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().

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).

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.

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.

Exercise

Simple linear regressions

Instructions