Data Handling

Data handling is a statistic that we can consider as a useful concept to ensure the research data integrity. The concept addresses the security, preservation, and confidentiality of the research data. Data handling in maths mostly includes numeric data in each field. Numerical figures are essential to figure out data and statistics. All the numeric values are called observations. Well, you will get to know all the details like what is data handling, and examples will help you to discover how to represent data. Mainly, all the observations are known as data collectively. However, many data management tactics are known, and statisticians use them to handle the data. Let’s look into the data and statistics and methods.


Well, you probably know the term data but data, i.e. observations seem to be useful when you represent accurately. Numeric figures are collectively known as data, and specific data represent specific information. Well, here we can consider anything as data that means data may include measurements, descriptions, numbers, words, observations. Data management is the process of securing researched data for the analysis process and even after completion of any analysis process.

Types Of Data: Data Handling In Maths

Well, to perform any data handling method, you must know the types of data. Data or observations are classified into mainly two types.

  1. Qualitative Data

  2. Quantitative Data


Qualitative data is descriptive information, and quantitative data is numerical information. Furthermore, we can divide quantitative data into two sections, like continuous data and discrete data. Data that contain some specific values like whole numbers are separate data. Whereas continuous data can include any value from the given range.

Data Handling Examples

When you relate observations to your school data or real-life examples, you can understand its usage practically. Lat’s study some practical examples of data handling.

  • The number of pastries remaining after your school baking sale.

  • Make a tally chart of colours of bags all students carry to the school.

  • Draw a chart of the number of girls and boys in your class or school.

  • Create a circular chart of colours liked by your family members.

  • Make a tally chart of absent students per each class.


Examples: Related To Real Life

  • Voter Polls

  • Marketing Surveys

  • The temperature of different cities in your country.

Types Of Data Handling:

Data can be observed from many factors like you can take the running time of different people, marks obtained by different students, daily calorie consumption, etc. All such observation is data and representation of data by different types of data handling will help you to get the critical values for experiments. You can organize data in various types like in the chart or graph.

How to Represent Data?

You can use any method to represent your collected data according to your required results.


Bar Graph

Pictographs

Line Graphs

Stem and Leaf Plot

Histograms

Dot Plots

Cumulative Tables and Graphs

Frequency Distribution


The bar graph is a very common, useful and straightforward method to represent the data. Let’s study Bar Graph with example.

Representation Of Data: Bar Graph

Numbers, pictures, graphics, and tables can be used to represent the data, and the most common method is a bar graph. In the bar graph method, all data is represented as bars, and it will give the bright idea of increment or decrement of any entity. The bar graph is also considered as a bar chart. There is a plane of vertical and horizontal lines to present the data as bars. Bars are rectangular flat visuals that place according to the researched observations. Bar length is proportional to the given entities. Well, the following example will help you to understand the bar graph.

Solved Examples: Bar Graph

Marks and attendance of about 400 students from the school are collected in a table. Represent the data with the bar graph method.

Table Of Data to Make The Bar Graph

Attendance (in percentage)

Number of Students

60

105

70

199

80

29

90

73

Total

406


Bar Graph

Each bar in the above example is of uniform width, and the data which varies is represented on one of the axes. Another axis represents the measure of the variable data through the height of the bars. The heights or the lengths of the bars denote the value of the variable. These graphs are also used to compare specific quantities.


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X-axes represent the attendance of all students, and Y-axes represent the number of students. The length of bars represents the number of students, and all the bars are the same in width. Well, after drafting bars in the chart, we can conclude the attendance of students in the school. We can find the following points by observing the above graph.


Number of students with 60% attendance: 105

Number of students with 70% attendance: 199

Number of students with 80% attendance: 29

Number of students with 90% attendance: 73


So, the bar graph will make your analysis organized and straightforward.


Solved Example:

The pie chart of 120 students representing their favourite juice is given below. From the chart, provide the answer to the simple questions given below.


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1.Is the Pie Chart More Useful Than a Bar Chart?

The pie chart is the best way to visualize data in percentage, and you can check the relative proportion of the given data.

 

2. What Data Cannot be Obtained from the Above Pie Chart?

The number of students who like the same juice.


3. Which Juice Do Students Love The Most?

Fruit cocktail.


4. Which Juice Is Wanted By The Same Number of Total Students?

Grape juice and apple juice.


FAQ (Frequently Asked Questions)

1. What is meant by data?

Data is different information collected by observation. Well, when you consider the data in maths, it relates to a specific purpose.

2. Briefly explain the steps of processing the data collection.

When you collect data for a specific purpose, it must have a pre-structure to use it effectively. Here we have listed some essential points to keep in mind to collect the data.


Define the purpose of data

Data fields you want for proper information

Decide the method of data collection

Collect data

Analyze data