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".

This exercise is part of the course

Introduction to Linear Modeling in Python

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the data
x_data, y_data = load_data()

# Model the data with specified values for parameters a0, a1
y_model = model(____, a0=150, a1=25)

# Compute the RSS value for this parameterization of the model
rss = np.sum(np.square(____ - ____))
print("RSS = {}".format(____))

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