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RD Sharma Class 8 Solutions Chapter 23 - Classification and Tabulation of Data (Ex 23.1) Exercise 23.1

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RD Sharma Class 8 Solutions Chapter 23 - Classification and Tabulation of Data (Ex 23.1) Exercise 23.1 - Free PDF

Free PDF download of RD Sharma Class 8 Solutions Chapter 23 - Classification and Tabulation of Data Exercise 23.1 solved by Expert Mathematics Teachers on Vedantu.com. All Chapter 23 - Classification and Tabulation of Data Ex 23.1 Questions with Solutions for RD Sharma Class 8 Maths to help you to revise the complete syllabus and score more marks. Register for online coaching for IIT JEE (Mains & Advanced) and other Engineering Entrance Exams.

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Chapter 23 - Tabulation of Data

The term ‘Data’ refers to information. In Statistical investigations, the first step is to collect observations. An observer's numerical observations are not immediately and directly usable. That's why it's referred to as Raw Data. Consider the following list of marks (out of 100) earned by ten eighth-grade students in a test:


56, 86, 45, 75, 83, 61, 91, 84, 72, 53,


Each entry in the preceding list is a numerical fact referred to as an observation. Raw Data is a term used to describe a collection of observations gathered at the start.


Data Presentation

After gathering Data, the investigator must figure out how to condense it into tabular form to study its key features. This is termed as presentation of Data. The raw Data can be arranged in a variety of ways, including:

  • Alphabetical order

  • Descending order

  • Ascending order

An array is a collection of raw Data organised in ascending or descending order of magnitude. Let the scores obtained by 10 students in Class VIII in a Class test, out of a possible 50, be as follows:


45, 35, 34, 24, 49, 40, 38, 27, 43, 26, 


This type of Data is referred to as Raw Data or Ungrouped Data.


Frequency Distribution

A Frequency table, also known as Frequency Distribution, is a way of presenting raw Data in a way that allows the information contained in the raw Data to be easily understood. Frequency Distributions are of two types:

  • Discrete Frequency Distribution. 

  • Continuous or grouped Frequency Distribution.

 

Discrete Frequency Distribution

The values of the variable are arranged individually in a Discrete Frequency Distribution. The number of times each value appears is represented by its Frequency.


The workers' weekly wages in rupees are listed below. Make a Discrete Frequency Distribution of the Data.


150, 200, 300, 150, 250, 250, 300, 150, 300, 200, 250, 300, 250, 150, 200, 250, 300, 150, 150, 200, 250, 300, 150, 300, 150, 200, 300, 150, 250, 200.


Weekly wages in Rs.

Number of workers

150

9

200

6

250

7

300

8


Continuous Frequency Distribution

Grouped Frequency Distribution is another name for Continuous Frequency Distribution.


Class intervals and respective Class frequencies are given in this format.

  • Initially, determine the Data set's range.

  • Next, divide the range by the number of the group in which you want your Data to be placed and rounded up.

  • After that, create groups by using Class width.

  • Lastly, calculate the Frequency of each group.

Consider the following scenario for daily maximum temperatures in ℃ in a city over 30 days.


20, 24, 18, 28, 30, 15, 23, 17, 27, 24, 18, 26, 27, 30, 23, 15, 17, 16, 15, 34, 33, 35, 24, 28, 30, 34, 31, 15, 16, 35.


  • Minimum value= 15

  • Maximum value= 35

  • Range= 35-15= 20

  • Number of Classes= say, 4

  • Width of each Class= 5

Table depicting the temperature Frequency Distribution in a city over 30 days.

Class Interval

Frequency

15-20

11

20-25

5

25-30

7

30-35

7

Total

30

FAQs on RD Sharma Class 8 Solutions Chapter 23 - Classification and Tabulation of Data (Ex 23.1) Exercise 23.1

1. How can you define Data?

A collection of information gathered through observations, measurements, research, or analysis is referred to as Data. It could include information such as facts, figures, numbers, names, or even general descriptions of objects. For our study, Data can be organised in the form of graphs, charts, or tables. Through Data mining, Data scientists assist in the analysis of collected Data. For example, Data can be used to represent information gathered, as shown below.


5, 6, 7, 8, 9 are a set of numbers.


A list of a Class's students' names.


Age, height, weight, and other physical characteristics

2. Differentiate between discrete Data and continuous Data.

Both, discrete Data and continuous Data are sub-parts of quantitative Data. Saying that both the Data, discrete and continuous, deal with the number of say numbers. The description of both the Data types is listed below.

  • Discrete: This type of Data uses countable values, such as the number of fruits on a tree, the number of students in a Class, and so on.

  • Continuous: Weight, length, temperature, speed, and other specific values that can be measured and fall within a specific range are examples of this type of Data.

3. Explain primary Data and Secondary Data.

Explanation of primary Data and Secondary Data is as follows:

  • Primary Data: Individually collected Data is referred to as primary Data. Data collected by a student in a lab experiment, a teacher administering an oral test and recording the results, letters, records, autobiographies, and so on.

  • Secondary Data: Secondary Data is information that has been gathered by someone else and is being used elsewhere. Another teacher, for example, may evaluate students using the results of an oral test, newspapers, encyclopaedias, biographies, and other sources.

4.What do you mean by nominal Data and ordinal Data?

Both, Nominal Data and Ordinal Data are sub-parts of qualitative Data. Qualitative Data is the type of Data that can be both recorded and observed. The difference between nominal Data and ordinal Data is as follows:

  • Nominal Data: It is a type of Data that is primarily used for naming, labelling, and Classification. It's also known as "named Data." Gender, country, race, eye colour, hair colour, hairstyle, and so on are examples.

  • Ordinal Data: It's a type of Data that is labelled, ordered, and has a range applied to it. For example, first, second, and third place in a Class.

5. Explain both, qualitative Data and quantitative Data.

As the name implies, the product is of high quality, and high quality entails uniqueness. Data that can be observed and recorded is referred to as qualitative Data. Gender, phone numbers, citizenship, and so on.


Qualitative Data is further divided into the following categories:

  • Nominal Data

  • Ordinal Data

It deals with quantity, and quantity is related to numbers, as the name implies. Numeric Data is another name for it. For example, how many bananas are in a dozen, how many candies are in a box, and so on?


Quantitative Data is further divided into the following categories:

  • Discrete Data

  • Continuous Data