Sample Questions
Question 1
Easy
An error correction model (ECM) is appropriate when:
Solution
A is correct.
An error correction model is used when variables are cointegrated — they share a long-run equilibrium relationship despite being individually non-stationary. The ECM includes both first-differenced terms (capturing short-run dynamics) and an error correction term (capturing the speed of adjustment back to the long-run equilibrium). This allows the model to incorporate both short-term fluctuations and long-run relationships.
An error correction model is used when variables are cointegrated — they share a long-run equilibrium relationship despite being individually non-stationary. The ECM includes both first-differenced terms (capturing short-run dynamics) and an error correction term (capturing the speed of adjustment back to the long-run equilibrium). This allows the model to incorporate both short-term fluctuations and long-run relationships.
Question 2
Medium
Principal Component Analysis (PCA) is a technique used to:
Solution
C is correct.
PCA is an unsupervised dimensionality reduction technique. It transforms a set of possibly correlated variables into a smaller number of uncorrelated variables called principal components. Each component captures the maximum remaining variance in the data. PCA is widely used to simplify high-dimensional datasets while retaining most of the information.
PCA is an unsupervised dimensionality reduction technique. It transforms a set of possibly correlated variables into a smaller number of uncorrelated variables called principal components. Each component captures the maximum remaining variance in the data. PCA is widely used to simplify high-dimensional datasets while retaining most of the information.
Question 3
Hard
Using the HY-class precision and recall, the F1 score for the HY class is closest to:
Solution
B is correct.
The F1 score is the harmonic mean of precision and recall, which penalizes imbalance between the two more than a simple average would. For the HY class, and .
Because the harmonic mean lies below the arithmetic mean whenever precision and recall differ, the F1 score (81.3%) is lower than the simple average (81.6%).
The F1 score is the harmonic mean of precision and recall, which penalizes imbalance between the two more than a simple average would. For the HY class, and .
Because the harmonic mean lies below the arithmetic mean whenever precision and recall differ, the F1 score (81.3%) is lower than the simple average (81.6%).
