What is Multicollinearity?
Multicollinearity is a statistical phenomenon in which two or more variables in a regression model are dependent upon the other variables in such a way that one can be linearly predicted from the other with a high degree of accuracy. It is generally used in observational studies and less popular in experimental studies.
Types of Multicollinearity
There are four types of Multicollinearity
- #1 – Perfect Multicollinearity – It exists when the independent variables in the equation predict the perfect linear relationship.
- #2 – High Multicollinearity – It refers to the linear relationship between the two or more independent variables which are not perfectly correlated to each other.
- #3 – Structural Multicollinearity – This is caused by the researcher himself by inserting different independent variables in the equation.
- #4 – Data based Multicollineaarity – It is caused by experiments that are poorly designed by the researcher.
Causes of Multicollinearity
Independent Variables, Change in the parameters of the Variables do that a little change in the variables. There is a significant impact on the result & Data Collection refers to the sample of the Selected population being taken.
Examples of Multicollinearity
Let’s assume that ABC Ltd, a KPO, has been hired by a pharmaceutical company to provide research services and statistical analysis on the diseases in India. For this, ABC ltd has selected age, weight, profession, height, and health as the prima facie parameters.
- In the above example, there is a multicollinearity situation since the independent variables selected for the study are directly correlated to the results. Hence it would be advisable for the researcher to adjust the variables first before starting any project since the results will be directly impacted because of the selected variables here.
Let’s assume that ABC Ltd has been appointed by Tata Motors to understand the sales volume of tata motors will be high in which category in the market.
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- In the above example, firstly, independent variables will be finalized based upon which the research needs to be completed. It can be monthly income, age. Brand, the lower class. It means only that data will be selected, which will fit into all these tabs in order to figure out how many people can buy this car ( tata nano ) without even looking at any other car.
Let’s Assume that ABC Ltd has been hired to submit a report to know how many people under 50 are prone to heart attacks. for this, the parameters are age, sex, medical history
- In the above example, there is multicollinearity that has arisen because the independent variable “age” needs to be tweaked to age under 50 for inviting applications from the public so that the persons who are more than 50 yrs of age automatically get filtered.
Below are some of the Advantages
- Linear Relationship between the Independent Variables in the equation.
- Very useful in statistical models and research reports prepared by the research-based firms.
- Direct impact on the desired result.
Below are some of the Disadvantages
- In some of the situations, this issue would be resolved by collecting more data on the variables.
- Incorrect use of dummy variables ie, the researcher may forget to use the dummy variables whenever needed.
- Inserting 2 same or identical variables in the equation like kg and lbs in weights.
- Inserting a variable in the equation which is a combination of 2.
- It is complicated to perform calculations since it is the statistical technique and requires statistical calculators to do the execution.
Multicollinearity is one of the most favored statistical tools often used in regression analysis and statistical analysis for large databases and the desired output. All major companies have a separate statistical department in their company to perform statistical regression analysis about products or people in order to provide a strategic view of the market to the management and also help them to draft their long term strategies keeping this mind. The Graphical presentation of the analysis gives the reader a clear picture of the direct relationship, accuracy, and performance.
- If the goal of the researcher is to understand the independent variables in the equation, then multicollinearity will be a big problem for him.
- The researcher needs to do the required changes in the variables at stage 0 itself, or else it may have a massive impact on the results.
- Multicollinearity can be done by examining the correlation matrix.
- Remedial measures play a significant role in solving the problems of multicollinearity.
This has been a guide to what is Multicollinearity and its definition. Here we discuss its formula, types along with examples, advantages, and disadvantages. You can learn more about excel modeling from the following articles –