R-Squared

In this exercise you'll compute another measure of goodness, R-squared. R-squared is the ratio of the variance of the residuals divided by the variance of the data we are modeling, and in so doing, is a measure of how much of the variance in your data is "explained" by your model, as expressed in the spread of the residuals.

Here we have pre-loaded the data x_data,y_data and the model predictions y_model for the best fit model; you're goal is to compute the R-squared measure to quantify how much this linear model accounts for variation in the data.

Compute the residuals, by subtracting the y_data from the y_model, and the deviations, by subtracting the y_data from the np.mean() of the y_data.
Compute the variance of the residuals and the variance of the deviations, using np.mean() and np.square() to each.
Compute the r_squared as 1 minus the ratio var_residuals / var_deviations, and print the result.

Exercise

R-Squared

Instructions