Drawing histograms and calculating frequency density

A Histogram is a graphical representation used in statistics to visually display the distribution of continuous data.
Unlike a bar chart, a histogram displays data that falls into different intervals, also known as bins, along the x-axis. The height of the bar denotes the frequency density of that interval.
Frequency density is calculated by dividing frequency by the class width. This allows for fair comparison of bins with differing widths.
In a histogram, the area of each bar is proportional to the frequency of data in the respective bin.

To draw a histogram, firstly calculate the frequency density for each bin. This is done by dividing the frequency by the class width.
Arrange the class intervals along the x-axis in ascending order.
Draw rectangular bars with bases as the class intervals and heights proportional to the frequency density (not the raw frequency!).
The bins should be adjacent, with no gaps in between, to signify the continuous nature of data.

Histograms are particularly useful when variable transformations have been performed, or when we need to compare two or more datasets that have different numbers of observations or bin widths.
In Histograms, the frequency density of a bin can be compared across different histograms, regardless the width of the bin. Thus making comparison between data sets more meaningful.

Frequency Density is calculated by dividing the frequency of the data in a bin by the width of the bin.
In a tabulated data set, frequency column represents the number of observations within those boundaries, and class width denotes the range of the data in that group.
The resulting frequency density value forms the vertical axis (y-axis) of the histogram.

Regularly practise constructing and interpreting histograms.
Engage in calculating frequency densities for various class intervals.
Consider the implications of different class widths and their effect on the histogram.
Understand how frequency density can allow for the comparison of differing data sets, regardless of differing bin widths. Study real-world data sets and attempt constructing their histograms to become confident in identifying frequency, class width, and frequency density.