Assumptions of Linear Regression — What Fellow Data Scientists Should Know

Author(s): Shaurya Lalwani

Originally published on Towards AI.

Photo by Marius Masalar on Unsplash

Linear Regression is a linear approach to modeling the relationship between a target variable and one or more independent variables. This modeled relationship is then used for predictive analytics. Working on the linear regression algorithm is just half the work done. The other half lies in understanding the following assumptions that this technique depends on:

For linear regression to work on the given data, it is assumed that Errors (residuals) follow a normal distribution. Although this is not necessarily required when the sample size is very large. The normality can be verified using the Q-Q Plot… Read the full blog for free on Medium.

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Published via Towards AI