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 is a part of the course
“Introduction to Linear Modeling in Python”
Exercise instructions
- Load the
x_data,y_datawith the pre-definedload_data()function. - Call the pre-defined
model(), passing inx_dataand specific valuesa0,a1. - Compute the residuals as
y_data - y_modeland then findrssby usingnp.square()andnp.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(____))