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function can be a useful tool for checking whether the simple regression model is appropriate for a dataset. It fits and removes a simple linear regression and then plots the residual values for each observation. Ideally, these values should be randomly scattered around y = 0 :

If there is structure in the residuals, it suggests that simple linear regression is not appropriate:

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to provide an easy interface to show a linear regression on “faceted” plots that allow you to explore interactions with up to three additional categorical variables.

The best way to separate out a relationship is to plot both levels on the same axes and to use color to distinguish them:

In addition to color, it’s possible to use different scatterplot markers to make plots the reproduce to black and white better. You also have full control over the colors used:

To add another variable, you can draw multiple “facets” which each level of the variable appearing in the rows or columns of the grid:

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is an “axes-level” function draws onto a specific axes. This means that you can make multi-panel figures yourself and control exactly where the regression plot goes. If no axes object is explictly provided, it simply uses the “currently active” axes, which is why the default plot has the same size and shape as most other matplotlib functions. To control the size, you need to create a figure object yourself.

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The Committee on Safeguards (the Safeguards Committee) was established to administer the Safeguards Agreement. It oversees the operation of the Agreement and is responsible for the surveillance of Members’ commitments. Governments have to notify and provide copies of their legislation authorizing the application of safeguard measures to the Committee, as well as report each phase of a safeguard investigation and related decision-making. The Committee reviews these reports and provides a forum for discussion of measures in place.

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