MCQs cover the fundamentals of Exploratory Data Analysis, covering data summarization, visualization techniques, handling anomalies, and inferring patterns from datasets. Ideal for data analysts and scientists to reinforce EDA practices using statistical and graphical methods.
1. What is the primary goal of Exploratory Data Analysis (EDA)?
✅ Correct Answer: b) To understand the data and uncover patterns
📝 Explanation:
EDA aims to summarize main characteristics, often using visual methods, to reveal insights and detect anomalies before modeling.
2. Which statistic measures the central tendency of a dataset?
✅ Correct Answer: b) Mean
📝 Explanation:
The mean is the average value, representing the center of the data distribution.
3. A histogram is primarily used for visualizing
✅ Correct Answer: b) Distribution of a single numerical variable
📝 Explanation:
Histograms divide continuous data into bins to show frequency distribution.
4. What does the interquartile range (IQR) represent?
✅ Correct Answer: b) The middle 50% of the data
📝 Explanation:
IQR = Q3 - Q1, measuring spread of the central half of the data.
5. In EDA, missing values are often identified using
✅ Correct Answer: b) Summary functions like isnull()
📝 Explanation:
Functions like df.isnull().sum() count missing entries per column.
6. Which plot is best for detecting outliers in numerical data?
✅ Correct Answer: b) Box plot
📝 Explanation:
Box plots display quartiles and whiskers, highlighting points beyond 1.5 * IQR.
7. The correlation coefficient ranges from
✅ Correct Answer: b) -1 to 1
📝 Explanation:
Pearson's r measures linear relationship strength and direction between -1 and 1.
8. Skewness measures the
✅ Correct Answer: b) Asymmetry of the distribution
📝 Explanation:
Positive skew indicates right tail, negative left; zero for symmetric.
9. A scatter plot visualizes
✅ Correct Answer: b) Relationship between two variables
📝 Explanation:
Scatter plots plot points for two continuous variables to show correlation or trends.
10. What is the purpose of a pair plot in EDA?
✅ Correct Answer: b) To visualize pairwise relationships in multivariate data
📝 Explanation:
Pair plots create a matrix of scatter plots and histograms for all variable pairs.
11. Kurtosis describes the
✅ Correct Answer: a) Tail heaviness relative to normal distribution
📝 Explanation:
High kurtosis indicates heavy tails and peaked center; low indicates flat.
12. For categorical data, which visualization is appropriate?
✅ Correct Answer: b) Bar chart
📝 Explanation:
Bar charts display frequencies or counts for discrete categories.
13. The standard deviation measures
✅ Correct Answer: b) Dispersion around the mean
📝 Explanation:
SD quantifies data variability; sqrt of variance.
14. In EDA, data scaling is often checked for
✅ Correct Answer: b) Feature ranges and distributions
📝 Explanation:
Scaling ensures features contribute equally, checked via min-max or z-scores.
15. A heatmap is used to visualize
✅ Correct Answer: b) Correlation matrices
📝 Explanation:
Heatmaps color-code values in a matrix, ideal for correlations.
16. What indicates a normal distribution in EDA?
✅ Correct Answer: a) Skewness = 0, Kurtosis = 3
📝 Explanation:
Normal distribution is symmetric (skew=0) with kurtosis=3 (mesokurtic).
17. Violin plots combine
✅ Correct Answer: b) Box plot and density plot
📝 Explanation:
Violin plots show distribution shape via kernel density and quartiles via box.
18. Multicollinearity is detected using
✅ Correct Answer: a) VIF (Variance Inflation Factor)
📝 Explanation:
VIF > 5-10 indicates high multicollinearity among features.
19. For time series EDA, which plot shows trends over time?
✅ Correct Answer: b) Line plot
📝 Explanation:
Line plots connect data points chronologically to reveal patterns.
20. The mode is the value that
✅ Correct Answer: b) Occurs most frequently
📝 Explanation:
Mode identifies the most common category or value in unimodal data.
21. In EDA, pivot tables are used for
✅ Correct Answer: a) Aggregating data by categories
📝 Explanation:
Pivot tables summarize data with rows, columns, and values for cross-tabulation.
22. A Q-Q plot assesses
✅ Correct Answer: a) Normality of distribution
📝 Explanation:
Q-Q plots compare quantiles to theoretical normal; straight line indicates normality.
23. Handling outliers in EDA often involves
✅ Correct Answer: b) Capping or removing after investigation
📝 Explanation:
Outliers may be errors or insights; decisions based on domain knowledge.
24. Categorical variables are encoded in EDA using
✅ Correct Answer: a) One-hot encoding
📝 Explanation:
One-hot creates binary columns for categories to avoid ordinal assumptions.
25. The coefficient of variation (CV) is
✅ Correct Answer: a) SD / Mean * 100%
📝 Explanation:
CV measures relative variability, useful for comparing dispersion across datasets.
26. Seaborn library in Python is popular for EDA because it
✅ Correct Answer: b) Provides statistical visualizations
📝 Explanation:
Seaborn builds on Matplotlib for attractive, informative plots like heatmaps.
27. In bivariate analysis, a low correlation implies
✅ Correct Answer: b) Weak or no linear relationship
📝 Explanation:
Correlation near 0 suggests little linear association between variables.
28. Data profiling in EDA includes
✅ Correct Answer: b) Summary statistics and data quality checks
📝 Explanation:
Profiling overviews structure, types, missingness, and stats.
29. A kernel density estimate (KDE) plot shows
✅ Correct Answer: b) Smooth probability density
📝 Explanation:
KDE approximates continuous distribution using a kernel function.
30. For imbalanced classes in EDA, check
✅ Correct Answer: a) Class distribution via bar plots
📝 Explanation:
Visualize target variable frequencies to identify imbalance.
31. The five-number summary includes
✅ Correct Answer: a) Min, Q1, Median, Q3, Max
📝 Explanation:
Used in box plots to describe data spread without assuming distribution.
32. In EDA, feature engineering starts with
✅ Correct Answer: a) Creating new variables from existing
📝 Explanation:
Derive interactions, polynomials, or bins to capture patterns.
33. A lag plot in time series EDA detects
✅ Correct Answer: b) Autocorrelation
📝 Explanation:
Plots value against its lagged version to show serial dependence.
34. Z-score for outlier detection is
✅ Correct Answer: a) (x - mean) / SD
📝 Explanation:
|Z| > 3 often flags outliers assuming normality.
35. Count plots are used for
✅ Correct Answer: b) Categorical frequencies
📝 Explanation:
Similar to bar charts but for single categorical variable counts.
36. In EDA, dimensionality reduction preview uses
✅ Correct Answer: a) PCA scree plot
📝 Explanation:
Shows explained variance by components to decide retention.
37. Median is robust to
✅ Correct Answer: a) Outliers
📝 Explanation:
Unlike mean, median resists extreme values.
38. Joint plots in Seaborn combine
✅ Correct Answer: b) Scatter, marginal histograms, and correlation
📝 Explanation:
For bivariate EDA with univariate margins.
39. Variance is the average of
✅ Correct Answer: a) Squared deviations from mean
📝 Explanation:
Variance = Σ(x_i - μ)^2 / n, measuring spread.
40. For text data in EDA, start with
✅ Correct Answer: a) Word clouds or n-gram frequencies
📝 Explanation:
Visualize common terms and phrases in unstructured text.
41. Autocorrelation function (ACF) plot identifies
✅ Correct Answer: b) Serial correlations in time series
📝 Explanation:
ACF shows correlation of series with its lags.
42. In EDA, data types include
✅ Correct Answer: b) Numerical, categorical, ordinal, datetime
📝 Explanation:
Understanding types guides appropriate analysis and visualization.
43. A facet grid in EDA allows
✅ Correct Answer: a) Subplotting by categories
📝 Explanation:
Splits plots into a grid conditioned on variables for comparisons.
44. Pearson correlation assumes
✅ Correct Answer: a) Linear relationship and normality
📝 Explanation:
For continuous variables; use Spearman for non-parametric.
45. Box plot whiskers typically extend to
✅ Correct Answer: a) 1.5 * IQR from quartiles
📝 Explanation:
Beyond whiskers are potential outliers.
46. In EDA, resampling checks
✅ Correct Answer: a) Stability of statistics
📝 Explanation:
Bootstrap or jackknife assesses variability in estimates.
47. Cramér's V measures
✅ Correct Answer: a) Association between categorical variables
📝 Explanation:
Ranges 0-1; chi-square based for nominal data.
48. Log transformation in EDA is used to
✅ Correct Answer: a) Handle skewed data
📝 Explanation:
Reduces right skew, stabilizing variance.
49. A swarm plot displays
✅ Correct Answer: a) Points without overlap for categorical
📝 Explanation:
Shows individual data points spread to avoid stacking.
50. In multivariate EDA, parallel coordinates plot
✅ Correct Answer: a) High-dimensional data as lines
📝 Explanation:
Each line represents an observation across normalized axes.
51. The range is
✅ Correct Answer: a) Max - Min
📝 Explanation:
Simplest spread measure, sensitive to outliers.
52. For geospatial data in EDA, use
✅ Correct Answer: a) Choropleth maps
📝 Explanation:
Color regions by data values for spatial patterns.
53. Chi-square test in EDA checks
✅ Correct Answer: a) Independence between categorical variables
📝 Explanation:
Null hypothesis: no association in contingency tables.
54. Strip plots show
✅ Correct Answer: a) Jittered points for categorical
📝 Explanation:
Adds random noise to positions to reveal overplotting.
55. In EDA, cross-validation previews
✅ Correct Answer: d) All of the above
📝 Explanation:
Early CV checks generalization before full modeling.
56. Quantile-quantile (Q-Q) plot compares
✅ Correct Answer: a) Sample to theoretical distribution
📝 Explanation:
Deviations from line indicate non-conformity.
57. For ordinal data, use
✅ Correct Answer: a) Ordinal encoding
📝 Explanation:
Assigns integers preserving order, unlike nominal.
58. Partial dependence plots explain
✅ Correct Answer: a) Feature effects in models
📝 Explanation:
Shows marginal effect of a feature on prediction.
59. In EDA, binning continuous data creates
✅ Correct Answer: a) Categorical intervals
📝 Explanation:
Reduces noise and reveals patterns in discretized form.
60. Spearman's rank correlation is
✅ Correct Answer: a) Non-parametric for monotonic relations
📝 Explanation:
Based on ranks, robust to non-normality.
61. A ridgeline plot visualizes
✅ Correct Answer: a) Overlapping density distributions
📝 Explanation:
Stacks shifted KDEs for category comparisons.
62. EDA documents findings via
✅ Correct Answer: a) Notebooks like Jupyter
📝 Explanation:
Combines code, visuals, and narrative for reproducibility.
63. Theil's U measures
✅ Correct Answer: a) Asymmetric association for categorical
📝 Explanation:
Uncertainty coefficient based on entropy.
64. In time series, decomposition separates
✅ Correct Answer: a) Trend, seasonality, residual
📝 Explanation:
Additive or multiplicative models isolate components.
65. For high-cardinality categoricals in EDA, use
✅ Correct Answer: a) Frequency or target encoding
📝 Explanation:
Reduces dimensions while retaining information.
66. A contour plot shows
✅ Correct Answer: a) Density levels in 2D
📝 Explanation:
Lines or colors indicate constant value regions.
67. EDA hypothesis generation leads to
✅ Correct Answer: a) Confirmatory analysis
📝 Explanation:
Patterns suggest testable hypotheses for further stats.
68. Mahalanobis distance detects
✅ Correct Answer: a) Multivariate outliers
📝 Explanation:
Accounts for covariance, unlike Euclidean.
69. In EDA, groupby operations compute
✅ Correct Answer: a) Aggregates by categories
📝 Explanation:
Like mean by group in pandas for subgroup analysis.
70. A hexbin plot is a
✅ Correct Answer: a) 2D histogram with hexagons
📝 Explanation:
Efficient for dense scatter data to show counts.
71. EDA for regression checks
✅ Correct Answer: a) Linearity, homoscedasticity via residuals
📝 Explanation:
Scatter of residuals vs fitted predicts assumptions.
72. Polychoric correlation for
✅ Correct Answer: a) Ordinal variables
📝 Explanation:
Assumes underlying continuous latent variables.
73. In EDA, melting data changes
✅ Correct Answer: a) Wide to long format
📝 Explanation:
Facilitates plotting multiple series.
74. SHAP values in EDA preview
✅ Correct Answer: a) Feature contributions post-model
📝 Explanation:
Explains individual predictions for interpretability.
75. For survival data EDA, Kaplan-Meier estimates
✅ Correct Answer: d) All of the above
📝 Explanation:
Non-parametric curve for time-to-event analysis.
76. A sunburst plot visualizes
✅ Correct Answer: a) Hierarchical data
📝 Explanation:
Nested rings show proportions in categories.
77. In EDA, Levene's test checks
✅ Correct Answer: a) Equal variances
📝 Explanation:
Robust to non-normality for ANOVA assumptions.
78. Treemap displays
✅ Correct Answer: a) Proportional areas for categories
📝 Explanation:
Rectangles sized by value in hierarchical layout.
79. EDA for clustering previews with
✅ Correct Answer: a) Elbow method on k-means
📝 Explanation:
Plots inertia vs k to suggest optimal clusters.
80. Biserial correlation for
✅ Correct Answer: a) Continuous and binary variables
📝 Explanation:
Point-biserial if binary is true dichotomy.
81. In EDA, pivot_longer in R or melt in Python
✅ Correct Answer: a) Reshapes wide to long
📝 Explanation:
Prepares tidy data for analysis.
82. LIME explains
✅ Correct Answer: a) Local model interpretations
📝 Explanation:
Approximates black-box models locally with interpretable ones.
83. For network data EDA, use
✅ Correct Answer: a) Graph visualizations like node-link
📝 Explanation:
Shows nodes and edges for connectivity patterns.
84. Anderson-Darling test assesses
✅ Correct Answer: a) Goodness-of-fit to distribution
📝 Explanation:
Sensitive to tail deviations from normal.
85. Sankey diagram illustrates
✅ Correct Answer: a) Flow between categories
📝 Explanation:
Width proportional to magnitude in multi-stage processes.
86. EDA for classification includes
✅ Correct Answer: a) Class separation via LDA plot
📝 Explanation:
Linear Discriminant Analysis projects for discriminability.
87. Tetrachoric correlation assumes
✅ Correct Answer: a) Binary data from underlying continuous
📝 Explanation:
For dichotomous variables implying latent scale.
88. In EDA, dcast or pivot in tools
✅ Correct Answer: a) Long to wide reshaping
📝 Explanation:
Spreads variables for certain summaries.
89. Counterfactual explanations in EDA show
✅ Correct Answer: a) What-if scenarios for predictions
📝 Explanation:
Alters features to see outcome changes.
90. For audio data EDA, spectrograms visualize
✅ Correct Answer: a) Frequency over time
📝 Explanation:
2D representation of signal spectrum.
91. Kolmogorov-Smirnov test compares
✅ Correct Answer: a) Distributions
📝 Explanation:
Maximum difference in empirical CDFs.
92. Alluvial plot shows
✅ Correct Answer: a) Changes in category proportions over stages
📝 Explanation:
Like Sankey but for categorical shifts.
93. In EDA for anomaly detection, isolation forest previews
✅ Correct Answer: a) Anomaly scores
📝 Explanation:
Path length in trees indicates isolation.
94. Phi coefficient for
✅ Correct Answer: a) 2x2 contingency association
📝 Explanation:
Pearson's r for binary variables.
95. Gather function in tidyverse
✅ Correct Answer: a) Wide to long
📝 Explanation:
Tidy data principle for analysis.
96. Anchored explanations focus on
✅ Correct Answer: a) Comparison to baseline prediction
📝 Explanation:
Highlights feature impacts relative to reference.
97. For image data EDA, use
✅ Correct Answer: a) Pixel histograms or t-SNE embeddings
📝 Explanation:
Visualize color distributions or latent spaces.
98. Shapiro-Wilk test for
✅ Correct Answer: a) Normality
📝 Explanation:
Powerful for small samples.
99. Chord diagram visualizes
✅ Correct Answer: a) Interconnections between categories
📝 Explanation:
Arc segments with linking ribbons.
100. EDA for recommendation systems includes
✅ Correct Answer: a) User-item matrix sparsity
📝 Explanation:
Percentage of missing interactions.
101. Contingency coefficient for
✅ Correct Answer: a) Categorical association beyond 2x2
📝 Explanation:
Chi-square based, asymmetric.
102. Spread function in R
✅ Correct Answer: a) Long to wide
📝 Explanation:
Pivots values into columns.
103. Prototype-based explanations use
✅ Correct Answer: a) Nearest neighbors as exemplars
📝 Explanation:
Shows similar cases for context.
104. For video data EDA, frame sampling and
✅ Correct Answer: a) Optical flow analysis
📝 Explanation:
Motion vectors between frames.
105. Jarque-Bera test combines
✅ Correct Answer: a) Skewness and kurtosis for normality
📝 Explanation:
Omnibus test against normal.
106. Parallel sets plot for
✅ Correct Answer: a) Multi-category flows like alluvial
📝 Explanation:
Ribbon bands for proportions.
107. In reinforcement learning EDA, check
✅ Correct Answer: a) State-action distributions
📝 Explanation:
Exploration coverage in environments.
108. Lambda coefficient for
✅ Correct Answer: a) Asymmetric nominal association
📝 Explanation:
Predictive reduction in error.
109. Unpivot in data tools
✅ Correct Answer: a) Wide to long
📝 Explanation:
Standardizes column structure.
110. Subgroup explanations in EDA target
✅ Correct Answer: a) Specific data slices
📝 Explanation:
Tailored insights for segments.
111. For graph data EDA beyond basics, centrality measures like
✅ Correct Answer: a) Degree, betweenness
📝 Explanation:
Node importance in networks.
112. D'Agostino's K-squared test for
✅ Correct Answer: a) Normality via skew and kurtosis
📝 Explanation:
Asymptotic chi-square.
113. Mosaic plot for
✅ Correct Answer: a) Categorical contingency visualization
📝 Explanation:
Tiled bars for associations.
114. EDA in causal inference previews with
✅ Correct Answer: a) DAGs for confounding
📝 Explanation:
Directed Acyclic Graphs map relationships.
115. Uncertainty coefficient for
✅ Correct Answer: a) Nominal predictive association
📝 Explanation:
Entropy-based, asymmetric.
116. Pivot_table in pandas for
✅ Correct Answer: a) Multi-index aggregation
📝 Explanation:
Flexible crosstabs with functions.
117. Contrastive explanations compare
✅ Correct Answer: a) Prediction to counterfactual
📝 Explanation:
Minimal changes for outcome flip.
118. For tabular data EDA, automated tools like
✅ Correct Answer: a) Pandas Profiling
📝 Explanation:
Generates comprehensive HTML reports.
119. Lilliefors test is a
✅ Correct Answer: a) KS variant without specified distribution
📝 Explanation:
For normality, parameters estimated from data.
120. Dot plot alternative to
✅ Correct Answer: a) Bar for small categories
📝 Explanation:
Points on axis for frequencies.
121. In federated learning EDA, focus on
✅ Correct Answer: a) Local dataset summaries without sharing
📝 Explanation:
Preserves privacy in distributed settings.
122. Goodman-Kruskal gamma for
✅ Correct Answer: a) Ordinal association
📝 Explanation:
Accounts for tied pairs in ranks.
123. Reshape in Python for
✅ Correct Answer: a) Data format changes
📝 Explanation:
Stack, melt, pivot for tidy data.
124. Input gradient explanations use
✅ Correct Answer: a) Model derivatives w.r.t. input
📝 Explanation:
Sensitivity of prediction to features.
125. For sensor data EDA, time-frequency analysis like
✅ Correct Answer: a) Wavelet transforms
📝 Explanation:
Localizes events in time and scale.
126. Cramér-von Mises test for
✅ Correct Answer: a) Goodness-of-fit
📝 Explanation:
Integral of squared CDF differences.
127. Streamgraph for
✅ Correct Answer: a) Stacked area over time
📝 Explanation:
Centered for flowing appearance.
128. EDA in A/B testing checks
✅ Correct Answer: a) Group balance and power
📝 Explanation:
Pre-test for valid comparisons.
129. Kappa coefficient for
✅ Correct Answer: a) Inter-rater agreement beyond chance
📝 Explanation:
For categorical ratings.
130. Tidy data principles in EDA ensure
✅ Correct Answer: a) One variable per column, observation per row
📝 Explanation:
Facilitates manipulation and analysis.
131. Guided backpropagation for
✅ Correct Answer: a) Salient feature visualization in images
📝 Explanation:
Modifies gradients for positive relevance.
132. For financial time series EDA, candlestick charts show
✅ Correct Answer: a) OHLC prices
📝 Explanation:
Open, High, Low, Close for volatility.
133. Anderson-Rubin test in
✅ Correct Answer: a) Instrumental variable strength
📝 Explanation:
Weak instrument detection.
134. Waffle chart for
✅ Correct Answer: a) Proportional parts like pie alternative
📝 Explanation:
Grid squares for percentages.
135. EDA for NLP includes
✅ Correct Answer: a) Token length distributions, vocab size
📝 Explanation:
Text-specific summaries.
136. Yule's Q for
✅ Correct Answer: a) 2x2 table association
📝 Explanation:
Dichotomous measure from odds ratio.
137. Stack in pandas for
✅ Correct Answer: b) Columns to rows
📝 Explanation:
Longer format from wide.
138. Layer-wise relevance propagation for
✅ Correct Answer: a) Deep net heatmaps
📝 Explanation:
Backpropagates relevance scores.
