Exercise

# Simple linear regressions

As you have seen, seaborn provides a convenient interface to generate complex and great-looking statistical plots. One of the simplest things you can do using seaborn is to fit and visualize a simple linear regression between two variables using `sns.lmplot()`

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One difference between seaborn and regular matplotlib plotting is that you can pass pandas DataFrames directly to the plot and refer to each column by name.
For example, if you were to plot the column `'price'`

vs the column `'area'`

from a DataFrame `df`

, you could call `sns.lmplot(x='area', y='price', data=df)`

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In this exercise, you will once again use the DataFrame `auto`

containing the auto-mpg dataset. You will plot a linear regression illustrating the relationship between automobile weight and horse power.

Instructions

**100 XP**

- Import
`matplotlib.pyplot`

and`seaborn`

using the standard names`plt`

and`sns`

respectively. - Plot a linear regression between the
`'weight'`

column (on the x-axis) and the`'hp'`

column (on the y-axis) from the DataFrame`auto`

. - Display the plot as usual with
`plt.show()`

. This has been done for you, so hit 'Submit Answer' to view the plot.