MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that contains a 'Price' column. After plotting a histogram, they observe that the distribution is right-skewed with many extreme high values. They plan to use a linear model that assumes normally distributed errors. Which of the following transformations should they apply to the 'Price' column to make it more normally distributed?
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
✓
Apply log transformation (log(Price)).
Log transformation is commonly applied to right-skewed data to reduce skewness and make the distribution more normal, which is suitable for linear models assuming normally distributed errors. Option B (square transformation) exacerbates skewness, making it worse. Option C (min-max scaling) only rescales the data to a fixed range and does not change the shape of the distribution. Option D (binning) discards information and does not transform the distribution to be normal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply log transformation (log(Price)).
Why this is correct
Log transformation compresses the tail and makes the distribution more symmetric.
- ✗
Apply square transformation (Price^2).
Why it's wrong here
Square transformation makes right skew worse.
- ✗
Apply min-max scaling to the 'Price' column.
Why it's wrong here
Scaling does not change the shape of the distribution.
- ✗
Bin the 'Price' values into equal-width intervals.
Why it's wrong here
Binning discretizes the data and loses information.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?
medium- A.Min-max scaling
- ✓ B.Log transformation
- C.Standardization (z-score)
- D.Binning
Why B: Log transformation compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.
Variation 2. During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?
easy- ✓ A.Apply log transformation.
- B.Standardize the features using Z-scores.
- C.Apply a sine transformation.
- D.Apply Box-Cox transformation with lambda=0.
Why A: A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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