CHAPTER 3- Organisation of Data
Introduction:
In everyday life, raw data is often messy and unorganized. To make sense of it, we need to classify or organize it. This chapter explains how to organize data into different categories for easier analysis, using examples from daily life and simple activities.
Raw Data:
Definition: Raw data is unorganized and scattered, making it hard to interpret.
Example: Think about how a junk dealer (kabadiwala) organizes materials like paper, glass, and plastic into different categories. This makes it easier to find items later.
1.1 Importance of Organizing Data:
Just like the kabadiwala, organizing information into categories helps us manage and understand it better.
When we classify data, we can analyze it more effectively and draw conclusions.
Classification of Data:
Classification means grouping things based on shared characteristics.
Purpose: It helps to bring order to raw data and makes further analysis easy.
2.1 Types of Classification:
Chronological Classification: Data arranged by time (years, months, etc.).
Example: Population data organized by year.
Geographical Classification: Data grouped by location (countries, states, cities).
Example: Wheat production across different countries.
Qualitative Classification: Grouping data based on qualities or attributes like gender, religion, or marital status.
2.2 Quantitative Classification:
This involves grouping data by numbers, like age, height, weight, or marks scored in an exam.
Example: Grouping student marks into categories like 0-10, 11-20, and so on.
Frequency Distribution:
Definition: It shows how often (or frequently) different values occur in a dataset.
Class Intervals: Values are divided into intervals (e.g., 10-20, 21-30) with each interval showing how many observations fall within that range.
3.1 Types of Class Intervals:
Inclusive Class Intervals: Both the lower and upper limits are included.
Exclusive Class Intervals: Either the upper or lower limit is excluded.
Variables:
Definition: A variable is any characteristic or number that can be measured.
Types of Variables:
Continuous Variables: Can take any value (e.g., height, weight).
Discrete Variables: Can only take specific values (e.g., number of cars).
4.1 Examples:
Continuous: A person’s height could be 5.5 feet, 5.6 feet, etc.
Discrete: The number of students in a class (you can't have half a student).
Loss of Information:
Summary: Grouping data into intervals or classes means we lose some details. For instance, instead of knowing a specific student’s exact score, we just know they fall within a range (e.g., between 40 and 50 marks).
Conclusion:
Data classification is essential for making raw information easier to analyze and interpret. Without it, analyzing large sets of data can be overwhelming. The methods discussed—chronological, geographical, and quantitative classification—are tools that simplify this process.
Recap:
Classification brings order to raw data.
Frequency Distribution shows how values are grouped into classes.
Types of Variables: Continuous variables can take any value, while discrete variables take specific values.
Class Intervals: Data can be divided into inclusive or exclusive intervals.
