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Checking assumptions for linear regression

Checking assumptions for linear regression - SQL Tutorial

From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced

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Checking assumptions for linear regression

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- [Instructor] What's up? And welcome back. After your model refinement in the last step, step 16, where you trimmed away less significant variables to create a more efficient model, the next logical step is to ensure your model is performing as expected. So why are we doing this now? Well, think of it as similar to testing a newly built car to make sure it runs smoothly. When you've honed your model down to a more streamlined version, you want to ensure it behaves reliably. To do that, you assess a set of fundamental assumptions that have become a crucial part of linear regression analysis, and here they are. Starting with assumption one, check for mean residuals. Imagine your predictions as darts thrown on a dartboard. You aim for the bullseye, and ideally, you want your darts to land evenly around it. If on average you consistently miss the mark, it suggests your model needs some adjustment. Assumption number two, check…

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