DA0-002 Data Analysis Practice Question
A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?
⚠ Common exam trap
CompTIA often tests the distinction between assumption validation (normality and homoscedasticity) and other regression diagnostics (autocorrelation, multicollinearity, influence) to see if candidates confuse model-building checks with residual assumption checks.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Q-Q plot to assess normality of residuals
A Q-Q plot is used to assess whether the residuals of a linear regression model are approximately normally distributed, which is a key assumption for valid inference (e.g., p-values and confidence intervals). Option E is correct because residual plots (e.g., fitted vs. residuals) are the standard diagnostic to check for homoscedasticity—constant variance of errors across all levels of the independent variables—another core assumption of ordinary least squares regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Durbin-Watson test for autocorrelation
Why it's wrong here
Autocorrelation is not a core assumption for linear regression.
- ✓
Q-Q plot to assess normality of residuals
Why this is correct
Q-Q plot checks normality assumption.
- ✗
Variance inflation factor (VIF) for multicollinearity
Why it's wrong here
Multicollinearity is a concern but not a core assumption.
- ✗
Cook's distance to identify influential points
Why it's wrong here
Influential points are diagnostics but not assumptions.
- ✓
Residual plots to check for homoscedasticity
Why this is correct
Residual plots help verify constant variance.
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