Linear Regression Assumptions

Stephanie Glen has a chart which explains the four key assumptions behind when Ordinary Least Squares is the Best Linear Unbiased Estimator:

If any of the main assumptions of linear regression are violated, any results or forecasts that you glean from your data will be extremely biased, inefficient or misleading. Navigating all of the different assumptions and recommendations to identify the assumption can be overwhelming (for example, normality has more than half a dozen options for testing).

Violating one of the assumptions isn’t the end of the world, though it can make understanding the model and generating accurate predictions harder.

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