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Practical 6: Charts & Data Visualization

Lesson 6 of 14 in the free Statistical Analysis using Excel Lab notes on Siksha Sarovar, written by Rohit Jangra.

Program Statement

Create various Excel charts to visualize and interpret different datasets.

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Dataset 1: Student Subject Marks

StudentMathsScienceEnglishHindi
Aman85907882
Priya70658075
Rohit92888590
Neha60556862
Vikas95928994

Task 1 – Clustered Column Chart (Subject-wise Comparison)

  1. Select the entire table
  2. Insert → Column Chart → Clustered Column
  3. Chart Title: "Subject-wise Student Performance"
  4. Interpretation: The subject with the highest column values across all students shows the highest average performance.

Task 2 – Bar Chart (Total Marks per Student)

  • Add a Total column: =SUM(B2:E2)
  • Select Student + Total columns → Insert → Bar Chart
  • Interpretation: The longest bar indicates the top-performing student.

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Dataset 2: Monthly Sales Data

MonthSales (₹)Cumulative Sales
January4500045000
February5200097000
March60000157000
April58000215000
May70000285000
June65000350000

Task 3 – Line Chart (Sales Trend)

  • Select Month + Sales columns → Insert → Line Chart
  • Interpretation: The month with the steepest upward slope shows maximum growth.

Task 4 – Area Chart (Cumulative Sales)

  • Select Month + Cumulative Sales → Insert → Area Chart
  • Why Area Chart? It visually fills the area under the line, making cumulative growth more obvious than a Line Chart.

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Dataset 3: Company Market Share

CompanyMarket Share (%)
TechCorp35
DataSoft25
GlobalInc20
InfoSys12
Others8

Task 5 – Pie Chart (Market Share)

  • Select Company + Market Share → Insert → Pie Chart → 2-D Pie
  • Add data labels (percentage)
  • Interpretation: The largest slice (TechCorp at 35%) dominates the market.

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Dataset 4: Height vs Weight (Scatter Plot)

Height (cm)Weight (kg)
15552
16058
16563
17068
17572
18078
18584

Task 6 – Scatter Plot (Correlation Analysis)

  • Select Height + Weight → Insert → Scatter → Scatter with only Markers
  • Interpretation: As height increases, weight also increases — this indicates a positive correlation between height and weight.