Organisation of Data

INTRODUCTION

This section introduces the fundamental concept of data classification in statistics, emphasizing the necessity of organizing raw data to facilitate further statistical analysis. It begins by recalling the previous chapter's focus on data collection methods, including census and sampling. The primary purpose of classifying raw data is to bring order and structure, making it easier to analyze and interpret. The chapter uses the analogy of a local junk dealer (kabadiwallah) who collects various types of recyclable materials such as old newspapers, broken household items, glass bottles, and plastics. Without proper organization, managing such a diverse and voluminous collection would be difficult. The kabadiwallah groups similar items together — newspapers tied with a rope, glass bottles in a sack, metals sorted into iron, copper, aluminum, brass, etc. This classification helps him quickly locate items demanded by buyers and manage his trade efficiently. Similarly, students arranging their schoolbooks by subjects (History, Geography, Mathematics, Science) find it easier to locate a particular book. The section stresses that classification is not arbitrary; it is based on clear criteria relevant to the purpose of grouping. For example, history books are grouped under 'History' and not mixed with other subjects, preserving the utility of classification. Thus, classification is defined as the process of arranging or organizing things into groups or classes based on some criteria. This foundational understanding sets the stage for learning various methods and types of data classification in statistics.

• Classification organizes raw data to facilitate analysis.
• Raw data are unorganized and difficult to interpret directly.
• Classification is based on clear, relevant criteria, not arbitrary grouping.
• Analogy of kabadiwallah illustrates practical importance of classification.
• Classification saves time and effort in handling data or objects.
• Classification is essential before applying statistical methods.
• 📌 Classification: Arranging or organizing things into groups or classes based on some criteria.
• 📌 Raw Data: Unclassified, unorganized data collected from various sources.

RAW DATA

Raw data refers to unclassified, unorganized data collected from various sources. Like the kabadiwallah's junk, raw data are often voluminous, disorganized, and difficult to handle or analyze directly. Presenting raw data in a simple table form, such as marks obtained by 100 students in mathematics (Table 3.1) or monthly household expenditure on food of 50 households (Table 3.2), shows data points scattered without any order. Extracting meaningful information such as highest marks or average expenditure from such raw data is tedious and time-consuming, especially as the size of data increases. For example, finding the highest mark among 1000 students would be cumbersome without classification. The section highlights that raw data consist of observations on variables, and their usefulness depends on the purpose of analysis. For instance, a mathematics teacher looking at raw marks would want to understand students' performance, pass/fail status, etc. To do so, the teacher would classify the data, often by constructing a frequency distribution. The section concludes that classification is necessary to summarize and make raw data comprehensible, enabling easy location, comparison, and inference. The example of the Indian Census is cited, where raw data on 20 crore persons is classified by gender, education, marital status, occupation, etc., to understand population structure. This section thus emphasizes the importance of organizing raw data before statistical analysis. **Table on page 3 (10×10)** | 47 | 45 | 10 | 60 | 51 | 56 | 66 | 100 | 49 | 40 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 60 | 59 | 56 | 55 | 62 | 48 | 59 | 55 | 51 | 41 | | 42 | 69 | 64 | 66 | 50 | 59 | 57 | 65 | 62 | 50 | | 64 | 30 | 37 | 75 | 17 | 56 | 20 | 14 | 55 | 90 | | 62 | 51 | 55 | 14 | 25 | 34 | 90 | 49 | 56 | 54 | | 70 | 47 | 49 | 82 | 40 | 82 | 60 | 85 | 65 | 66 | | 49 | 44 | 64 | 69 | 70 | 48 | 12 | 28 | 55 | 65 | | 49 | 40 | 25 | 41 | 71 | 80 | 0 | 56 | 14 | 22 | | 66 | 53 | 46 | 70 | 43 | 61 | 59 | 12 | 30 | 35 | | 45 | 44 | 57 | 76 | 82 | 39 | 32 | 14 | 90 | 25 | **Table on page 3 (10×5)** | 1904 | 1559 | 3473 | 1735 | 2760 | | --- | --- | --- | --- | --- | | 2041 | 1612 | 1753 | 1855 | 4439 | | 5090 | 1085 | 1823 | 2346 | 1523 | | 1211 | 1360 | 1110 | 2152 | 1183 | | 1218 | 1315 | 1105 | 2628 | 2712 | | 4248 | 1812 | 1264 | 1183 | 1171 | | 1007 | 1180 | 1953 | 1137 | 2048 | | 2025 | 1583 | 1324 | 2621 | 3676 | | 1397 | 1832 | 1962 | 2177 | 2575 | | 1293 | 1365 | 1146 | 3222 | 1396 | **Table on page 12 (12×5)** | Class | Observations | Tally Mark | Frequency | Class Mark | | --- | --- | --- | --- | --- | | 0–10 | 0 | / | 1 | 5 | | 10–20 | 10, 14, 17, 12, 14, 12, 14, 14 | / / / / / / | 8 | 15 | | 20–30 | 25, 25, 20, 22, 25, 28 | / / / / / | 6 | 25 | | 30–40 | 30, 37, 34, 39, 32, 30, 35, | / / / / / | 7 | 35 | | 40–50 | 47, 42, 49, 49, 45, 45, 47, 44, 40, 44, 49, 46, 41, 40, 43, 48, 48, 49, 49, 40, 41 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 21 | 45 | | 50–60 | 59, 51, 53, 56, 55, 57, 55, 51, 50, 56, 59, 56, 59, 57, 59, 55, 56, 51, 55, 56, 55, 50, 54 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 23 | 55 | | 60–70 | 60, 64, 62, 66, 69, 64, 64, 60, 66, 69, 62, 61, 66, 60, 65, 62, 65, 66, 65 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 19 | 65 | | 70–80 | 70, 75, 70, 76, 70, 71 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 6 | 75 | | 80–90 | 82, 82, 82, 80, 85 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 5 | 85 | | 90–100 | 90, 100, 90, 90 | / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | 4 | 95 | | | Total | | 100 | | **Table on page 17 (3×15)** | 1 | 3 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 3 | 3 | 3 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 3 | 3 | 2 | 3 | 2 | 2 | 6 | 1 | 6 | 2 | 1 | 5 | 1 | 5 | 3 | | 2 | 4 | 2 | 7 | 4 | 2 | 4 | 3 | 4 | 2 | 0 | 3 | 1 | 4 | 3 | **Table on page 17 (5×14)** | 28 | 17 | 15 | 22 | 29 | 21 | 23 | 27 | 18 | 12 | 7 | 2 | 9 | 4 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 8 | 3 | 10 | 5 | 20 | 16 | 12 | 8 | 4 | 33 | 27 | 21 | 15 | | 3 | 36 | 27 | 18 | 9 | 2 | 4 | 6 | 32 | 31 | 29 | 18 | 14 | 13 | | 15 | 11 | 9 | 7 | 1 | 5 | 37 | 32 | 28 | 26 | 24 | 20 | 19 | 25 | | 19 | 20 | 6 | 9 | | | | | | | | | | |

• Raw data are unclassified and disorganized, making analysis difficult.
• Large volumes of raw data are cumbersome to handle without classification.
• Raw data consist of observations on variables.
• Classification summarizes raw data to facilitate meaningful conclusions.
• Example: Indian Census data classified by demographic attributes.
• Purpose of classification depends on the analysis objective.
• 📌 Raw Data: Unclassified, unorganized data collected from various sources.
• 📌 Variable: A characteristic or attribute that can assume different values.