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WorksheetsBusiness Analytics Worksheet
Total questions: 94
Worksheet time: 2hrs 34mins
Business analytics primarily focuses on:
Data collection only
Data analysis and decision-making
Software programming
Accounting processes
Which of the following is NOT a type of business analytics?
Descriptive
Predictive
Prescriptive
Reactive
Descriptive analytics answers which question?
What happened?
Why did it happen?
What will happen?
What should we do?
Predictive analytics is mainly used to:
Summarize historical data
Forecast future trends
Optimize resource allocation
Reduce costs
Prescriptive analytics suggests:
What action should be taken
What happened in the past
Which data is missing
How to clean data
The main goal of business analytics is to:
Replace management
Improve decision-making
Increase coding knowledge
Automate emails
Which of the following best describes 'Big Data'?
Small structured data
High volume, velocity, and variety data
Data stored in Excel
Only numerical data
Data cleaning is also called:
Data validation
Data wrangling
Data merging
A dashboard in analytics is used to:
Visualize key performance metrics
Store raw data
Perform data entry
Delete redundant data
A KPI stands for:
Key Process Information
Key Performance Indicator
Knowledge Performance Insight
Key Predictive Index
The first step in data analytics is:
Data cleaning
Data collection
Data modeling
Data visualization
Which tool is widely used for data visualization?
Tableau
Word
Notepad
Photoshop
Structured data is:
Data in predefined format like tables
Free-text data
Images and videos
Social media posts
Unstructured data examples include:
A. Emails, videos, and tweets
B. Sales spreadsheets
C. SQL tables
D. Financial statements
Which of the following is NOT a data analytics tool?
Power BI
Python
Excel
Microsoft Word
Correlation measures:
A scatter plot is used to show:
Relationship between two numerical variables
Frequency distribution
Hierarchical data
Text analytics
Regression analysis helps to:
Predict dependent variable values
Describe historical events
Group similar records
Create pie charts
In regression, the dependent variable is also known as:
Predictor
Outcome variable
Independent variable
Factor variable
The R² value indicates:
Variance explained by the model
Sample size
Number of predictors
Data errors
A histogram represents:
Frequency distribution of numerical data
Categorical comparisons
Time series
Pie chart distribution
Which programming language is most popular in analytics?
Python
HTML
C++
PHP
Data mining is used to:
Discover patterns from large datasets
Delete old data
Reduce storage space
Encrypt data
A decision tree helps in:
Classification and prediction
Data cleaning
Regression only
Correlation testing
Which of the following is an open-source data visualization library in Python?
Matplotlib
Excel
PowerPoint
Access
In SQL, the command used to extract data is:
UPDATE
SELECT
INSERT
DELETE
In Excel, the function =AVERAGE(A1:A5) calculates:
Median
Mean
Mode
Range
Which of the following is a cloud-based analytics tool?
Google Data Studio
MS Paint
Notepad++
Adobe Reader
ETL stands for:
Extract, Transform, Load
Evaluate, Test, Launch
Execute, Transfer, Link
Extract, Transfer, Log
Data warehousing is used for:
Centralized storage of historical data
Email communication
Financial accounting
Marketing automation
In time series analysis, trend refers to:
An outlier is:
An extreme value differing from others
Missing data
Median value
Mode of data
Data normalization is used to:
Scale variables to a standard range
Delete columns
Merge datasets
Increase bias
A confusion matrix is used to evaluate:
Classification models
Regression models
Time series
Clustering
In machine learning, supervised learning involves:
Labeled data
Unlabeled data
Random data
Missing data
Which algorithm is commonly used for classification?
K-Nearest Neighbors (KNN)
K-Means
Apriori
PCA
Clustering is an example of:
Unsupervised learning
Supervised learning
Reinforcement learning
Statistical inference
The 'elbow method' is used to find:
Optimal number of clusters in K-Means
Regression coefficients
Correlation strength
Time series seasonality
Logistic regression predicts:
Binary outcomes
Continuous outcomes
Missing values
Random noise
Multicollinearity occurs when:
Independent variables are highly correlated
Dependent variables are missing
Outliers exist
Sample size is small
Principal Component Analysis (PCA) is used for:
Dimensionality reduction
Regression
Classification
Forecasting
Which measure evaluates model accuracy for regression?
RMSE
F1-Score
Recall
Precision
Cross-validation helps to:
Check model performance on unseen data
Increase training size
Detect missing data
Normalize inputs
A heatmap is best used for:
Showing correlations among variables
Displaying time series
Classifying data
Text mining
A box plot shows:
Data distribution and outliers
Sales by category
Time trends
Market share
In probability, the sum of all possible outcomes equals:
0
1
100
Type I error occurs when:
A true null hypothesis is rejected
A false null hypothesis is accepted
Data is incomplete
Sampling is biased
The p-value indicates:
Strength of evidence against null hypothesis
Sample size
Data range
Population mean
A low p-value (<0.05) indicates:
Strong evidence against null hypothesis
Strong support for null hypothesis
Missing values
No significance
A hypothesis test helps to:
Make inferences about population parameters
Measure data entry speed
Clean data
Generate visuals
Optimization in business analytics aims to:
Find the best possible decision under given constraints
Randomly choose options
Increase data storage
Visualize charts only
Linear programming is used for:
Resource allocation problems
Text mining
Image recognition
Regression analysis
Decision variables in linear programming represent:
Values to be determined for optimization
Constraints
Fixed parameters
Random factors
The objective function in optimization represents:
The goal to maximize or minimize
Random data variation
Constraints in a model
Time period
Sensitivity analysis studies:
How changes in inputs affect outputs
Customer sentiment
Regression coefficients
Data validation errors
A Monte Carlo simulation is used to:
Model uncertainty using random variables
Clean data
Create dashboards
Analyze text data
What is the main purpose of forecasting models?
Predict future values based on historical data
Describe current status
Optimize cost structure
Identify outliers
The moving average method is used for:
Smoothing time series data
Regression analysis
Outlier detection only
Data collection
An exponential smoothing model gives:
More weight to recent observations
Equal weight to all observations
Less weight to recent data
Random weights
Which of the following is NOT a time series component?
Trend
Seasonality
Random variation
Hypothesis
Prescriptive analytics in supply chain management helps to:
Optimize inventory and logistics decisions
Store data securely
Track only shipments
In finance, analytics is often used for:
Risk modeling and portfolio optimization
Employee payroll
Brand image
Advertising design
Customer churn prediction uses which type of analytics?
Predictive
Descriptive
Diagnostic
Prescriptive
Market basket analysis is commonly applied in:
Retail recommendation systems
HR analytics
Logistics planning
Financial forecasting
The Apriori algorithm is used for:
Association rule mining
Regression analysis
Time series
Classification
Support and confidence are measures in:
Association rules
Clustering
Regression
Time series
Sentiment analysis is most used in:
Text and social media analytics
Time series
Regression
Supply chain optimization
Which metric measures accuracy in classification?
(TP + TN) / (TP + TN + FP + FN)
RMSE
R²
Chi-square
Precision is defined as:
(a)
Recall measures:
How many actual positives are correctly predicted
False positive rate
Model bias
Data variance
An ROC curve plots:
True Positive Rate vs. False Positive Rate
Precision vs. Recall
Accuracy vs. Error
Cost vs. Profit
A confusion matrix is a table used to evaluate:
Classification model performance
Regression accuracy
Market share
Financial ratios
In predictive analytics, overfitting occurs when:
Model performs well on training but poorly on test data
Data is under-sampled
Model generalizes well
There’s no missing data
Regularization techniques are used to:
Prevent overfitting
Increase bias
Reduce sample size
Normalize categorical data
The “What-If” analysis in Excel helps managers:
Analyze different scenarios and outcomes
Collect new data
Create pie charts
Clean datasets
In HR analytics, predictive models can forecast:
Employee attrition
Company tax rates
Office expenses
Brand recall
Dashboard interactivity in Power BI is achieved through:
Filters and slicers
Data encryption
SQL scripts only
PDF exports
Data storytelling combines:
Analytics, visualization, and communication
Fiction writing
Database design
Coding techniques
A business analyst primarily acts as:
A. A bridge between technical teams and management
B. A data entry clerk
C. A salesperson
D. A finance auditor
Key outcome of business analytics is:
Data-driven decision making
Manual reporting
Increased paperwork
Random experimentation
In case of missing data, imputation means:
Replacing missing values with estimates
Deleting all records
Random sampling
Ignoring missing variables
Data governance ensures:
Data quality, security, and compliance
Marketing decisions
Cost accounting
Software testing
A KPI dashboard should be:
Interactive, clear, and goal-oriented
Complex and technical
Static and lengthy
Confidential only
Business Intelligence (BI) focuses mainly on:
Reporting and descriptive analytics
Predictive algorithms
Statistical testing
The “data-driven culture” in organizations promotes:
Decisions based on facts and analytics
Gut-feeling decisions
Hierarchical control
Paper-based workflows
Predictive maintenance in manufacturing uses:
IoT sensors and analytics to prevent breakdowns
Financial reporting
Sales forecasting
Text mining
In marketing analytics, ROI measures:
Return on Investment from campaigns
Random operations index
Report on income
Resource optimization input
Text mining is used to:
Extract useful patterns from textual data
Build numeric models
Analyze videos
Create charts
A data-driven company benefits by:
Making faster, more accurate decisions
Reducing transparency
Avoiding technology
Limiting employee input
The final step in the analytics process is:
Communicating insights and implementing decisions
Data collection
Model building
Cleaning raw data
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