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.
Cet exercice fait partie du cours
Statistical Thinking in Python (Part 2)
Instructions
- Compute the parameters for the slope and intercept using
np.polyfit()
. The Anscombe data are stored in the arraysx
andy
. - Print the slope
a
and interceptb
. - Generate theoretical \(x\) and \(y\) data from the linear regression. Your \(x\) array, which you can create with
np.array()
, should consist of3
and15
. To generate the \(y\) data, multiply the slope byx_theor
and add the intercept. - Plot the Anscombe data as a scatter plot and then plot the theoretical line. Remember to include the
marker='.'
andlinestyle='none'
keyword arguments in addition tox
andy
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!
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Perform linear regression: a, b
a, b = ____
# Print the slope and intercept
print(____, ____)
# Generate theoretical x and y data: x_theor, y_theor
x_theor = np.array([____, ____])
y_theor = ____ * ____ + ____
# Plot the Anscombe data and theoretical line
_ = ____
_ = ____
# Label the axes
plt.xlabel('x')
plt.ylabel('y')
# Show the plot
plt.show()