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RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 Data Handling

Here we have provided RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 for the ease of the students. From this article you can also download this chapter's PDF.
authorImageAnanya Gupta14 Aug, 2024
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RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2: RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 on Data Handling provide detailed solutions for students tackling various data representation tasks. This exercise focuses on practical applications of data handling techniques, including constructing and interpreting bar graphs, pie charts and pictograms.

Each solution is detailed and step-by-step helping students understand how to effectively visualize and analyze data. By practicing these exercises students can develop strong skills in presenting and interpreting data.

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 Data Handling Overview

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 focuses on the practical aspects of data handling specifically emphasizing the creation and interpretation of bar graphs, pie charts and pictograms. This exercise guides students through various problems where they are required to organize data into these visual formats, interpret the given graphs, and draw meaningful conclusions. Each solution is explained to ensure a clear understanding of how to convert raw data into visual representations and extract insights from them. By working through these problems students gain valuable skills in data visualization, which enhances their ability to analyze and present data effectively in different contexts.

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 PDF

For students seeking detailed explanations and step-by-step solutions to problems in RS Aggarwal Class 8 Maths Chapter 21 Exercise 21.2 on Data Handling the PDF link provided below. By referring to this PDF students can better understand how to solve the exercises and apply their knowledge of data handling effectively. The solutions are designed to help clarify concepts and improve problem-solving skills in data handling.

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 Data Handling PDF

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 (Ex 21B)

Below we have provided RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 Data Handling -

(Question1) The marks obtained by 40 students of a class in an examination are given below:

8, 47, 22, 31, 17, 13, 38, 26, 3, 34, 29, 11, 22, 7, 15, 24, 38, 31, 21, 35, 42, 24, 45, 23, 21, 27, 29, 49, 25, 48, 21, 15, 18, 27, 19, 45, 14, 34, 37, 34.

Prepare a frequency distribution table with equal class intervals, starting from 0 – 10 (where 10 is not included).

Solution:

Arrange the data in ascending order, we get the observations as 0 – 10 = 8, 3, 7 10 – 20 = 17, 13, 11, 15, 15, 18, 19, 1 20 – 30 = 22, 26, 29, 22, 24, 21, 24, 23, 21, 27, 29, 25, 21, 27 30 – 40 = 31, 38, 34, 38, 31, 35, 34, 37, 34 40 – 50 = 47, 42, 45, 49, 48, 45. We may represent the data as given below:

(Question 2) The electricity bills (in rupees) of 25 houses of a certain locality for a month are given below:

324, 700, 617, 400, 356, 365, 435, 506, 548, 736, 780, 378, 570, 685, 312, 630, 584, 674, 754, 776, 596, 745, 565, 763, 472.

Arrange the above data in increasing order and form a frequency table using equal class intervals, starting from 300 – 400, where 400 is not included.

Solution:

Arrange the data in ascending order, we get the observations as 300 – 400 = 324, 356, 365, 378, 312 400 – 500 = 400, 435, 472 500 – 600 = 506, 548, 570, 584, 596, 565 600 – 700 = 617, 685, 630, 674 700 – 800 = 700, 736, 780, 754, 776, 745, 763. We may represent the data as given below:

(Question 3) The weekly wages (in rupees) of 28 workers of a factory are given below:

668, 610, 642, 658, 668, 620, 719, 720, 700, 690, 710, 642, 672, 654, 692, 706, 718, 702, 704, 678, 615, 640, 680, 716, 705, 615, 636, 656.

Construct a frequency table with equal class intervals; taking the first of the class intervals as 610 – 630, where 630 is not included.

Solution:

Arrange the data in ascending order, we get the observations as 610 – 630 = 610, 620, 615, 615 630 – 650 = 642, 642, 640, 63 650 – 670 = 668, 658, 668, 654, 656 670 – 690 = 672, 678, 680 690 – 710 = 700, 690, 692, 706, 702, 704, 705 710 – 730 = 719, 720, 710, 718, 716. We may represent the data as given below:

(Question 4) The weekly pocket expenses (in rupees) of 30 students of a class are given below:

62, 80, 110, 75, 84, 73, 60, 62, 100, 87, 78, 94, 17, 86, 65, 68, 90, 80, 118, 72, 95, 72, 103, 96, 64, 94, 87, 85, 105, 115.

Construct a frequency table with class intervals 60 – 70 (where 70 are not included), 70 – 80, 80 – 90, etc.

Solution:

Arrange the data in ascending order, we get the observations as 60 – 70 = 62, 60, 62, 65, 68, 64 70 – 80 = 75, 73, 78, 72, 72 80 – 90 = 80, 84, 87, 86, 80, 87, 85 90 – 100 = 94, 90, 95, 96, 94 100 – 110 = 100, 103, 108 110 – 120 = 110, 117, 118, 115

(Question 5) The daily earnings (in rupees) of 24 stores in a market was recorded as under:

715, 650, 685, 550, 573, 530, 610, 525, 742, 680, 736, 524, 500, 585, 723, 545, 532, 560, 580, 545, 625, 630, 645, 700.

Prepare a frequency table taking equal class sizes. One such class is 500 – 550, where 550 is not included.

Solution:

Arrange the data in ascending order, we get the observations as 500 – 550 = 530, 525, 524, 500, 532, 545 550 – 600 = 550, 573, 585, 560, 580 600 – 650 = 610, 625, 630, 645 650 – 700 = 650, 685, 680, 700 – 750 = 715, 742, 736, 723, 700

(Question 6) The heights (in cm) of 22 students were recorded as under:

125, 132, 138, 144, 142, 136, 134, 125, 135, 130, 126, 132, 135, 142, 143, 143, 128, 126, 136, 135, 130, 130, 133.

Prepare a frequency distribution table, taking equal class intervals and starting from 125 – 130, where 130 is not included.

Solution:

Arrange the data in ascending order, we get the observations as 125 – 130 = 125, 125, 126, 128, 126 130 – 135 = 132, 134, 130, 132, 130, 130, 133 135 – 140 = 138, 136, 135, 135, 136, 135 140 – 145 = 144, 142, 142, 143, 143

Benefits of RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2

  • Clear Explanations : The solutions provide step-by-step explanations making it easier for students to understand and solve data handling problems effectively.
  • Enhanced Problem-Solving Skills : By working through the detailed solutions students can improve their problem-solving abilities and gain a deeper understanding of data interpretation and analysis.
  • Self-Assessment : Students can use the solutions to check their answers and identify areas where they may need further study or clarification, allowing for targeted improvement.
  • Practice and Preparation : Regular practice with these solutions prepares students for exams and assessments, boosting their confidence and performance in data handling.

RS Aggarwal Solutions for Class 8 Maths Chapter 21 Exercise 21.2 FAQs

What is data handling?

Data handling involves collecting, organizing, summarizing, and interpreting data. It includes various methods and techniques to manage and analyze data effectively, such as creating graphs, charts, and tables.

Why is data handling important?

Data handling is important because it helps in making sense of large amounts of information. It enables us to identify patterns, trends, and insights from data, which is essential for decision-making and problem-solving in various fields.

What types of data representations are commonly used?

Common types of data representations include bar graphs, line graphs, pie charts, and histograms. These visual tools help in understanding data more clearly and effectively.

What is the difference between qualitative and quantitative data?

Qualitative data describes characteristics or qualities that are not numerical (e.g., colors, names), while quantitative data is numerical and can be measured (e.g., height, weight). Both types of data are important for comprehensive data analysis.
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