Chapter 4 · Section 1 practice

Targets, Regression and OLS

0 of 3 answered

These questions review the examples in the companion slides. Choose an answer, then check the explanation.

Question 1

Which statement is justified by the OLS example?

  • Training R² proves the signal will make money
  • A coefficient measures a conditional fitted association, and future forecast quality needs chronological evaluation
  • Normal-looking features guarantee independent errors
  • Pending outcomes should be filled with zero before training

Question 2

Which condition is needed to define an identifiable OLS coefficient vector?

  • Every predictor is normally distributed
  • The design matrix has full column rank
  • The training R² exceeds 0.5
  • Every outcome is positive

Question 3

Adding an irrelevant regressor to OLS with an intercept can do what?

  • Reduce training R² mechanically
  • Leave or increase training R² while worsening validation error
  • Guarantee a lower validation RMSE
  • Eliminate residual variance