MCQs

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Overall MCQs for Reference

What is the primary goal of Exploratory Data Analysis?
a. Predict outcomes
b. Summarize data characteristics
c. Build models
d. Only visualize predictions
Answer: b. Summarize data characteristicsplacementpreparation
Which technique is most commonly used during EDA?
a. Clustering
b. PCA
c. Descriptive statistics
d. Feature selection
Answer: c. Descriptive statisticsplacementpreparation
What visualization best shows the relationship between two numerical variables in EDA?
a. Histogram
b. Boxplot
c. Scatter plot
d. Bar chart
Answer: c. Scatter plotplacementpreparation
Why is detecting skewness important in EDA?
a. Helps feature scaling
b. Determines model type
c. Affects data distribution assumptions
d. Improves visualization
Answer: c. Affects data distribution assumptionsplacementpreparation
What is a common statistical test performed during EDA?
a. t-test
b. ROC curve
c. K-means
d. Neural network
Answer: a. t-testsimplilearn
Which is a graphical summary technique in univariate analysis?
a. Scatter plot
b. Histogram
c. Correlation matrix
d. Heatmap
Answer: b. Histogramsimplilearn
Which library is widely used in Python for EDA plots?
a. TensorFlow
b. matplotlib
c. PyTorch
d. Keras
Answer: b. matplotlibplacementpreparation
Which of the following is a form of bivariate analysis?
a. Box plot
b. Scatter plot
c. Frequency table
d. Pie chart
Answer: b. Scatter plotsimplilearn
EDA helps in identifying which aspect?
a. Model accuracy
b. Data patterns
c. Feature scaling
d. Algorithm efficiency
Answer: b. Data patternssimplilearn
What is a measure of central tendency?
a. Range
b. Variance
c. Mean
d. Mode
Answer: c. Meansimplilearn
Detecting outliers is crucial in EDA because:
a. It affects the mean and variance
b. Removes duplicates
c. Decides the data type
d. Finds unique features
Answer: a. It affects the mean and variancepubnub+1
Which visualization best identifies outliers?
a. Pair plot
b. Box plot
c. Bar chart
d. Line plot
Answer: b. Box plotsimplilearn
What is the first step in EDA?
a. Model building
b. Data cleaning
c. Data visualization
d. Understanding the data
Answer: d. Understanding the datasimplilearn
What is data imputation?
a. Removing missing values
b. Filling missing data
c. Changing data type
d. Plotting data
Answer: b. Filling missing datasimplilearn
Which of the following detects multicollinearity?
a. PCA
b. Correlation matrix
c. Feature scaling
d. Scatter plot
Answer: b. Correlation matrixplacementpreparation+1
During EDA, data standardization is used to:
a. Reduce outliers
b. Normalize scales
c. Visualize correlation
d. Summarize patterns
Answer: b. Normalize scalessimplilearn
Dimensionality reduction is performed in EDA primarily to:
a. Speed up computation
b. Remove missing values
c. Increase model accuracy
d. Simplify patterns
Answer: a. Speed up computationsimplilearn
Which is NOT a step in data cleaning for EDA?
a. Handle missing values
b. Remove duplicates
c. Build predictive model
d. Correct data types
Answer: c. Build predictive modelsimplilearn
Which is a descriptive statistic used in EDA?
a. t-SNE
b. Pair plot
c. Standard deviation
d. Neural network
Answer: c. Standard deviationsimplilearn
What does a heatmap visualize?
a. Feature scaling
b. Correlation matrix
c. Data distribution
d. Outliers
Answer: b. Correlation matrixsimplilearn
In EDA, pair plots are useful for:
a. Identifying outliers
b. Visualizing variable interactions
c. Data scaling
d. Removing duplicates
Answer: b. Visualizing variable interactionssimplilearn
Kernel density estimate plots are primarily used for:
a. Categorical data
b. Ordinal data
c. Continuous data
d. Missing value detection
Answer: c. Continuous datasimplilearn
Which step is typically last in EDA?
a. Data cleaning
b. Documentation and reporting
c. Model selection
d. Feature generation
Answer: b. Documentation and reportingsimplilearn
What does a histogram best represent in EDA?
a. Data distribution
b. Data relationships
c. Missing values
d. Models
Answer: a. Data distributionsimplilearn
Which is NOT a EDA visual technique?
a. Bar chart
b. Line plot
c. ROC curve
d. Box plot
Answer: c. ROC curvesimplilearn
Why is feature engineering important during EDA?
a. It finds missing values
b. It creates new descriptive variables
c. It deletes outliers
d. It plots data
Answer: b. It creates new descriptive variablessimplilearn
What is the major outcome of EDA?
a. Data insights
b. Final models
c. Hyper-tuning
d. Deployment plan
Answer: a. Data insightssimplilearn
Which of the following is NOT part of EDA?
a. Descriptive statistics
b. Checking normality
c. Outlier detection
d. Estimating population parameters
Answer: d. Estimating population parametersbrainscape
EDA helps validate which assumption before model building?
a. Normality
b. Model accuracy
c. Hyperparameter tuning
d. Training data size
Answer: a. Normalitybrainscape+1
What is the main difference between EDA and inferential statistics?
a. EDA summarizes and visualizes data
b. EDA tests statistical significance
c. Inferential statistics always use visualization
d. Inferential statistics clean data
Answer: a. EDA summarizes and visualizes dataibm+1
Which is a common step of univariate analysis?
a. Fitting regression line
b. Plotting histogram
c. Scatter plot
d. Cross-tabulation
Answer: b. Plotting histogramsimplilearn
Which of the following is suitable for analyzing categorical data distribution?
a. Box plot
b. Bar chart
c. Scatter plot
d. Pair plot
Answer: b. Bar chartsimplilearn
The process of integrating multiple datasets in EDA is known as:
a. Feature engineering
b. Data cleaning
c. Data integration
d. Data modeling
Answer: c. Data integrationsimplilearn
How does EDA handle duplicate records?
a. Ignores duplicates
b. Visualizes duplicates
c. Removes duplicates
d. Estimates duplicates
Answer: c. Removes duplicatessimplilearn
Which of these identifies variable relationships?
a. Heatmap
b. Histogram
c. Pie chart
d. Bar chart
Answer: a. Heatmapsimplilearn
What principal method is used for dimensionality reduction during EDA?
a. PCA
b. kNN
c. SVM
d. Logistic regression
Answer: a. PCAsimplilearn
Which visualization tool detects missing values most effectively?
a. Matrix plot
b. Scatter plot
c. Pair plot
d. Pie chart
Answer: a. Matrix plotsimplilearn
The process of creating new variables from existing data is called:
a. Feature engineering
b. Data selection
c. Data normalization
d. Outlier detection
Answer: a. Feature engineeringsimplilearn
What statistic is commonly used to understand spread in EDA?
a. Median
b. Standard deviation
c. Mode
d. Frequency
Answer: b. Standard deviationsimplilearn
Outlier detection techniques in EDA include:
a. Z-score analysis
b. ROC curve
c. Gradient boosting
d. Data normalization
Answer: a. Z-score analysisitfeature+1
Which summary statistic describes the center of the data?
a. Mean
b. Standard deviation
c. Range
d. Variance
Answer: a. Meansimplilearn
Which visualization would NOT be used in EDA?
a. Scatter plot
b. Histogram
c. Precision-Recall curve
d. Pair plot
Answer: c. Precision-Recall curvesimplilearn
What is the purpose of cross-tabulation in EDA?
a. Show bivariate relationships
b. Handle missing values
c. Detect outliers
d. Apply feature scaling
Answer: a. Show bivariate relationshipssimplilearn
Which process does NOT occur in EDA?
a. Hypothesis testing
b. Model evaluation
c. Data visualization
d. Descriptive analytics
Answer: b. Model evaluationpubnub+1
Advanced visualization techniques in EDA include:
a. Violin plot
b. Pie chart
c. ROC curve
d. Precision-Recall curve
Answer: a. Violin plotsimplilearn
What descriptive statistic is least affected by outliers?
a. Mean
b. Median
c. Standard deviation
d. Range
Answer: b. Mediansimplilearn
Which data visualization is best for time series data in EDA?
a. Bar chart
b. Line plot
c. Box plot
d. Heatmap
Answer: b. Line plotpubnub+1
Data summarization in EDA includes:
a. Measures of central tendency
b. ROC curves
c. Neural networks
d. Model deployment
Answer: a. Measures of central tendencypubnub+1
What is an example of multivariate analysis in EDA?
a. Pair plot
b. Histogram
c. Bar chart
d. Line plot
Answer: a. Pair plotsimplilearn
Which process commonly iterates during EDA for deeper understanding?
a. Summarization
b. Documentation
c. Refinement of analysis
d. Model deployment
Answer: c. Refinement of analysisguvi+1

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