Understanding Data
Chapter 7: Computer Science - Ultimate Study Guide | NCERT Class 12 Notes, Questions, Examples & Quiz 2025
Full Chapter Summary & Detailed Notes - Understanding Data Class 12 NCERT
Overview & Key Concepts
- Chapter Goal: Understand data as facts for decisions; types (structured/unstructured), collection/storage/processing; stats (mean/median/mode/range/SD). Exam Focus: Tables 7.1-7.3, Fig 7.1-7.2, Ex 7.1-7.5; 2025 Updates: Big data emphasis, Python libs (Pandas/NumPy) intro. Fun Fact: Quote "Data is not information..." ties to processing need. Core Idea: Raw data → Processed info → Decisions. Real-World: Census/placement analysis. Expanded: All subtopics point-wise with evidence (e.g., Table 7.1 inventory), examples (e.g., height data), debates (e.g., structured vs unstructured handling).
- Wider Scope: From manual to digital data; sources: Examples (college choice, census), tables (7.1-7.2), figures (7.1 cycle, 7.2 problems).
- Expanded Content: Include modern aspects like big data sources (sensors/social); point-wise for recall; add 2025 relevance like AI data ethics.
Introduction to Data
- Data Definition: Collection of facts/symbols representing situations; plural (datum singular). Ex: Placement records for college choice.
- Importance: Decisions (govt policies, sports strategies, banking); hidden traits via processing. Ex: ATM debit, cyclone alerts.
- Business/Other Uses: Dynamic pricing (airlines/cabs), voting results, experiments, libraries, search engines, weather.
- Expanded: Evidence: Census value; debates: Data overload vs insights; real: Post-2020 data explosion (COVID tracking).
Conceptual Diagram: Data to Decision Flow
Flow: Collect Data → Store → Process (Stats) → Analyze → Decide. Ties to Fig 7.1 cycle.
Why This Guide Stands Out
Comprehensive: All subtopics point-wise, table integrations; 2025 with big data examples, processes analyzed for real analysis.
Types of Data
- Structured: Organized tabular (rows/columns, attributes/observations). Ex: Table 7.1 kitchen inventory; calc sums/products.
- Examples: Books (title/author), Fees (name/class), ATM (acc/amount). Table 7.2.
- Unstructured: No fixed format (news/emails/images). Ex: Variable page layouts; metadata (size/type).
- Focus: Book handles structured; unstructured via metadata.
- Expanded: Evidence: Activity 7.1 voter cards; real: Social media vs databases.
| Entity | Attributes |
|---|---|
| Books | BookTitle, Author, Price, Year |
| ATM Withdrawal | AccHolder, Amount, Date, ATM ID |
Data Collection
- Process: Gather/identify from sources (manual/digital). Ex: Grocery sales from diary → Spreadsheet/CSV.
- Scenarios: Enter manual, use existing file, develop software (Python/MySQL).
- Sources: Interactions (hospitals/malls), sensors, social media, global orgs (World Bank).
- Expanded: Evidence: Aadhaar attributes; debates: Privacy in collection.
Data Storage
- Need: Retain for future; challenges: Volume/speed, solved by cheap devices.
- Devices: HDD/SSD/CD/DVD/Pen/Memory Card.
- Files vs DBMS: Files for images/docs; DBMS overcomes limits (add/modify/delete).
- Expanded: Evidence: School/hospital data; real: Cloud storage 2025.
Data Processing
- Cycle: Input (collect/entry) → Process (store/retrieve/classify/update) → Output (reports). Fig 7.1.
- Examples: Admit card (verify/eligibility), ATM (PIN/balance), Ticket (login/berth). Fig 7.2.
- Automation: Online payments/bookings.
- Expanded: Evidence: Vast data needs processing; debates: Manual vs automated.
Statistical Techniques
- Central Tendency: Mean (avg, x̄=Σxi/n; sensitive to outliers), Median (middle sorted), Mode (most frequent).
- Examples: Height data [85,90,...115]: Mean=101.33, Median=102, Mode=110 (Ex 7.1-7.3).
- Variability: Range (Max-Min=30), SD (√[Σ(xi-x̄)²/n]=10.2; all values). Table 7.3.
- Expanded: Evidence: Outlier removal; real: Python NumPy for calcs.
Summary & Exercise
- Key Takeaways: Data → Info via process/stats; structured for analysis; store/process for decisions.
- Exercise Tease: Identify data/services; steps for scholarships; stats selection.
Key Definitions & Terms - Complete Glossary
All terms from chapter; detailed with examples, relevance. Expanded: 30+ terms grouped by subtopic; added advanced like "Metadata", "Outlier" for depth/easy flashcards.
Data
Collection of facts/symbols. Ex: Placement records. Relevance: Basis for decisions.
Structured Data
Tabular organized. Ex: Table 7.1 inventory. Relevance: Easy processing.
Unstructured Data
No fixed format. Ex: Emails/news. Relevance: Needs metadata.
Metadata
Data about data. Ex: Email subject/size. Relevance: Describes unstructured.
Data Collection
Gather/identify sources. Ex: Diary to CSV. Relevance: First step.
Data Storage
Retain on devices. Ex: HDD/SSD. Relevance: Future use.
DBMS
Manages data beyond files. Ex: MySQL. Relevance: Overcomes limits.
Data Processing
Transform to info. Ex: Fig 7.1 cycle. Relevance: Conclusions.
Mean
Average (Σxi/n). Ex: Height 101.33. Relevance: Central value.
Median
Middle sorted. Ex: 102 cm. Relevance: Less outlier-sensitive.
Mode
Most frequent. Ex: 110 cm. Relevance: Dominant value.
Range
Max-Min. Ex: 30 cm. Relevance: Spread extremes.
Standard Deviation
√[Σ(xi-x̄)²/n]. Ex: 10.2 cm. Relevance: Overall spread.
Outlier
Extreme value. Ex: Unusual height. Relevance: Affects mean/range.
Attribute
Column parameter. Ex: Price in Table 7.1. Relevance: Structured data.
Observation
Row data. Ex: One product row. Relevance: Instance.
Central Tendency
Single value summary. Ex: Mean/median/mode. Relevance: Data idea.
Dispersion
Spread measure. Ex: Range/SD. Relevance: Variability.
CSV
Comma-separated file. Ex: Sales data. Relevance: Digital storage.
HDD
Hard Disk Drive. Ex: Computer storage. Relevance: Digital devices.
SSD
Solid State Drive. Ex: Faster storage. Relevance: Modern devices.
Dynamic Pricing
Demand-supply based. Ex: Cabs/airlines. Relevance: Data analysis use.
Census
Population data collection. Ex: Govt policies. Relevance: Systematic data.
Big Data
Large volume/fast generation. Ex: Sensors/social. Relevance: ICT revolution.
Knowledge Base
AI facts/assumptions. Ex: Decision rules. Relevance: Data for AI.
Input
Data collection/entry. Ex: Fig 7.1. Relevance: Processing start.
Output
Reports/results. Ex: Admit card. Relevance: Processed info.
Variability
Dispersion synonym. Ex: Salary differences. Relevance: Group diversity.
Frequency
Occurrence count. Ex: Mode basis. Relevance: Stats calc.
Tip: Group by type/process; examples for recall. Depth: Debates (e.g., mean vs median outliers). Historical: Data growth. Interlinks: To Python Ch8. Advanced: Big data tools. Real-Life: Analytics. Graphs: Stats tables. Coherent: Evidence → Interpretation. For easy learning: Flashcard per term with ex.
60+ Questions & Answers - NCERT Based (Class 12) - From Exercises & Variations
Based on chapter + expansions. Part A: 10 (1 mark, one line), Part B: 10 (3 marks, four lines), Part C: 10 (4 marks, six lines), Part D: 10 (6 marks, eight lines). Answers point-wise in black text. Include tables/calcs where apt.
Part A: 1 Mark Questions (10 Qs - Short)
1. Define data.
Collection of facts/symbols.
2. What is structured data?
Tabular organized data.
3. Name one storage device.
HDD.
4. What is mean?
Average of values.
5. Define median.
Middle sorted value.
6. What is mode?
Most frequent value.
7. Define range.
Max - Min.
8. What is SD?
Spread measure.
9. What is metadata?
Data about data.
10. Example of unstructured data?
Email content.
Part B: 3 Marks Questions (10 Qs - Medium, Exactly 4 Lines Each)
1. Differentiate structured vs unstructured data.
- Structured: Tabular fixed. Ex: Inventory.
- Unstructured: Variable format. Ex: News.
- Structured easy process; unstructured needs metadata.
- Focus: Structured in book.
2. List 3 data sources with examples.
- Personal: Name/age.
- Transactions: Banking/ticketing.
- Media: Images/videos.
- Sensors: Signals.
3. Explain data collection steps.
- Identify sources (manual/digital).
- Enter to file (spreadsheet/CSV).
- Develop software if needed.
- Ex: Grocery diary to digital.
4. What is data storage? Devices.
- Retain for future use.
- Devices: HDD/SSD/CD/Pen.
- Files vs DBMS for management.
- Challenge: Volume/speed.
5. Describe data processing cycle.
- Input: Collect/entry.
- Process: Store/classify.
- Output: Reports.
- Ex: Fig 7.1.
6. Explain mean with formula.
- Average: x̄=Σxi/n.
- Sensitive to outliers.
- Ex: Heights sum/9=101.33.
- Remove extremes first.
7. How to find median? Ex.
- Sort data; middle value.
- Odd: Position (n+1)/2.
- Ex: Sorted heights, 5th=102.
- Even: Avg two middle.
8. Define mode with ex.
- Highest frequency value.
- No/multiple possible.
- Ex: 110 appears 3 times.
- Numeric/non-numeric.
9. What is range? Limitation.
- Max-Min.
- Ex: 115-85=30.
- Outlier-sensitive.
- Numerical only.
10. Formula for SD? Meaning.
- σ=√[Σ(xi-x̄)²/n].
- Spread from mean.
- Small: Less spread.
- Ex: Heights=10.2.
Part C: 4 Marks Questions (10 Qs - Medium-Long, Exactly 6 Lines Each)
1. Explain importance of data with 2 ex.
- Decisions: College placement.
- Hidden traits: Computer processing.
- Ex1: ATM debit/update.
- Ex2: Cyclone satellite alerts.
- Business: Dynamic pricing.
- Govt: Census policies.
2. Describe structured data with table ex.
- Rows/columns; attributes/obs.
- Ex: Table 7.1 kitchen.
- Calc: Sum inventory, total value.
- Table 7.2: Books/ATM.
- Easy summarization.
- Spreadsheet software.
3. What is unstructured data? Metadata.
- No fixed structure. Ex: Newspaper variable.
- Emails/images/videos.
- Metadata: Data about it (subject/size).
- Ex: Image resolution/JPEG.
- Processing harder.
- Book focus: Structured.
4. Steps for data collection in grocery.
- Scenario1: Diary → Digital entry.
- Scenario2: Existing CSV use.
- Scenario3: Develop Python/MySQL software.
- Sources: Continuous generation.
- Ex: Mall sales analysis.
- Global: IMF economic data.
5. Explain data storage challenges/solutions.
- High volume/rate.
- Solutions: Cheap devices (HDD/SSD).
- Files for docs/images.
- DBMS for school/hospital.
- Limitations: File processing.
- Ex: Add/modify data.
6. Describe data processing with Fig 7.2 ex.
- Cycle: Input-process-output.
- Ex1: Admit card verification.
- Ex2: ATM PIN/balance check.
- Ex3: Ticket berth allocation.
- Automation: Online bills.
- Vast data needs it.
7. Central tendency measures with ex.
- Mean: Avg, outlier-sensitive. Ex:101.33.
- Median: Middle, robust. Ex:102.
- Mode: Frequent. Ex:110.
- Selection: Data chars.
- Instead of individuals.
- Easy comprehension.
8. Explain range and SD with formulas.
- Range: M-S. Ex:30; extremes only.
- SD: √[Σ(xi-x̄)²/n]. Ex:10.2.
- All values; spread degree.
- Small SD: Less variation.
- Table 7.3 calc.
- Numerical data.
9. Why process data? Steps.
- Vast data no conclusion.
- Steps: Gather-Process-Analyze.
- Ex: Placement brochure summary.
- Stats for understanding.
- Automated: Tickets/payments.
- Info from raw.
10. Outliers effect on stats.
- Extreme values/errors.
- Affect mean/range badly.
- Median/mode less.
- Remove before mean.
- Ex: Height outlier.
- Influence calcs.
Part D: 6 Marks Questions (10 Qs - Long, Exactly 8 Lines Each)
1. Justify data crucial for decisions; 3 scenarios.
- Raw facts processed to info.
- Ex1: College: Placement/faculty data.
- Ex2: Census: Policies/planning.
- Ex3: Sports: Opponent analysis.
- Business: Market feedback.
- Hidden traits via computer.
- Ex: Weather satellites.
- Voting: Quick results.
2. When/why use mean/median/mode? Calc for heights.
- Mean: Avg, no outliers. Ex:101.33=912/9.
- Median: Central, outlier-robust. Ex:102 (5th).
- Mode: Frequent. Ex:110 (3 times).
- Selection: Data type.
- Formulae: As above.
- Evidence: Ex 7.1-7.3.
- Summary single value.
- Frequency basis mode.
4. Steps to collect/process school scholarship data.
- Collect: Marks (2 yrs>75%), income<5L.
- Store: Student table (name/class/marks/income).
- Process: Filter criteria.
- Ex: Query marks>75 and income<500000.
- Output: Beneficiary list.
- Steps: Entry-Verify-Sort-Report.
- Structured data ideal.
- DBMS for efficiency.
5. Define variability; calc range/SD for temps [34,34,27,28,27,34,34].
- Dispersion/spread around mean.
- Range: Max34-Min27=7.
- Mean: (34*4+27*2+28)/7=31.14.
- SD: Calc diffs sq avg sqrt≈3.2.
- Indicates diversity.
- Same mean diff dispersion.
- Ex: Salaries comparison.
- Table for steps.
| Temp | xi-x̄ | (xi-x̄)² |
|---|---|---|
| 34 | 2.86 | 8.18 |
6. Explain processing cycle with admit card ex.
- Input: Student details/payment.
- Process: Verify eligibility/fee/photo.
- Output: Roll no/admit card.
- Fig 7.2: Correctness check.
- Add to eligible list.
- Automation shortens time.
- Similar: ATM/ticket.
- Raw to info.
7. Select stats for problems: Salary disparity, avg performance, etc.
- Disparity: SD/Range.
- Avg performance: Mean.
- Height compare: Mean/Median.
- Dominant: Mode.
- Income compare: Mean.
- Popular color: Mode.
- Justify: Spread vs central.
- Tools: Python libs.
8. Create dataset for canteen; compare prices/sales.
- Table: Item/Purchase/Sale.
- Juice:10/15; Biscuit:5/8; Samosa:20/30.
- Compare price: Juice diff5, etc.
- Sales: Assume vol, total revenue.
- Variation: SD prices.
- Mean purchase/sale.
- Ex: Range juice prices.
- Stats for analysis.
| Item | Purchase | Sale |
|---|---|---|
| Juice | 10 | 15 |
9. Differentiate central tendency vs variability.
- Central: Single value (mean/med/mode).
- Variability: Spread (range/SD).
- Same central diff spread possible.
- Ex: Heights mean101, range30.
- Tendency: Idea about data.
- Dispersion: Diversity degree.
- Selection: Purpose.
- Table 7.3 evidence.
10. Role of stats in data; Python tools.
- Preliminary understanding.
- Summarization tabular.
- Ex: Mean for avg, SD spread.
- Tools: Python Pandas/NumPy.
- Libraries for large volumes.
- Decide technique first.
- 2025: Data science focus.
- Ch8 link.
Tip: Include tables in ans; practice calcs. Additional 30 Qs: Variations on ex, stat selection.
Key Concepts - In-Depth Exploration
Core ideas with examples, pitfalls, interlinks. Expanded: All concepts with steps/examples/pitfalls for easy learning. Depth: Debates, analysis.
Data Importance
Steps: 1. Collect facts, 2. Process hidden, 3. Decide. Ex: Census. Pitfall: Raw overload. Interlink: Processing. Depth: ICT volume.
Structured Data
Steps: 1. Attributes rows, 2. Calc sums. Ex: Table 7.1. Pitfall: Rigid format. Interlink: Stats. Depth: Tabular ease.
Unstructured Data
Steps: 1. Variable, 2. Use metadata. Ex: Emails. Pitfall: Hard process. Interlink: Metadata. Depth: Multimedia.
Data Collection
Steps: 1. Sources identify, 2. Digitalize. Ex: Diary-CSV. Pitfall: Incomplete. Interlink: Storage. Depth: Continuous gen.
Data Storage
Steps: 1. Devices choose, 2. DBMS if complex. Ex: SSD files. Pitfall: Volume loss. Interlink: Processing. Depth: Cost decrease.
Data Processing
Steps: 1. Input, 2. Process, 3. Output. Ex: Fig 7.2. Pitfall: No analysis. Interlink: Stats. Depth: Automation.
Mean
Steps: 1. Sum/n. Ex:101.33. Pitfall: Outliers skew. Interlink: SD. Depth: Avg central.
Median
Steps: 1. Sort, 2. Middle. Ex:102. Pitfall: Even avg. Interlink: Outliers. Depth: Robust.
Mode
Steps: 1. Frequency count. Ex:110. Pitfall: None/multiple. Interlink: Non-numeric. Depth: Dominant.
Range
Steps: 1. Max-Min. Ex:30. Pitfall: Extremes only. Interlink: Variability. Depth: Simple spread.
Standard Deviation
Steps: 1. Diffs sq avg sqrt. Ex:10.2. Pitfall: Calc intensive. Interlink: All values. Depth: True dispersion.
Outlier
Steps: 1. Identify extreme, 2. Remove for mean. Ex: Unusual. Pitfall: Ignore error. Interlink: Stats effect. Depth: Influence.
Metadata
Steps: 1. Describe (size/type). Ex: Image res. Pitfall: Missing unstructured. Interlink: Unstructured. Depth: About data.
DBMS
Steps: 1. Store/retrieve structured. Ex: MySQL. Pitfall: File limits. Interlink: Ch later. Depth: Management.
Big Data
Steps: 1. Generate fast, 2. Analyze. Ex: Sensors. Pitfall: Volume challenge. Interlink: 2025. Depth: ICT.
Advanced: Pandas for stats, ethics. Pitfalls: Outlier miss. Interlinks: To file handling. Real: Analytics. Depth: 14 concepts details. Examples: Real calcs. Graphs: Tables. Errors: Wrong stat. Tips: Steps evidence; compare tables (central vs variability).
Examples & Calculations - From Text with Simple Explanations
Expanded with evidence, analysis; focus on applications. Added variations for practice.
Example 1: Structured Data (Table 7.1)
Simple Explanation: Inventory calc.
| Model | Name | Price | Disc% | Items |
|---|---|---|---|---|
| ABC1 | Bottle | 126 | 8 | 13 |
- Step 1: Sum items=128.
- Step 2: Total value= Price*Items sum.
- Step 3: Ex: Bottle 126*13=1638.
- Simple Way: Spreadsheet sum.
Example 2: Mean Calculation (Ex 7.1)
Simple Explanation: Avg height.
| Height |
|---|
| 90,102,110,115,85,90,100,110,110 |
- Step 1: Sum=912.
- Step 2: /9=101.33.
- Step 3: Outlier? Remove first.
- Simple Way: Sum formula.
Example 3: Median (Ex 7.2)
Simple Explanation: Middle value.
| Sorted |
|---|
| 85,90,90,100,102,110,110,110,115 |
- Step 1: Sort ascending.
- Step 2: 5th=102.
- Step 3: Equal halves.
- Simple Way: List sort.
Example 4: Mode (Ex 7.3)
Simple Explanation: Frequent.
- Step 1: Count: 110=3.
- Step 2: Highest freq.
- Step 3: Multiple possible.
- Simple Way: Frequency table.
Example 5: Range (Ex 7.4)
Simple Explanation: Spread extremes.
- Step 1: Max115 Min85.
- Step 2: Diff=30.
- Step 3: Outlier affects.
- Simple Way: Min/max find.
Example 6: SD (Ex 7.5, Table 7.3)
Simple Explanation: Full spread (2025 NumPy ready).
| Height | xi-x̄ | (xi-x̄)² |
|---|---|---|
| 90 | -11.33 | 128.37 |
| Sum sq=938, √(938/9)=10.2 | ||
- Step 1: Mean=101.33.
- Step 2: Diffs sq sum/9.
- Step 3: Sqrt=10.2.
- Simple Way: Calc table.
Tip: Practice with Excel; troubleshoot (e.g., sort for median). Added for stats, tables.
Interactive Quiz - Master Understanding Data
10 MCQs in full sentences; 80%+ goal. Covers types, processing, stats.
Quick Revision Notes & Mnemonics
Concise, easy-to-learn summaries for all subtopics. Structured in tables for quick scan: Key points, examples, mnemonics. Covers types, process, stats. Bold key terms; short phrases for fast reading.
| Subtopic | Key Points | Examples | Mnemonics/Tips |
|---|---|---|---|
| Data Intro |
|
College placement; census. | DIP (Data-Info-Process). Tip: "Data Drives Decisions" – Quote recall. |
| Types |
|
Inventory; emails. | SU (Structured-Unstructured). Tip: "Structure Sums, Unstructured Stories" – Tables vs text. |
| Collection/Storage |
|
Diary-CSV; HDD files. | CS (Collect-Store). Tip: "Collect to Store, Secure Forever" – Steps chain. |
| Processing |
|
Ticket booking. | IPO (Input-Process-Output). Tip: "Input Processed Outputs Insights" – Cycle loop. |
| Central Tendency |
|
Height:101/102/110. | MMM (Mean-Median-Mode). Tip: "Mean Middle Most" – Central trio. |
| Variability |
|
30/10.2 height. | RS (Range-SD). Tip: "Range Rough, SD Sophisticated" – Simple vs full. |
Overall Tip: Use DIP-SU-IPO-MMM-RS for full scan (5 mins). Flashcards: Front (term), Back (points + mnemonic). Print table for wall revision. Covers 100% chapter – easy for exams!
Key Terms & Processes - All Key
Expanded table 30+ rows; quick ref. Added advanced (e.g., Dispersion, Frequency).
| Term/Process | Description | Example | Usage |
|---|---|---|---|
| Data | Facts collection | Placement records | Decisions |
| Structured | Tabular rows/cols | Table 7.1 | Processing |
| Unstructured | No fixed format | Emails | Metadata |
| Metadata | Data about data | Image size | Describe |
| Collection | Gather sources | Diary to CSV | First step |
| Storage | Retain devices | HDD/SSD | Future |
| DBMS | Data management | MySQL | Beyond files |
| Processing | Transform info | Fig 7.1 cycle | Conclusions |
| Mean | Σxi/n | 101.33 height | Avg |
| Median | Middle sorted | 102 cm | Central |
| Mode | Most frequent | 110 cm | Dominant |
| Range | Max-Min | 30 cm | Extremes |
| SD | √[sq diff/n] | 10.2 cm | Spread |
| Outlier | Extreme value | Unusual height | Affect mean |
| Attribute | Column param | Price | Structured |
| Observation | Row instance | One product | Data point |
| Central Tendency | Single summary | Mean/med/mode | Idea |
| Dispersion | Variability | Range/SD | Diversity |
| CSV | Comma values | Sales file | Digital |
| HDD | Hard disk | Computer store | Device |
| SSD | Solid state | Fast drive | Modern |
| Dynamic Pricing | Demand based | Cabs | Analysis |
| Census | Population record | Govt data | Policies |
| Big Data | Large/fast gen | Sensors | ICT |
| Knowledge Base | AI facts/rules | Decisions | System |
| Input | Collect/entry | Details | Cycle start |
| Output | Reports | Admit card | Info |
| Variability | Spread synonym | Salary diff | Group |
| Frequency | Occurrence count | Mode basis | Stats |
| Attribute | Variable char | Age/gender | Structured |
| Metadata | Email parts | Subject/body | Unstructured |
| Outlier Removal | Before mean | Extreme filter | Accuracy |
| Processing Cycle | Input-process-out | Fig 7.1 | Transform |
Tip: Examples memory; sort subtopic. Easy: Table scan. Added 10 rows depth.
Data Processing Processes Step-by-Step
Step-by-step breakdowns of core processes, structured as full questions followed by detailed answers with steps. Visual descriptions for easy understanding; focus on actionable Q&A with examples from chapter.
Question 1: How to process inventory data from Table 7.1?
- Step 1: Enter tabular (spreadsheet).
- Step 2: Sum Items_in_Inventory=128.
- Step 3: Calc value=Price*(1-Disc/100)*Items per row.
- Step 4: Total sum all values.
- Step 5: Output report.
- Step 6: Update for sales.
Visual: Table → Formulas → Summary. Example: Bottle value=126*0.92*13≈1508.
Question 2: Steps for mean/median/mode on heights [90,102,110,115,85,90,100,110,110]?
- Step 1: List data.
- Step 2: Mean: Sum912/9=101.33.
- Step 3: Median: Sort, 5th=102.
- Step 4: Mode: Count, 110=3.
- Step 5: Check outliers.
- Step 6: Interpret central.
Visual: List → Sort/Count → Values. Example: Ex 7.1-7.3 outputs.
Question 3: What steps in data collection for Aadhaar?
- Step 1: Identify attributes (name/ID/photo).
- Step 2: Gather from sources (forms/biometrics).
- Step 3: Digital entry structured.
- Step 4: Verify uniqueness.
- Step 5: Store in DBMS.
- Step 6: Issue card.
Visual: Form → Entry → Verify → Store. Example: Activity voter cards.
Question 4: Full process of SD calculation (Table 7.3)?
- Step 1: Mean x̄=101.33.
- Step 2: For each xi, diff=xi-x̄.
- Step 3: Sq each diff, sum=938.
- Step 4: Avg sq=938/9=104.22.
- Step 5: Sqrt=10.2.
- Step 6: Interpret spread.
Visual: Mean → Diff Table → Sum Sqrt. Example: Heights SD=10.2.
Question 5: How does data processing cycle work for ATM withdrawal (Fig 7.2)?
- Step 1: Input PIN/acc/amount.
- Step 2: Process: Valid PIN? Balance? Deduct.
- Step 3: Count notes/print receipt.
- Step 4: Output: Cash/slip.
- Step 5: Update account.
- Step 6: Error? Deny.
Visual: Input → Checks → Dispense. Example: Valid → Cash; Invalid → Msg.
Question 6: Steps to select/handle stats for class performance comparison.
- Step 1: Identify goal (avg/divisions).
- Step 2: Choose: Mean for avg, Mode divisions.
- Step 3: Collect marks/div.
- Step 4: Calc (sum/n, freq).
- Step 5: Compare classes/months.
- Step 6: Report insights.
Visual: Goal → Tool → Calc → Compare. Example: Unit tests mean over months.
Tip: Treat as FAQ; apply to ex. Easy: Q → Steps + Visual. Full Q&A for exam-like practice.
