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Checking correlation after removing outliers

Checking correlation after removing outliers - SQL Tutorial

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

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Checking correlation after removing outliers

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- [Instructor] Okay, in your previous step, you examine the relationship between highway access and property tax using a scatterplot. You discovered that while there was a strong correlation, no clear trend was visible, possibly due to outliers. So, what do we do next? Let's find out in our 10th step here. Outliers, as we learned earlier, can disrupt our analysis. They are data points that don't quite follow the usual pattern and can lead to misleading results. In this step, you're taking a proactive approach by removing these outliers associated with high property tax rates. That is, over 600. This is the Python code that does that for you. With the outliers temporarily set aside, we're now going to calculate the correlation between property tax and highway access using a statistical method called Pearson's Correlation Coefficient. Now, Pearson's Correlation Coefficient is like a data detective. It tells us how two…

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