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.
This exercise is part of the course
Statistical Thinking in Python (Part 2)
Exercise instructions
- Compute the parameters for the slope and intercept using
np.polyfit(). The Anscombe data are stored in the arraysxandy. - Print the slope and intercept.
- Generate theoretical \(x\) and \(y\) data from the linear regression. Your \(x\) values should consist of 3 and 15.
- Plot the Anscombe data as a scatter plot and the theoretical line.
- Label the axes (just \(x\) and \(y\) will do).
- Show your plot.
Hands-on interactive exercise
Have a go at this exercise by completing this sample 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()