For this, you divide the number of values within one bin by the bin width. To determine the height of the bars, we should also calculate the width. Now, the individual data is divided into bins and determines the bin frequency. For example, one bin could include throws between 30 and 34 meters. It’s a good idea to ensure uniformity, though-at least in the middle part of the chart-as this makes the visual representation easier to understand. In a histogram, the width of the bar makes it clear how big the respective bin is. To do this, you divide the measured values into different bins. You’ll want to process these values visually. The people in charge naturally measure different throws here. Let’s assume you want to process the results of a throwing competition from a children’s sports day visually using a histogram.
When you create this kind of chart, you can independently set the size of the bin. The size of a bin can be read from the width of the bar – and this is one of the advantages of a histogram. Here, both the width and the height of the bars play a role. With the appropriate graphics, it’s possible to read how often certain values appear in one bin (a group of values). Histograms represent the distribution of frequencies, which is why this kind of chart is mainly used in statistics.
