A * histogram* is very similar to the bar chart for a

*based on*

**frequency distribution***showing the distribution of*

**quantitative data***. It is a useful graphical representation of data that helps to visualize the*

**qualitative data***.*

**distribution of data**The * histogram* is constructed from the

*by taking the*

**grouped data***(not class limits) along the x-axis and the corresponding frequencies along the y-axis. For ungrouped data, we have to form the*

**class boundaries***grouped frequency distribution*before making a

*It consists of a set of bars (like*

**histogram.***bar chart) but these bars are adjacent to each other and the height of the bars is proportional to the frequency associated with respective classes.*

**a**The area of each rectangle represented the respective class frequencies. When the class intervals are equal, the rectangles all have the same width and their heights directly represent the class frequencies. For the case in which class intervals are not all equal, the height of the rectangle (bar) over an unequal class interval, is to be adjusted because it is area and not the height that measures frequency. This means that the height of a rectangle must be proportionally decreased if the length of the corresponding class-interval increases.

For example, if the length of a class interval becomes double, then the height of the rectangle is to be halved so that area, being the fundamental property of the rectangle of a histogram remains unchanged. This sort of rescaling is necessary to observe the correct pattern of distribution.

The feature of a * histogram* is that there is no gap (space) between the vertical bars because the variable plotted on the horizontal axis is

*quantitative*and the variable is from the measure of scale either

*interval*or

*ratio.*Thus, the

*provides an easily interpreted visual representation of a frequency distribution. Note that class midpoints are used as labels for the classes.*

**histogram***The Histogram* allows us to analyze extremely large

*datasets*by reducing them to a single graphical representation which is used to show primary, secondary, and tertiary peaks in data and also helps us by giving a visual representation of the

*statistical significance*of those peaks.

An alternative to the * histogram* is

*kernel density estimation*, which uses a

*kernel*to

*smooth*samples. This will construct a smooth

*probability density function*, which will, in general, more accurately reflect the underlying variable.

**Histogram for continuous grouped data**

To draw a * histogram* from the

*continuous grouped frequency distribution*, the following steps are taken.

- Mark
*the class boundaries*of the classes along the x-axis. - Mark frequencies along the y-axis.
- Draw a rectangle for each class such that the height of each rectangle is proportional to the frequency corresponding to that class. This is the case when classes are of equal width as they often are.
- If the classes are of unequal width, then the area instead of the height of each rectangle is proportional to the frequency corresponding to that class, and the height of each rectangle is obtained by dividing the frequency of the class by the width of that class.

It may be noted that the area under a ** histogram** can be calculated by adding up the areas of all the rectangles that constitute the

*The area of one rectangle is obtained by the multiplication of the width of the class by the corresponding frequency i.e.*

**histogram.****Area of a single rectangle = width of the class x frequency of the class**

**Histogram for Discrete Data**

Bar graphs are usually drawn for discrete and categorical data but there are some situations where there is a need to make an approximation, the * histogram* may be constructed. To construct a

*for*

**histogram***discrete grouped data*, the following steps are taken:

- Mark possible values on the x-axis.
- Mark frequencies along the y-axis.
- Draw a rectangle centered on each value with equal width on each side possibly 0.5 to either side of the value.

The advantages of the * histogram* as compared to the unprocessed data are:

- It gives a range of data.
- It gives the location of the data.
- it gives a clue about the skewness of the data.
- It gives information about the out-of-control situation.
- Histograms are density estimates (give a good impression of the distribution of data.
- Can be compared to the normal curve.

The disadvantages are:

- Exact values cannot be read from
because data is grouped into categories and individuality of data vanishes in grouped data.**histograms** - It is more difficult to compare two data sets.
- It is used only for the continuous data set.