The Linear Regression is the simplest non-trivial relationship. The biggest mistake one can make is to perform a regression analysis that violates one of its assumptions! So, it is important to consider these assumptions before applying regression analysis on the dataset.

This article focuses both on the assumptions and measures to fix them in case the dataset violates it.

  1. Linearity: The specified model must represent a linear relationship.

This is the simplest assumption to deal with as it signifies that the relationship between dependent and independent variable is linear wherein independent variable is multiplied by its coefficient to obtain dependent…

Himanshu Garg


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