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Importance of Graphical Representation of Data in Statistics

In Statistics, a graphical representation is a visual display of data in the form of a diagram or graph. It represents the set of data in a meaningful way. It provides data where it helps to take decisions in a much better way. It encompasses a wide range of techniques to clarify, interpret and analyse the data by drawing line segments or by plotting the points in the graphs. It also uses the geometrical symbol to show the rapid visualisation of the data set. 

Economists use the graphical representation method to show the data value, increase or decrease. They make decisions by comparing the value or cost to confer the report of how a specific situation was yesterday and how it is today. They display the data in the form of graphs and charts. Generally, statisticians use four methods to represents the frequency distribution graphically. They are pie charts, ogive or frequency cumulative graph, Histogram, and smoothed frequency graph. Apart from these methods, other methods of representation are line graphs, bar graphs, stem and leaf plot etc. 

Since this method makes the data easier to understand, it has a lot of advantage in the field of Statistics. But it has some disadvantages also. Let’s discuss the pros and cons of using graphical representation. 

Advantages

Some of the benefits of using graphical representation methods are:

  • Decision Making
  • Comparative analysis
  • Acceptability
  • Represents complete idea
  • Less effort and time
  • Less error and mistake
  • Useful for low level educated people

Disadvantages

The drawbacks of using pictorial representations are

  • Costly
  • Lack of secrecy
  • Problems for selecting suitable methods
  • Problem of understanding

Statistics is associated with the collection, classification, arrangement and representation of numerical data. It allows us to interpret various results from the graph presentation and help us to forecast many possibilities. By using standard maths formulas, we can find different measures of central tendency like mean, median, mode, variance and standard deviation.

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