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  3. Introduction to Linear Modeling in Python

Exercise

Residual Sum of the Squares

In a previous exercise, we saw that the altitude along a hiking trail was roughly fit by a linear model, and we introduced the concept of differences between the model and the data as a measure of model goodness.

In this exercise, you'll work with the same measured data, and quantifying how well a model fits it by computing the sum of the square of the "differences", also called "residuals".

Instructions

100 XP
  • Load the x_data, y_data with the pre-defined load_data() function.
  • Call the pre-defined model(), passing in x_dataand specific values a0, a1.
  • Compute the residuals as y_data - y_model and then find rss by using np.square() and np.sum().
  • Print the resulting value of rss.