The correct answer is the logarithmic transformation applied to the dependent variable. This transformation directly addresses both issues of heteroscedasticity and non-normal residuals by compressing the scale of the data, which stabilizes non-constant variance and pulls skewed distributions closer to normality. In the context of the CompTIA Data+ DA0-001 exam, this question tests your understanding of linear regression assumptions and the practical remedies for when they are violated—a common scenario in real-world business data like sales and advertising spend. A frequent trap is confusing this with transforming the independent variable or using a different method like Box-Cox, but the log is the standard first-line fix for multiplicative relationships and diminishing returns. Remember the memory tip: “When variance grows with the mean, take the log to make it clean.”
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
A data analyst is building a linear regression model to predict sales based on advertising spend. The analyst notices that the residuals are not normally distributed and have a non‑constant variance. Which of the following transformations is most appropriate to apply to the dependent variable?
Why wrong: Standardization rescales data to mean 0 and std 1 but does not address heteroscedasticity or normality of residuals.
B
Normalization (min-max scaling)
Why wrong: Normalization only rescales to [0,1] and does not correct non‑constant variance or non‑normality.
C
Logarithmic transformation
Log transformation is commonly used to stabilize variance and make residuals more normally distributed.
D
Square root transformation
Why wrong: Square root transformation can help with variance but is less effective than log for non‑constant variance and does not guarantee normality.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Logarithmic transformation
The logarithmic transformation is the most appropriate choice because it stabilizes non‑constant variance (heteroscedasticity) and helps make the residuals more normally distributed, which are key assumptions for linear regression. By compressing the scale of the dependent variable (sales), it reduces the impact of large values and often linearizes multiplicative relationships, such as diminishing returns from advertising spend.
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.
✗
Standardization (z-score)
Why it's wrong here
Standardization rescales data to mean 0 and std 1 but does not address heteroscedasticity or normality of residuals.
✗
Normalization (min-max scaling)
Why it's wrong here
Normalization only rescales to [0,1] and does not correct non‑constant variance or non‑normality.
✓
Logarithmic transformation
Why this is correct
Log transformation is commonly used to stabilize variance and make residuals more normally distributed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Square root transformation
Why it's wrong here
Square root transformation can help with variance but is less effective than log for non‑constant variance and does not guarantee normality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that any scaling technique (standardization or normalization) can fix heteroscedasticity or non‑normality, but these methods only change the range or center of the data, not the shape of the residual distribution or the variance structure.
Detailed technical explanation
How to think about this question
Under the hood, a logarithmic transformation assumes that the variance of the residuals is proportional to the square of the mean, a pattern often seen in financial or sales data where larger values have larger fluctuations. In practice, applying log(sales) converts a multiplicative error model (sales = exp(β₀ + β₁·spend + ε)) into an additive one, making ordinary least squares (OLS) assumptions more tenable. A subtle behavior: if the dependent variable contains zeros, a log transformation fails, and analysts often use log(1 + y) or a Box-Cox transformation as alternatives.
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: Logarithmic transformation — The logarithmic transformation is the most appropriate choice because it stabilizes non‑constant variance (heteroscedasticity) and helps make the residuals more normally distributed, which are key assumptions for linear regression. By compressing the scale of the dependent variable (sales), it reduces the impact of large values and often linearizes multiplicative relationships, such as diminishing returns from advertising spend.
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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