MMiDS 2.5: Self-Assessment Quiz

In linear regression, the goal is to find coefficients \( \beta_j \)'s that minimize which of the following criteria?

How does the least squares method solve for the coefficients in matrix form?

The normal equations for linear regression are:

In the numerical example with a degree-20 polynomial fit, the fitted curve:

Which of the following best describes overfitting?

What is the primary advantage of using simulated data to test the least squares method?