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The shape of the data determines the type of tools that can be used to draw conclusions from it. Here is how to graphically plot out the data to find its shape:
Step 1: Plot Data into Categories
To begin with, the data must be divided into equal categories. The categories must have equal intervals to make the data meaningful. Then a frequency table must be prepared from the available data set and the number of times an item occurs within an interval category must be noted down.
Step 2: Draw a Histogram
The next step is to plot the data intervals on a graph paper and create a histogram. A histogram is nothing but a bar chart of a continuous set of data with equal intervals.
Step 3: Join the Midpoints to Find the Shape
The next step is to plot the midpoints of the bars of the histogram. These midpoints must then be joined to develop the curve of the data that is also called the shape of the data.
Amongst the many characteristics of the shape of the data that are important, perhaps the prime category is symmetry. The reasons for the same have been listed below.
The shape of the data is of such prime importance because statistical techniques have been developed which can make decisions about the probability of data based on its shape. The details of the same are as follows:
Symmetrical Data: Symmetrical data sis the easiest type of data to work with. This is because many statistical techniques have been developed for the same. In fact symmetrical data is so common that it is called the normal curve. It also has other names like the bell curve. There are standard measurements available which can tell the probability of a data point occurring based on the number of standard deviations it is away from the mean. From a six sigma point of view it helps understand how the results of a process are likely to be distributed.
Most things which are measured continuously in nature as well as in operations have the normal distribution. It is for this reason that the applications of symmetrical data are enormous.
Skewed Data: Many times the data is not symmetrical i.e it is skewed towards one side. Data can be either positively or negatively skewed. There are statistical techniques available which help us find out the probability distributions of skewed data too. However such techniques are not very well developed. This is because most of the sample data being collected usually follows the normal distribution. Statistical analysis of skewed data is therefore not often performed.
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