Topics
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
Commercial Mathematics
Goods and Services Tax (G.S.T.)
Banking
Algebra
Geometry
Shares and Dividends
Symmetry
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
Statistics
Problems on Quadratic Equations
- Method for Solving a Quadratic Word Problem
- Problems Based on Numbers
- Problems on Ages
- Problems Based on Time and Work
- Problems Based on Distance, Speed and Time
- Problems Based on Geometrical Figures
- Problems on Mensuration
- Problems on C.P. and S.P.
- Miscellaneous Problems
Ratio and Proportion
Probability
Remainder Theorem and Factor Theorem
- Function and Polynomial
- Division Algorithm for Polynomials
- Remainder Theorem
- Factor Theorem
- Applications of Factor Theorem
Matrices
Arithmetic Progression
Geometric Progression
Reflection
- Co-ordinate Geometry
- Advanced Concept of Reflection in Mathematics
- Invariant Points
- Combination of Reflections
- Using Graph Paper for Reflection
Section and Mid-Point Formulae
Equation of a Line
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
Angle and Cyclic Properties of a Circle
Tangent Properties of Circles
Constructions
Volume and Surface Area 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
Graphical Representation of Statistical Data
Measures of Central Tendency (Mean, Median, Quartiles and Mode)
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:
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Understand large amounts of information quickly
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Identify patterns and trends
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Compare values easily
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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
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Example: 10, 20, 30, 40, 50, 60, 70, 80
2. Descending Order – From largest to smallest value
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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) |
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Column 1 (Marks): All unique values from lowest to highest
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Column 2 (Tally Marks): Visual representation using short lines (||||) to count
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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
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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
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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
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Factories use frequency distribution to check products—how many items are defective, how many are perfect, how many need minor adjustments
4. Medical Records
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Hospitals organise patient data like blood pressure readings, ages, or weights using frequency distribution to identify common health patterns




Key Points Summary
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Array is raw data arranged in ascending or descending order of magnitude.
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Frequency distribution shows how many times each value appears in a dataset.
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A frequency distribution table has three columns: marks, tally marks, and frequency.
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Tally marks are visual counting tools; every fifth mark is drawn as a diagonal cross (`cancel (||||)`).
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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]
The number of students (boys and girls) of class IX participating in different activities during their annual day function is given below:
| Activities | Dance | Speech | Singing | Quiz | Drama | Anchoring |
| Boys | 12 | 5 | 4 | 4 | 10 | 2 |
| Girls | 10 | 8 | 6 | 3 | 9 | 1 |
Draw a double bar graph for the above data.
The following data on the number of girls (to the nearest ten) per thousand boys in different sections of Indian society is given below.
| Section | Number of girls per thousand boys |
| Scheduled Caste (SC) | 940 |
| Scheduled Tribe (ST) | 970 |
| Non SC/ST | 920 |
| Backward districts | 950 |
| Non-backward districts | 920 |
| Rural | 930 |
| Urban | 910 |
- Represent the information above by a bar graph.
- In the classroom discuss what conclusions can be arrived at from the graph.
The length of 40 leaves of a plant are measured correct to one millimetre, and the obtained data is represented in the following table:-
| Length (in mm) | Number of leaves |
| 118 - 126 | 3 |
| 127 - 135 | 5 |
| 136 - 144 | 9 |
| 145 - 153 | 12 |
| 154 - 162 | 5 |
| 163 - 171 | 4 |
| 172 - 180 | 2 |
- Draw a histogram to represent the given data. [Hint: First make the class intervals continuous]
- Is there any other suitable graphical representation for the same data?
- Is it correct to conclude that the maximum number of leaves are 153 mm long? Why?

