DA0-002 Data Analysis Practice Question
A data analyst is working with a dataset that contains a column 'income' with a highly skewed distribution. The analyst wants to apply a transformation to make the distribution more symmetric for use in a linear regression model. Which transformation is most appropriate?
⚠ Common exam trap
A common mix-up: candidates confuse scaling transformations (like standardization) with shape-changing transformations (like log). Scaling does not fix skewness.
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
✓
Logarithmic transformation
A logarithmic transformation is effective for reducing right skewness in positive data like income. It compresses the upper tail, making the distribution more symmetric and improving linearity for regression. Standardization and min-max normalization only rescale without changing shape. Binning discards information and does not achieve symmetry. Therefore, the logarithmic transformation is the correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max normalization
Why it's wrong here
Min-max normalization scales data to a fixed range, typically [0,1], but like standardization, it does not alter the shape of the distribution. Skewness persists. Min-max is sensitive to outliers, which are common in skewed data, and can compress the majority of data into a small range. It is not suitable for reducing skewness.
- ✗
Binning into equal-width intervals
Why it's wrong here
Binning converts a continuous variable into categorical bins, which loses information and does not make the distribution symmetric. It can actually obscure the underlying distribution. For linear regression, binning would require creating dummy variables and may not capture the continuous relationship. It is not an appropriate transformation for reducing skewness.
- ✗
Standardization (z-score normalization)
Why it's wrong here
Standardization rescales data to have a mean of 0 and a standard deviation of 1, but it does not change the shape of the distribution. A skewed distribution remains skewed after standardization. While standardization is useful for algorithms sensitive to scale, it does not address skewness. Therefore, it is not the most appropriate transformation for making the distribution more symmetric.
- ✓
Logarithmic transformation
Why this is correct
A logarithmic transformation is commonly used to reduce right skewness in positive data. Income data often has a long right tail, and taking the log compresses larger values more than smaller ones, making the distribution more symmetric. This can improve the performance of linear regression by making the relationship more linear and stabilizing variance. Thus, it is the most appropriate choice for this scenario.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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