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Linear regression is basically a method of exhibiting the relation a dependent variable (y) has with other independent parameters (X). Two forms of linear regression are:
- Single Linear Regression –
In this form, there is only one explanatory factor.
- Multiple Linear Regression –
Here as the name suggests, there are multiple explanatory factors or variables.
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Few Assumptions of multiple linear regression:
- Multicollinearity:
It can be detected by checking
- Correlation among IVs (individual variables).
- Tolerance figures.
- VIF (Variance Inflation Factor).
- Homoscedasticity:
- This means equal distribution of the dissimilaritiesaround the line of regression.
- In theabsence of normality or when there are no normal variables, there would be heteroscedasticity.
- Linear relation:
There is a linear relation between the dependent and one independent variable, while other parameters remain constant.
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Many techniques of statistics like Poisson regression, logistic regression, survival analysis, etc. are all based on the theory of multiple linear regression. So for higher studies of any of these areas of analysis, your concept of multiple linear regression methods has to be very clear. That is the only way to become a successful statistical analyst.
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