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  3. Statistical Thinking in Python (Part 2)

Exercise

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.

Instructions

100 XP
  • 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!