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@ -73,8 +73,8 @@ We'll explain what variation and covariation are, and we'll show you several way
You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results.
This is true even if you measure quantities that are constant, like the speed of light.
Each of your measurements will include a small amount of error that varies from measurement to measurement.
Variables can also vary if you measure across different subjects (e.g. the eye colors of different people) or different times (e.g. the energy levels of an electron at different moments).
Every variable has its own pattern of variation, which can reveal interesting information about how that variable varies between measurements on the same observation as well as across observations.
Variables can also vary if you measure across different subjects (e.g., the eye colors of different people) or at different times (e.g., the energy levels of an electron at different moments).
Every variable has its own pattern of variation, which can reveal interesting information about how that it varies between measurements on the same observation as well as across observations.
The best way to understand that pattern is to visualize the distribution of the variable's values, which you've learned about in @sec-data-visualization.
We'll start our exploration by visualizing the distribution of weights (`carat`) of \~54,000 diamonds from the `diamonds` dataset.