From the course: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
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Assumption 1: Checking for mean residuals - SQL Tutorial
From the course: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced
Assumption 1: Checking for mean residuals
- [Instructor] Now let's dive right in to our first pit stop. Assumption number one. You are going to check for something called mean residuals. Residuals are like leftover pieces of a puzzle. They represent the differences between your model's predictions, and the actual home values. In simpler terms, you want these residuals to behave nicely. To do that, you calculate their mean, which is just the average of all those leftover pieces. Think of it as checking if on average your aim is right on target when throwing darts at a bullseye. This code here is used to perform this calculation. Here's what we found when we calculated the mean of our residuals. We've gotten a number of -5.58 times 10 to the -15. Now, that might look like a complicated number, but what it's telling you is that the mean of the residuals is very, very close to zero. This is a good sign. It's like finding out that on average, your darts are…
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Creating the linear regression model and model summary: Part 19m 33s
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Creating the linear regression model and model summary: Part 27m 16s
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Creating the linear regression model and model summary: Part 35m 33s
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Dropping insignificant variables and re-creating the model7m 57s
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Checking assumptions for linear regression3m 18s
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Assumption 1: Checking for mean residuals2m 47s
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Assumption 2: Checking homoscedasticity3m 13s
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Assumption 3: Checking linearity2m 12s
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Assumption 4: Checking normality of error terms3m 24s
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Q-Q plot for checking the normality of error terms3m 14s
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Model performance comparison on train and test data6m 7s
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Applying cross-validation and evaluation4m 40s
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Challenge: Model building48s
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Solution: Model building1m 16s
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