MS-DS Master of Data science Master of Data science Exploratory Data Analysis 1 — Questions and Answers
Question 1: Which statistical measure is most resistant to the influence of outliers when describing the center of a dataset?
- Mean
- Median (Correct answer)
- Mode
- Standard deviation
Correct answer: Median
The median is resistant to outliers because it depends only on the middle value(s) of an ordered dataset, not the magnitude of extreme values. The mean, by contrast, is pulled toward outliers.
Question 2: A histogram of a feature shows that most values cluster near zero with a long tail extending to very large positive values. What transformation would most likely normalize this distribution?
- Square root or logarithmic transformation (Correct answer)
- Z-score standardization
- Min-max scaling
- One-hot encoding
Correct answer: Square root or logarithmic transformation
Log or square root transformations compress large values and spread small values, which is effective for right-skewed distributions common when data clusters near zero with a long positive tail. Standardization and scaling preserve shape.
Question 3: During EDA, you notice that two features have a Pearson correlation coefficient of –0.87. What does this indicate?
- The features are unrelated
- As one feature increases, the other tends to decrease strongly (Correct answer)
- The features have a non-linear relationship
- One feature causes the other to decrease
Correct answer: As one feature increases, the other tends to decrease strongly
A Pearson correlation of –0.87 indicates a strong negative linear relationship: as one variable increases, the other tends to decrease. Correlation does not imply causation, and Pearson only measures linear association.
Question 4: What is the primary purpose of a box plot in exploratory data analysis?
- To display the frequency distribution of a categorical variable
- To summarize the distribution of a continuous variable and highlight outliers (Correct answer)
- To show the correlation between two variables
- To compare proportions across multiple groups
Correct answer: To summarize the distribution of a continuous variable and highlight outliers
A box plot visualizes the five-number summary (minimum, Q1, median, Q3, maximum) and flags potential outliers as points beyond 1.5×IQR from the quartiles, making it effective for understanding spread, skew, and anomalies.
Question 5: In EDA, what does a high proportion of missing values in a feature column most directly suggest to an analyst?
- The feature should always be dropped from the dataset
- The feature may be systematically missing and requires investigation before deciding on imputation or removal (Correct answer)
- The missing values should be replaced with the column mean
- The dataset is too small to be useful
Correct answer: The feature may be systematically missing and requires investigation before deciding on imputation or removal
High missingness warrants investigation into whether the data is missing at random, missing completely at random, or missing not at random. The mechanism determines whether imputation, removal, or alternative handling is appropriate.
Question 6: Which plot is best suited for visualizing the distribution of a single continuous variable and detecting potential bimodality?
- Scatter plot
- Bar chart
- KDE (Kernel Density Estimate) plot (Correct answer)
- Heat map
Correct answer: KDE (Kernel Density Estimate) plot
A KDE plot smooths the data into a continuous probability density curve, making it easy to see whether the distribution has one peak (unimodal) or two (bimodal). Histograms can also reveal this but are sensitive to bin width; KDE provides a cleaner view.
MS-DS Master of Data science Master of Data science Exploratory Data Analysis 1
Which statistical measure is most resistant to the influence of outliers when describing the center of a dataset?