CHAPTER 4- Presentation of Data
Introduction:
Data collected from various sources is usually large and hard to understand in its raw form. To make sense of this data, we need to present it in an organized way. This chapter discusses three common methods of data presentation: textual, tabular, and diagrammatic, each serving different purposes based on the size and complexity of the data.
Textual Presentation of Data:
Definition: In textual presentation, the data is described within the text itself. This is best suited when the amount of data is small.
Example: A news report stating that, out of 100 schools in a town, 10 were closed and 90 were open due to a strike. The data is simple and small enough to be explained through text.
1.1 Advantages:
Helps in emphasizing important points.
Suitable for small datasets.
1.2 Disadvantages:
Becomes difficult to understand when the data is large and complex.
You need to read through the entire text to grasp the data.
Tabular Presentation of Data:
Definition: Data is organized into rows and columns in tables, making it easier to interpret large amounts of information.
2.1 Example of Tabular Data:
A table showing literacy rates in rural and urban areas for males and females could be set up with rows for each group (male, female, total) and columns for rural and urban locations.
2.2 Types of Classification:
Qualitative Classification: This groups data based on qualities or attributes (e.g., male/female, rural/urban).
Quantitative Classification: Groups data based on numerical characteristics, like age, income, or height.
Temporal Classification: Organizes data by time (e.g., sales by year).
Spatial Classification: Groups data by location (e.g., export data by countries).
2.3 Advantages of Tabular Presentation:
Organizes data for further analysis.
Makes comparisons between different groups easy.
Summarizes large data sets effectively.
Diagrammatic Presentation of Data:
Definition: Diagrammatic presentation uses charts, graphs, and diagrams to represent data visually. This method is easier to understand and is often more effective than tables for interpreting large amounts of data.
3.1 Types of Diagrams:
Bar Diagrams: Use rectangular bars of equal width to represent data. These can be simple bar diagrams (comparing one variable) or multiple bar diagrams (comparing two or more sets of data).
Example: A bar diagram comparing literacy rates between different states.
Pie Diagrams: A circular chart divided into slices, where each slice represents a percentage of the total.
Example: A pie chart showing the population distribution by working status (main workers, marginal workers, non-workers).
Histograms: Used to represent frequency distributions. Rectangles are drawn with their heights proportional to the class frequency.
Example: A histogram showing the distribution of daily wage earners based on their earnings.
Frequency Polygon: Created by joining midpoints of histogram bars. It is a smoother alternative to a histogram and useful for comparing multiple distributions.
Ogive: A cumulative frequency curve that helps identify the median of a dataset.
Line Graph: Plots points on a graph to show trends over time (e.g., exports and imports over the years).
3.2 Advantages of Diagrammatic Presentation:
Provides a quick, visual representation of data.
Easier to compare different data points.
Ideal for showing trends and patterns.
Conclusion:
By now, you should have a good understanding of how data can be presented in different forms—textual, tabular, and diagrammatic. Each method has its own strengths depending on the type and quantity of data being presented. Presenting data effectively helps us comprehend large datasets quickly and makes decision-making easier.
Recap:
Textual Presentation is good for small amounts of data but can be difficult for large datasets.
Tabular Presentation organizes data into rows and columns, making it ideal for large datasets and comparisons.
Diagrammatic Presentation visually represents data and is useful for showing trends, distributions, and comparisons.
