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Determination of the number of groups in histogram generation# Determination of the number of groups in histogram generation

##### Sturges formula and other methods of determining the number of groups in histogram generation

Today I will talk about the statics and histograms. Generally speaking, the histogram is a graphic display of grouping method, i.e. the distribution of the measurements set of some quantity into groups according to an important feature of this group. The grouping method is widely used for primary data processing.

Under the primary data in the statistics we understand the statistical series, which is called **time series** when it comes to changing of phenomena in time, or **variation series** when it comes to the composition or structure of the investigated phenomenon.

If it comes to the series, based on the qualitative attribute (for example, enterprises by their property category), these series are called ** attributive**, if the series are built by quantitative attribute (for example, enterprises by their trade turnover), they are called **variational**.

Depending on the continuity of the variation, there are **discrete** and **interval** variation series.

A histogram is a bar graph constructed based on the findings, which are divided into several groups. The amount of data allocated in each group (frequency) is expressed by the height of the column corresponding to this group.

The histogram can be built for any series, while if it is an attribute or a discrete variation series (for example, the number of workers in each wage category), the number of allocated groups is equal to the number of the characteristic value options. In case of interval variation series, the number of groups will depend on the size of the interval used to group data.

Interval - is the difference between the maximum and minimum values of an attribute in each group. i.e. the more groups the lower the interval and vice versa. Groups, in this case, sometimes referred to as **interval classes**.

For example, you can divide the obtained data about the number of workers on the enterprises by the following groups:

up to 25 men

25-50 men

50-100 men

more than 100 men.

Thus, the histogram will contain 5 columns, the height of which will correspond to the number of companies in this group.

We'll note that the distribution above is an example of the **uneven intervals** use, allocated with the research program i.e. us.

The question of choosing the value of the interval (number of groups) used for elements of interval variation series grouping is not an easeful one. Apart from the fact that the histogram is an excellent means of data visualization, it is also nothing more than an approximation of **probability distribution function** (see the picture). I.e. the value of a column in each group shows the probability that the next measured value will fall into this group.

The large amount of groups can give cause too "jerky" graph and the little amount - too "smooth". Ideally, it's obvious that it's better to have the amount of groups giving you the least deviations of probability distribution function i.e. able to give you the most precise evaluations of the true probability distribution function of the studied phenomenon.

In general, the mathematicians studied this

It seems first one was Sturges. He reviewed the idealized frequency histogram of k classes, where the i-th value equal to the binomial coefficient . For sufficiently large k the histogram form was approaching the form of a normal distribution. The sum of all values was equal .

Thus, for n results of the value measurement that are normally distributed, the number of classes used in histogram should be taken as and the form of the obtained histogram will be closer to the shape of the normal distribution for sufficiently large k. This is the **Sturgess formula**. In this form, it has got almost all the textbooks on statistics.

This formula is now being criticized just for the fact that it explicitly uses the binomial distribution to approximate of the normal distribution, which is not always applicable. It is believed that this formula allows you to build a satisfactory histogram if the number of dimensions less than 200.

There are a number of alternative formulas, some of which calculate the length of the interval, and then determines the number of required classes (seehere).

Let's review some of these formulas

**Scott's formula **

, where h - interval length, s - the standard deviation of the measurement series

**Friedman/Diaconis formula**

, where h - interval length, (IQ) - the difference between the upper and lower quartile.

These formulas are quite simple and justified by statistical theory and considered more preferable formula Sturgess.

The calculator below uses as the results the measurements of random number generator built into Javascript.

Since distribution function of the generator is practically constant, the random number received from the generator can be further modified by selecting something interesting in "Function ..." graph. This will actually let us observe more cheerful graphics instead of almost a straight line.

In addition to constructing a histogram using the number of classes obtained by the Sturgess formula, it builds histograms based on the number of classes based on Scott and Friedman/Diaconis and also with the number of classes randomly set by the user.

Of course, there is no practical application to this calculator but you can see the difference in the amount of classes and the appearance of the histogram.

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