The answer is linearity between independent and dependent variables, independence of errors, and homoscedasticity (constant variance of residuals). These three core assumptions of linear regression are essential for valid ordinary least squares estimation because violations can produce biased coefficients, unreliable standard errors, and invalid hypothesis tests. The independence of errors assumption, in particular, is grounded in the Gauss-Markov theorem—correlated residuals, often seen in time series data as autocorrelation, undermine the best linear unbiased estimator property and can be detected with the Durbin-Watson test. On the CompTIA Data+ DA0-001 exam, this question tests your ability to distinguish statistical assumptions from data preparation steps; a common trap is confusing normality of errors (required for small-sample inference but not for OLS unbiasedness) with the core assumptions. Remember the mnemonic “LIH” for Linearity, Independence, Homoscedasticity—if any of these three is missing, your regression results may mislead.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE of the following are assumptions of linear regression? (Select THREE).
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
Homoscedasticity (constant variance of errors)
Why this is correct
Variance of errors should be constant.
Related concept
Read the scenario before looking for a memorised answer.
✓
Linearity between independent and dependent variables
Why this is correct
The relationship must be linear.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the normality assumption for errors with a normality assumption for the independent variables, leading them to incorrectly select Option A.
Detailed technical explanation
How to think about this question
Under the hood, the Gauss-Markov theorem guarantees that ordinary least squares (OLS) estimators are the Best Linear Unbiased Estimators (BLUE) only when assumptions of linearity, independence of errors, homoscedasticity, and no perfect multicollinearity hold. In practice, heteroscedasticity can be detected using the Breusch-Pagan test, and if present, robust standard errors (e.g., Huber-White sandwich estimators) can be used to correct inference. A real-world scenario is modeling housing prices where errors from nearby houses are correlated (spatial autocorrelation), violating independence and requiring spatial regression models.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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