Topics
Goods and Services Tax (G.S.T.)
Commercial Mathematics
Compound Interest
- Compound Interest as a Repeated Simple Interest Computation with a Growing Principal
- Use of Compound Interest in Computing Amount Over a Period of 2 Or 3-years
- Use of Formula
- Finding CI from the Relation CI = A – P
Banking
Algebra
Shares and Dividends
Geometry
Mensuration
Linear Inequations
Quadratic Equations
- Quadratic Equations
- Method of Solving a Quadratic Equation
- Factorisation Method
- Quadratic Formula (Shreedharacharya's Rule)
- Nature of Roots of a Quadratic Equation
- Equations Reducible to Quadratic Equations
Trigonometry
Ratio and Proportion
Statistics
Probability
Factorisation of Polynomials
- Function and Polynomial
- Division Algorithm for Polynomials
- Remainder Theorem
- Factor Theorem
- Applications of Factor Theorem
Matrices
Arithmetic and Geometric Progression
Co-ordinate Geometry
- Co-ordinate Geometry
- Advanced Concept of Reflection in Mathematics
- Invariant Points
- Combination of Reflections
- Using Graph Paper for Reflection
Symmetry
Similarity
Loci
- Locus
- Points Equidistant from Two Given Points
- Points Equidistant from Two Intersecting Lines
- Summary of Important Results on Locus
- Important Points on Concurrency in a Triangle
Circles
Tangent and Secant Properties
Constructions
Area and Volume of Solids (Cylinder, Cone and Sphere)
- Mensuration of Cylinder
- Hollow Cylinder
- Mensuration of Cones
- Mensuration of a Sphere
- Hemisphere
- Conversion of Solids
- Solid Figures
- Problems on Mensuration
Trigonometrical Identities
Heights and Distances
- Angles of Elevation and Depression
- Problems based on Elevation and Depression
Statistics : basic concepts, Mean, Median, Mode. Histograms and Ogive
Probability
- Introduction
- Frequency Distribution & Data Arrangement
- Structure of a Frequency Distribution Table
- Example
- Real-life Applications
- Key Points Summary
Introduction
Data representation is the process of organizing and displaying raw data in a meaningful way so we can easily see patterns and draw conclusions.
The importance of representing data properly lies in helping us:
-
Understand large amounts of information quickly
-
Identify patterns and trends
-
Compare values easily
-
Make informed decisions based on facts
Frequency Distribution & Data Arrangement
Frequency Distribution:
It shows how many times each value appears in a dataset, making it much easier to work with large sets of numbers.
Arranging Data in Order:
Before we can create a frequency distribution, we need to arrange raw data in either:
1. Ascending Order – From smallest to largest value
-
Example: 10, 20, 30, 40, 50, 60, 70, 80
2. Descending Order – From largest to smallest value
-
Example: 80, 70, 60, 50, 40, 30, 20, 10
When data is arranged in ascending or descending order, it's called an array.
Structure of a Frequency Distribution Table
| Column 1 | Column 2 | Column 3 |
|---|---|---|
| Marks | Tally Marks | Number of Students (Frequency) |
-
Column 1 (Marks): All unique values from lowest to highest
-
Column 2 (Tally Marks): Visual representation using short lines (||||) to count
-
Column 3 (Frequency): The number count for each mark
Example
Problem: Construct a frequency distribution table for the following data:
Raw data: 55, 56, 56, 54, 57, 57, 56, 55, 55, 56, 56, 57, 55, 56, 56, 54, 56, 55, 54, 57, 57, 56, 55, 54, and 55.
Solution:
Step 1: Arrange data in ascending order
-
54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57
Step 2: Create the frequency distribution table
Step 3: Mark Tally for Each Value
Step 4: Count the Tally Marks
| Marks | Tally Marks | Frequency |
|---|---|---|
| 54 | |||| | 4 |
| 55 | `cancel (||||)` || | 7 |
| 56 | `cancel (||||)` ||| | 9 |
| 57 | `cancel (||||)` | 5 |
| Total | 25 |
Real-life Applications
Here are some practical uses of frequency distribution in everyday life.
1. School Test Results
-
Teachers use frequency distribution to analyse test scores and see how many students fall into different performance ranges (excellent, good, average, etc.)
2. Election Polling
-
Election officials count votes using tally marks and create frequency distributions to see how many people voted for each candidate
3. Quality Control in Manufacturing
-
Factories use frequency distribution to check products—how many items are defective, how many are perfect, how many need minor adjustments
4. Medical Records
-
Hospitals organise patient data like blood pressure readings, ages, or weights using frequency distribution to identify common health patterns




Key Points Summary
-
Array is raw data arranged in ascending or descending order of magnitude.
-
Frequency distribution shows how many times each value appears in a dataset.
-
A frequency distribution table has three columns: marks, tally marks, and frequency.
-
Tally marks are visual counting tools; every fifth mark is drawn as a diagonal cross (`cancel (||||)`).
-
Arranging data in order makes it easier to count frequencies accurately.
Test Yourself
Video Tutorials
Shaalaa.com | Graphical Representation of Data
Related QuestionsVIEW ALL [95]
Following table gives the distribution of students of sections A and B of a class according to the marks obtained by them.
| Section A | Section B | ||
| Marks | Frequency | Marks | Frequency |
| 0 – 15 | 5 | 0 – 15 | 3 |
| 15 – 30 | 12 | 15 – 30 | 16 |
| 30 – 45 | 28 | 30 – 45 | 25 |
| 45 – 60 | 30 | 45 – 60 | 27 |
| 60 –75 | 35 | 60 – 75 | 40 |
| 75 – 90 | 13 | 75 – 90 | 10 |
Represent the marks of the students of both the sections on the same graph by two frequency polygons. What do you observe?
Manoj appeared for ICSE examination 2018 and secured percentage of marks as shown in the following table:
| Subject | Hindi | English | Maths | Science | Social Study |
| Marks as percent | 60 | 45 | 42 | 48 | 75 |
Represent the above data by drawing a suitable bar graph.
