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?