A stationary AR(1) process will eventually converge toward:
Select an option first.
Correct answer: B — Its long‑run mean
Explanation: Why right: Stationary AR(1) processes revert to their mean. Why wrong: A/C/D: Not implied.
Question 214
ARCH behavior is most associated with:
Select an option first.
Correct answer: B — Volatility clustering
Explanation: Why right: ARCH models capture periods of high/low volatility. Wrong: A/C/D: Not relevant.
Question 215
A common test for ARCH is to:
Select an option first.
Correct answer: A — Regress squared residuals on lagged squared residuals
Explanation: Why right: This checks whether past shocks predict current variance. Wrong: B/C/D: Not ARCH tests.
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Explanation: Forecast error increases with horizon.
Question 223
A model with DW = 0.10 should be:
Select an option first.
Correct answer: B — Re‑specified to address serial correlation
Explanation: DW near 0 → strong positive serial correlation → model invalid.
Question 224
If a series shows strong seasonal patterns, the model should include:
Select an option first.
Correct answer: B — Seasonal lags or seasonal dummies
Explanation: Seasonality must be explicitly modeled.
Question 225
A correctly specified AR model should have residuals that are:
Select an option first.
Correct answer: A — Serially uncorrelated
Explanation: Residuals must be white noise.
Question 226
A model with uncorrelated residuals and stable variance is:
Select an option first.
Correct answer: A — Likely well‑specified
Explanation: Why right: These are key diagnostics of a good model. Why wrong: B/C/D: Not implied.
Question 227
Using bigrams like “stock_market” helps:
Select an option first.
Correct answer: A — Capture phrase‑level meaning
Explanation: Why A is right: Bigrams preserve local context and meaning. Why others are wrong: B: Bigrams usually increase dimensionality. C/D: Not their purpose.
Question 228
A common method to address class imbalance is:
Select an option first.
Correct answer: A — Oversampling the minority class
Explanation: Why A is right: Oversampling helps the model learn minority patterns. Why others are wrong: B: Eliminates the class of interest. C: Accuracy is unreliable. D: Removes useful data.
Question 229
Undersampling the majority class can lead to:
Select an option first.
Correct answer: A — Loss of useful information
Explanation: Why A is right: Removing majority samples risks discarding valuable patterns. Why others are wrong: B: Dataset shrinks. C/D: Not affected.
Question 230
A correlation matrix is used to:
Select an option first.
Correct answer: B — Show relationships between multiple numeric variables
Explanation: Why B is right: Correlation matrices summarize pairwise relationships. Why others are wrong: A: Use histograms. C: It only reveals multicollinearity. D: Not its purpose.
Question 231
High correlation between two features suggests:
Select an option first.
Correct answer: A — They may be redundant
Explanation: Why A is right: Highly correlated features often provide overlapping information. Why others are wrong: B: Keeping both may cause multicollinearity. C/D: Not implied.
Question 232
Multicollinearity occurs when:
Select an option first.
Correct answer: A — Features are highly correlated
Explanation: Why A is right: High correlation among features causes instability in models. Why others are wrong: B/C/D: Not definitions.
Question 233
A common way to handle multicollinearity is to:
Select an option first.
Correct answer: A — Remove one of the correlated features
Explanation: Why A is right: Removing redundant features stabilizes models. Why others are wrong: B: Makes it worse. C/D: Not solutions.
Question 234
Standardization typically transforms a numeric feature to have:
Select an option first.
Correct answer: A — Mean 0 and standard deviation 1
Explanation: Why A is right: Standardization centers data at zero and scales it to unit variance. Why others are wrong: B: Not guaranteed. C: Standardization doesn’t change data type. D: Variance becomes 1, not 0.
Question 235
Feature engineering can include:
Select an option first.
Correct answer: A — Creating interaction terms
Explanation: Why A is right: Interaction terms capture combined effects of variables. Why others are wrong: B/C/D: Not engineering.
Question 236
A feature with many missing values may:
Select an option first.
Correct answer: A — Be removed
Explanation: Why A is right: If missingness is too high, the feature may be unreliable. Why others are wrong: B/C/D: Not always appropriate.
Question 237
A heat map of correlations helps identify:
Select an option first.
Correct answer: A — Multicollinearity
Explanation: Why A is right: High correlation values indicate potential multicollinearity. Why others are wrong: B/C/D: Not correlation‑related.
Question 238
A pair of features with correlation near zero are:
Select an option first.
Correct answer: A — Weakly related
Explanation: Why A is right: Near‑zero correlation indicates little linear relationship. Why others are wrong: B: Opposite. C/D: Not necessarily true.
Question 239
A feature that is strongly correlated with the target but also with many other features may:
Select an option first.
Correct answer: A — Cause multicollinearity
Explanation: Why A is right: Strong correlations across features create instability in models. Why others are wrong: B/C/D: Not necessarily true.
Question 240
A distance metric that works well for binary data is:
Select an option first.
Correct answer: A — Hamming distance
Explanation: Why A is right: Hamming distance counts mismatches in binary vectors. Why others are wrong: B/C: Numeric distances. D: Measures angle, not mismatches.
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