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 TWO of the following are common assumptions of linear regression?
Why wrong: Independence is assumed, but it is not listed among the options as correct; however, the question asks for TWO, and B and D are the most commonly cited assumptions.
B
No multicollinearity
Why wrong: Multicollinearity is not an assumption; it is a condition that can be problematic.
C
Linearity of the relationship
Assumes linear relationship between predictors and outcome.
D
Normality of the dependent variable
Why wrong: Normality of errors is assumed for inference, not the dependent variable.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Linearity of the relationship
Linear regression assumes that the relationship between the independent and dependent variables is linear (option C). This means the model expects that a unit change in the predictor results in a constant change in the outcome, which is the core assumption for ordinary least squares (OLS) estimation to produce unbiased coefficients.
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.
✗
Independence of observations
Why it's wrong here
Independence is assumed, but it is not listed among the options as correct; however, the question asks for TWO, and B and D are the most commonly cited assumptions.
✗
No multicollinearity
Why it's wrong here
Multicollinearity is not an assumption; it is a condition that can be problematic.
✓
Linearity of the relationship
Why this is correct
Assumes linear relationship between predictors and outcome.
Related concept
Read the scenario before looking for a memorised answer.
✗
Normality of the dependent variable
Why it's wrong here
Normality of errors is assumed for inference, not the dependent variable.
✓
Homoscedasticity
Why this is correct
Assumes constant variance of residuals.
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 often confuse the assumption of normality of residuals with normality of the dependent variable, leading them to incorrectly select option D instead of recognizing that homoscedasticity (option E) is a core assumption.
Detailed technical explanation
How to think about this question
Under the hood, linear regression relies on the Gauss-Markov theorem, which states that OLS estimators are the best linear unbiased estimators (BLUE) when assumptions of linearity, constant variance (homoscedasticity), and uncorrelated errors hold. Homoscedasticity (option E) ensures that the variance of residuals is constant across all levels of the independent variable, which is critical for valid hypothesis testing and confidence intervals. In a real-world scenario like predicting housing prices, if the spread of residuals increases with price (heteroscedasticity), standard errors become unreliable, leading to incorrect p-values.
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: Linearity of the relationship — Linear regression assumes that the relationship between the independent and dependent variables is linear (option C). This means the model expects that a unit change in the predictor results in a constant change in the outcome, which is the core assumption for ordinary least squares (OLS) estimation to produce unbiased coefficients.
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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