1. . Exploratory Data Analysis (EDA) is:
(A) Compressing data only
(B) Encrypting datasets only
(C) The process of analyzing datasets to summarize their main characteristics using visual and statistical methods
(D) Backup only
2. . The main goal of EDA is to:
(A) Understand data patterns, detect anomalies, and check assumptions before modeling
(B) Encrypt assumptions
(C) Compress patterns
(D) Backup only
3. . Common statistical measures used in EDA include:
(A) Backup only
(B) Encrypting statistics only
(C) Compressing statistics only
(D) Mean, median, mode, standard deviation, and variance
4. . The standard deviation in EDA measures:
(A) Backup only
(B) Encrypting data variation
(C) Compressing dispersion
(D) The amount of variation or dispersion of a dataset
5. . Skewness indicates:
(A) Compressing distribution
(B) Encrypting asymmetry
(C) The asymmetry of the data distribution
(D) Backup only
6. . Kurtosis measures:
(A) Compressing peaks
(B) Encrypting tails
(C) The "tailedness" or peakedness of the data distribution
(D) Backup only
7. . Outliers in EDA are:
(A) Backup only
(B) Encrypting outliers
(C) Compressing outliers
(D) Data points that are significantly different from other observations
8. . Box plots in EDA are used to:
(A) Compress plots
(B) Encrypt box data
(C) Visualize the distribution, median, quartiles, and outliers of a dataset
(D) Backup only
9. . Histograms in EDA help to:
(A) Show the frequency distribution of a dataset
(B) Encrypt frequencies
(C) Compress histogram
(D) Backup only
10. . Scatter plots are used in EDA to:
(A) Encrypt plots
(B) Visualize relationships or correlations between two variables
(C) Compress plots
(D) Backup only
11. . Pair plots in EDA are used to:
(A) Compress pair data
(B) Encrypt pairs
(C) Visualize pairwise relationships between multiple variables simultaneously
(D) Backup only
12. . Correlation analysis in EDA helps to:
(A) Measure the strength and direction of the relationship between two variables
(B) Encrypt correlations
(C) Compress correlation data
(D) Backup only
13. . Heatmaps in EDA are used for:
(A) Backup only
(B) Encrypting heatmaps
(C) Compressing maps
(D) Visualizing correlation matrices or large datasets
14. . Missing value analysis in EDA identifies:
(A) Encrypting missing data
(B) Incomplete or null entries in the dataset
(C) Compressing nulls
(D) Backup only
15. . Frequency tables in EDA are used to:
(A) Summarize the count of unique values in categorical variables
(B) Encrypt frequency
(C) Compress tables
(D) Backup only
16. . EDA helps in feature selection because:
(A) Backup only
(B) Encrypting features
(C) Compressing features
(D) It identifies important variables and relationships relevant for modeling
17. . Categorical variable visualization in EDA is often done using:
(A) Backup only
(B) Encrypting categories
(C) Compressing charts
(D) Bar charts or count plots
18. . Continuous variable visualization in EDA is often done using:
(A) Encrypting continuous data
(B) Histograms, box plots, or density plots
(C) Compressing continuous data
(D) Backup only
19. . Outlier treatment in EDA may include:
(A) Backup only
(B) Encrypting outliers
(C) Compressing outliers
(D) Removing, capping, or transforming extreme values
20. . The main purpose of EDA is to:
(A) Encrypt data only
(B) Understand, visualize, and summarize data to prepare for modeling and analysis
(C) Compress files only
(D) Backup only