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