multi liner regression

A Detailed Guide to Multiple Linear Regression

The statistic is one of the essential tools that help us simplify our day to day analytic problems. The Multiple Linear Regression is one such method that enables you to predict analysis most efficiently. The regressions analysis can help you find out how much predictive data is associated with each predictor when you control or remove any correlation with all the taken predictors. Multiple Linear Regression calculator can help you run your statistical analysis quite quickly. On taking about the significance of Multiple Linear Regression, here we are sharing a detailed insight of method and its advantages:

Multiple Linear Regression:

Multiple linear regression is also known as multiple regression. As a predictive analysis, this statistic technique is used to define the association within the two or more independent variables and a single continuous dependent variable. The technique model the linear relationship between the explanatory and response variable

Formula: yi=β0+β1xi1+β2xi2+…+βpxip+ϵ

Where for i=n observations:

yi=dependent variable
xi=explanatory variables
β0 = y-intercept (constant term)
βp =slope coefficients for each explanatory variable
ϵ = the model’s error term

Following assumptions defines the execution of multiple linear regression:

  • The linear relationship between both the independent and dependant variable
  • Different Independent variables are not highly correlated.
  • Dependent variable observations are picked randomly and independently within the population.
  • Regression residuals should be normally distributed.


An entrepreneur has 50 employees working in his company. He wishes to analyses how the employees’ work efficiency is influenced by different variables like QI, emotional health, physical health, and community support.

Here, we presume that the work efficiency of the employees influenced by metal, physical, and emotional health along with social support. However, we don’t have any exact no. or ration to define the reality or at what extant the factors are affecting the employees’ performance. The multiple linear regression offers numerical estimates of such relations.

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Advantages of Multiple linear regression calculators:

  • The technique helps in determining the relative influence of two or more predictor variables to a criterion value.
  • The formula enables you to set definitive outliners to reach a particular result. You can exclude the factors that are not representing the needs of your model.
  • Multiple linear regression calculators can help different business sectors like real estate, research institutes, educational organizations, management departments, and more to identify the relationship and influence of varying variables.