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Assumption 2: Checking homoscedasticity

Assumption 2: Checking homoscedasticity - SQL教程

课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced

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Assumption 2: Checking homoscedasticity

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- [Instructor] All right, now that you've ensured that your model isn't biased, let's move on to your next pit stop, assumption two. We're going to check for something called homoscedasticity. But what does that even mean? Well, homoscedasticity is a bit of a mouthful, but it's a crucial concept in linear regression. It's all about how the residuals, those leftover pieces we talked about earlier, are spread out around your regression line. Imagine your baking cookies and you want them all to be the same size. That's what you want with your residuals. You want them to be evenly spread out around your regression line, just like those perfectly uniform cookies. The regression line is like your baking guide, helping you achieve this even distribution of residuals for more accurate predictions. Now the opposite of homoscedasticity is something called heteroscedasticity. If the residuals aren't evenly spread, but instead form…

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