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Non-Multicollinearity Test in Multiple Linear Regression

When analyzing data using linear regression using the Ordinary Least Square (OLS) method, it takes an understanding of the assumption test that must be passed. The non-multicollinearity test is necessary to get the best linear unbiased estimator. The multiple linear regression OLS method has been widely applied in various fields: economics, agribusiness, and socio-economic fields. The estimation of the output of this linear regression has many benefits. Various research problems can be solved with this analytical approach. When we choose to use regression analysis, we are trying to see the influence or impact of one or more variables on other variables. Therefore, many researchers, lecturers, students, and practitioners choose linear regression using the OLS method as a data analysis tool.

How to Calculate a Simple Linear Regression using Excel

In statistics, simple linear regression analysis consists of only two variables. The two variables are one dependent variable and one independent variable. On this occasion, we will discuss how to calculate a simple linear regression using Excel.

How to Calculate a Multiple Linear Regression using Excel

Multiple linear regression analysis can determine the effect of one variable on other variables. In multiple linear regression, the number of independent variables observed is two or more than two variables. In this article, we specifically discuss multiple linear regression with two independent variables.

Difference Among Regression, Correlation, and Comparative Test


The ability to understand statistical analysis is increasingly important for us. Researchers, practitioners, and students often use statistical analysis to process research results. In economics, agribusiness, and social sciences, we often observe the relationship of variables associated with each other.