor variables are: a

person’s height and weight, age and sales price of a car, or years of education and

annual income.

An easy way to detect multicollinearity is to calculate correlation coefficients for all pairs

of predictor variables. If the correlation coefficient, r, is exactly +1 or -1, this is called

perfect multicollinearity. If r is close to or exactly -1 or +1, one of the variables should be

removed from the model if at all possible. Data-based multicollinearity: caused by poorly

designed experiments, data that is 100% observational, or data collection methods that

cannot be manipulated. In some cases, variables may be highly correlated (usually due

to collecting data from purely observational studies) and there is no error on the

researcher’s part. For this reason, you should conduct experiments whenever possible,

setting the level of the predictor variables in advance. Structural multicollinearity:

caused by you, the researcher, creating new predictor variables.

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Meo School of Research

Shishir Shakya

Noman Arshed

Multicollinearity

Hossain Academy Note

Univariate Models |

Multivariate Models |

Panel Data Model |