A graph is a sort of chart through which statistical data are represented in the form of line, or curves drawn across the coordinated points plotted on its surface. While all other diagrams discussed in the previous chapter give only a bird’s eye – view of a phenomenon, graphs help us in studying the cause and effect relationship between two variables. This means that with the help of graphs we can measure the extent of a change in one variable when another variable changes by a certain amount. Graphs are useful for studying both time series and frequency distribution as they give clear account and precise picture of a problem. Thus, from statistical point of view, graphs are much better tools than diagrams for presenting and analysing the data.

**Characteristics. **From the above definitions, the essential characteristics of a graph may be outlined as under :

- It is a chart a statistical data.
- It is drawn on a graph paper in form of a line, or a curve through the coordinated points.
- It shows cause and effect relationship between two variables without much effort and time.
- It brings to the trend of a phenomenon, and the way in which a trend changes.
- It is used in studying both a time series and a frequency distribution.
- It is more obvious, precise and accurate than diagrams.

- Difference Between Diagrams and Graphs
- Format of a Graph
- Graphs on Ratio Scale
- Types of Graphs and Their Displayal
- Histograms
- Diagram
- Multiple Bar Diagrams
- Simple Bar Diagram
- One Dimensional Diagram
- Merits of Diagrammatic Representation
- General Guidelines for Diagrammatic Representation
- Objectives of Diagrammatic Representation
- Demerits of Diagrammatic Representation
- Difference Between Tabulation and Diagrammatic Representation
- Cartogram
- Pictograms
- Characteristics of a Sample
- Cluster Sampling
- Probability Sampling Methods
- Non-Probability Sampling Methods
- Quasi Random Sampling
- Simple Random Sampling
- Stratified Random Sampling
- Theories of Sampling
- Frequency Polygon
- Ogive