Ahistogram is a visual representation of thedistribution of quantitative data. To construct a histogram, the first step is to"bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlappingintervals of a variable. The bins (intervals) are adjacent and are typically (but not required to be) of equal size.[1]
Histograms give a rough sense of the density of the underlying distribution of the data, and often fordensity estimation: estimating theprobability density function of the underlying variable. The total area of a histogram used for probability density is always normalized to 1. If the length of the intervals on thex-axis are all 1, then a histogram is identical to arelative frequency plot.
Histograms are sometimes confused withbar charts. In a histogram, each bin is for a different range of values, so altogether the histogram illustrates the distribution of values. But in a bar chart, each bar is for a different category of observations (e.g., each bar might be for a different population), so altogether the bar chart can be used to compare different categories. Some authors recommend that bar charts always have gaps between the bars to clarify that they are not histograms.[2][3]
The term "histogram" was first introduced byKarl Pearson, the founder of mathematicalstatistics, in lectures delivered in1892 atUniversity College London. Pearson's term is sometimes incorrectly said to combine the Greek rootγραμμα (gramma) = "figure" or "drawing" with the rootἱστορία (historia) = "inquiry" or "history". Alternatively the rootἱστίον (histion) is also proposed, meaning "web" or "tissue" (as inhistology, the study of biological tissue). Both of theseetymologies are incorrect, and in fact Pearson, who knew Ancient Greek well, derived the term from a different ifhomophonous Greek root,ἱστός = "something set upright", "mast", referring to the vertical bars in the graph. Pearson's new term was embedded in a series of other analogousneologisms, such as "stigmogram" and "radiogram".[4]
Pearson himself noted in 1895 that although the term "histogram" was new, the type of graph it designates was "a common form of graphical representation".[5]In fact the technique of using a bar graph to represent statistical measurements was devised by the Scottisheconomist,William Playfair, in hisCommercial and political atlas (1786).[4]
This is the data for the histogram to the right, using 500 items:
| Bin/Interval | Count/Frequency |
|---|---|
| −3.5 to −2.51 | 9 |
| −2.5 to −1.51 | 32 |
| −1.5 to −0.51 | 109 |
| −0.5 to 0.49 | 180 |
| 0.5 to 1.49 | 132 |
| 1.5 to 2.49 | 34 |
| 2.5 to 3.49 | 4 |
The words used to describe the patterns in a histogram are: "symmetric", "skewed left" or "right", "unimodal", "bimodal" or "multimodal".
It is a good idea to plot the data using several different bin widths to learn more about it. Here is an example on tips given in a restaurant.
TheU.S. Census Bureau found that there were 124 million people who work outside of their homes.[6] Using their data on the time occupied by travel to work, the table below shows the absolute number of people who responded with travel times "at least 30 but less than 35 minutes" is higher than the numbers for the categories above and below it. This is likely due to people rounding their reported journey time.[citation needed] The problem of reporting values as somewhat arbitrarilyrounded numbers is a common phenomenon when collecting data from people.[citation needed]
| Interval | Width | Quantity | Quantity/width |
|---|---|---|---|
| 0 | 5 | 4180 | 836 |
| 5 | 5 | 13687 | 2737 |
| 10 | 5 | 18618 | 3723 |
| 15 | 5 | 19634 | 3926 |
| 20 | 5 | 17981 | 3596 |
| 25 | 5 | 7190 | 1438 |
| 30 | 5 | 16369 | 3273 |
| 35 | 5 | 3212 | 642 |
| 40 | 5 | 4122 | 824 |
| 45 | 15 | 9200 | 613 |
| 60 | 30 | 6461 | 215 |
| 90 | 60 | 3435 | 57 |
This histogram shows the number of cases perunit interval as the height of each block, so that the area of each block is equal to the number of people in the survey who fall into its category. The area under thecurve represents the total number of cases (124 million). This type of histogram shows absolute numbers, with Q in thousands.
| Interval | Width | Quantity (Q) | Q/total/width |
|---|---|---|---|
| 0 | 5 | 4180 | 0.0067 |
| 5 | 5 | 13687 | 0.0221 |
| 10 | 5 | 18618 | 0.0300 |
| 15 | 5 | 19634 | 0.0316 |
| 20 | 5 | 17981 | 0.0290 |
| 25 | 5 | 7190 | 0.0116 |
| 30 | 5 | 16369 | 0.0264 |
| 35 | 5 | 3212 | 0.0052 |
| 40 | 5 | 4122 | 0.0066 |
| 45 | 15 | 9200 | 0.0049 |
| 60 | 30 | 6461 | 0.0017 |
| 90 | 60 | 3435 | 0.0005 |
This histogram differs from the first only in thevertical scale. The area of each block is the fraction of the total that each category represents, and the total area of all the bars is equal to 1 (the fraction meaning "all"). The curve displayed is a simpledensity estimate. This version shows proportions, and is also known as a unit area histogram.
In other words, a histogram represents a frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportional to the corresponding frequencies: the height of each is the average frequency density for the interval. The intervals are placed together in order to show that the data represented by the histogram, while exclusive, is also contiguous. (E.g., in a histogram it is possible to have two connecting intervals of 10.5–20.5 and 20.5–33.5, but not two connecting intervals of 10.5–20.5 and 22.5–32.5. Empty intervals are represented as empty and not skipped.)[7]
The data used to construct a histogram are generated via a functionmi that counts the number of observations that fall into each of the disjoint categories (known asbins). Thus, if we letn be the total number of observations andk be the total number of bins, the histogram datami meet the following conditions:
A histogram can be thought of as a simplistickernel density estimation, which uses akernel to smooth frequencies over the bins. This yields asmoother probability density function, which will in general more accurately reflect distribution of the underlying variable. The density estimate could be plotted as an alternative to the histogram, and is usually drawn as a curve rather than a set of boxes. Histograms are nevertheless preferred in applications, when their statistical properties need to be modeled. The correlated variation of a kernel density estimate is very difficult to describe mathematically, while it is simple for a histogram where each bin varies independently.
An alternative to kernel density estimation is the average shifted histogram,[8]which is fast to compute and gives a smooth curve estimate of the density without using kernels.
A cumulative histogram: a mapping that counts the cumulative number of observations in all of the bins up to the specified bin. That is, the cumulative histogramMi of a histogrammj can be defined as:
There is no "best" number of bins, and different bin sizes can reveal different features of the data. Grouping data is at least as old asGraunt's work in the 17th century, but no systematic guidelines were given[9] untilSturges's work in 1926.[10]
Using wider bins where the density of the underlying data points is low reduces noise due to sampling randomness; using narrower bins where the density is high (so the signal drowns the noise) gives greater precision to the density estimation. Thus varying the bin-width within a histogram can be beneficial. Nonetheless, equal-width bins are widely used.
Some theoreticians have attempted to determine an optimal number of bins, but these methods generally make strong assumptions about the shape of the distribution. Depending on the actual data distribution and the goals of the analysis, different bin widths may be appropriate, so experimentation is usually needed to determine an appropriate width. There are, however, various useful guidelines and rules of thumb.[11]
The number of binsk can be assigned directly or can be calculated from a suggested bin width h as:
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The braces indicate theceiling function.
which takes the square root of the number of data points in the sample and rounds to the nextinteger. This rule is suggested by a number of elementary statistics textbooks[12] and widely implemented in many software packages.[13]
Sturges's rule[10] is derived from abinomial distribution and implicitly assumes an approximately normal distribution.
Sturges's formula implicitly bases bin sizes on the range of the data, and can perform poorly ifn < 30, because the number of bins will be small—less than seven—and unlikely to show trends in the data well. On the other extreme, Sturges's formula may overestimate bin width for very large datasets, resulting in oversmoothed histograms.[14] It may also perform poorly if the data are not normally distributed.
When compared to Scott's rule and the Terrell-Scott rule, two other widely accepted formulas for histogram bins, the output of Sturges's formula is closest whenn ≈ 100.[14]
TheRice rule[15] is presented as a simple alternative to Sturges's rule.
Doane's formula[16] is a modification of Sturges's formula which attempts to improve its performance with non-normal data.
where is the estimated 3rd-moment-skewness of the distribution and
Bin width is given by
where is the samplestandard deviation.Scott's normal reference rule[17] is optimal for random samples of normally distributed data, in the sense that it minimizes the integrated mean squared error of the density estimate.[9] This is the default rule used in Microsoft Excel.[18]
TheTerrell–Scott rule[14][19] is not a normal reference rule. It gives the minimum number of bins required for an asymptotically optimal histogram, where optimality is measured by the integrated mean squared error. The bound is derived by finding the 'smoothest' possible density, which turns out to be. Any other density will require more bins, hence the above estimate is also referred to as the 'oversmoothed' rule. The similarity of the formulas and the fact that Terrell and Scott were at Rice University when the proposed it suggests that this is also the origin of the Rice rule.
TheFreedman–Diaconis rule gives bin width as:[20][9]
which is based on theinterquartile range, denoted by IQR. It replaces 3.5σ of Scott's rule with 2 IQR, which is less sensitive than the standard deviation to outliers in data.
This approach of minimizing integrated mean squared error from Scott's rule can be generalized beyond normal distributions, by using leave-one out cross validation:[21][22]
Here, is the number of datapoints in thekth bin, and choosing the value ofh that minimizesJ will minimize integrated mean squared error.
The choice is based on minimization of an estimatedL2risk function[23]
where and are mean and biased variance of a histogram with bin-width, and.
Rather than choosing evenly spaced bins, for some applications it is preferable to vary the bin width. This avoids bins with low counts. A common case is to chooseequiprobable bins, where the number of samples in each bin is expected to be approximately equal. The bins may be chosen according to some known distribution or may be chosen based on the data so that each bin has samples. When plotting the histogram, thefrequency density is used for the dependent axis. While all bins have approximately equal area, the heights of the histogram approximate the density distribution.
For equiprobable bins, the following rule for the number of bins is suggested:[24]
This choice of bins is motivated by maximizing the power of aPearson chi-squared test testing whether the bins do contain equal numbers of samples. More specifically, for a given confidence interval it is recommended to choose between 1/2 and 1 times the following equation:[25]
Where is theprobit function. Following this rule for would give between and; the coefficient of 2 is chosen as an easy-to-remember value from this broad optimum.
A good reason why the number of bins should be proportional to is the following: suppose that the data are obtained as independent realizations of a bounded probability distribution with smooth density. Then the histogram remains equally "rugged" as tends to infinity. If is the "width" of the distribution (e. g., the standard deviation or the inter-quartile range), then the number of units in a bin (the frequency) is of order and therelative standard error is of order. Compared to the next bin, the relative change of the frequency is of order provided that the derivative of the density is non-zero. These two are of the same order if is of order, so that is of order. This simple cubic root choice can also be applied to bins with non-constant widths.[citation needed]
