DA0-002 Data Analysis Practice Question
Which THREE of the following are assumptions of linear regression? (Select THREE).
⚠ Common exam trap
Many candidates confuse the normality assumption for errors with a normality assumption for the independent variables, leading them to incorrectly select Option A.
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
✓
Independence of errors
Independence of errors is a core assumption of linear regression, meaning the residuals (errors) should not be correlated with each other. This is critical for valid inference because correlated errors violate the Gauss-Markov theorem, leading to biased standard errors and unreliable hypothesis tests. In time series data, this assumption is often violated due to autocorrelation, which can be detected using the Durbin-Watson test.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normal distribution of independent variables
Why it's wrong here
Normal distribution of independent variables is not required; normality of errors is.
- ✗
Multicollinearity among independent variables
Why it's wrong here
Multicollinearity violates the assumption of no perfect collinearity; it's not an assumption.
- ✓
Independence of errors
Why this is correct
Errors should be independent.
- ✓
Homoscedasticity (constant variance of errors)
Why this is correct
Variance of errors should be constant.
- ✓
Linearity between independent and dependent variables
Why this is correct
The relationship must be linear.
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