From the course: Machine Learning Foundations: Statistics
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The correlation vs. causation - Python Tutorial
From the course: Machine Learning Foundations: Statistics
The correlation vs. causation
- [Instructor] We have discovered that correlation measures how much two items are related to each other. The example that we used was the correlation between the number of rainy days per month and monthly umbrella sales. If we represent it graphically, we notice that as the number of rainy days increases, the monthly umbrella sales also increase. We obviously have a linear relationship. Our correlation coefficient equals 0.98, so there is a high positive relationship between the increase in rainy days and the increase in umbrella sales. The important thing to notice is that correlation does not imply causation. Some variables can be correlated, and still there could be zero causation. The results can be misleading. For example, we could calculate the correlation between pet ownership and living longer and get high correlation, but that still doesn't mean having a pet will increase your lifespan. People tend to…
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