Linear regression on appropriate Anscombe data

For practice, perform a linear regression on the data set from Anscombe's quartet that is most reasonably interpreted with linear regression.

Compute the parameters for the slope and intercept using np.polyfit(). The Anscombe data are stored in the arrays x and y.
Print the slope a and intercept b.
Generate theoretical \(x\) and \(y\) data from the linear regression. Your \(x\) array, which you can create with np.array(), should consist of 3 and 15. To generate the \(y\) data, multiply the slope by x_theor and add the intercept.
Plot the Anscombe data as a scatter plot and then plot the theoretical line. Remember to include the marker='.' and linestyle='none' keyword arguments in addition to x and y when to plot the Anscombe data as a scatter plot. You do not need these arguments when plotting the theoretical line.
Hit submit to see the plot!