Histogram

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Histogram
Histogram of arrivals per minute.svg
First described byKarl Pearson
PurposeTo roughly assess the probability distribution of a given variable by depicting the frequencies of observations occurring in certain ranges of values
 
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For the histograms used in digital image processing, see Image histogram and Color histogram.
Histogram
Histogram of arrivals per minute.svg
First described byKarl Pearson
PurposeTo roughly assess the probability distribution of a given variable by depicting the frequencies of observations occurring in certain ranges of values

A histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to bin the range of values, and then count how many values fall into each interval. A rectangle is drawn with height proportional to the count and width equal to the bin size, so that rectangles abut each other. A histogram may also be normalized displaying relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and usually equal size.[2] The rectangles of a histogram are drawn so that they touch each other to indicate that the original variable is continuous.[3]

Histograms give a rough sense of the density of the data, and often for density estimation: estimating the probability 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 the x-axis are all 1, then a histogram is identical to a relative frequency plot.

A histogram can be thought of as a simplistic kernel density estimation, which uses a kernel to smooth frequencies over the bins. This will yields a smoother 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.

A variable binwidth histogram was introduced by Denby and Mallows (2009). Examples of this are displayed on Census bureau data below.

Another alternative is the average shifted histogram which is fast to compute, and gets a smooth curve estimate of the density without using kernels.

The histogram is one of the seven basic tools of quality control.[4]

Histograms are often confused with barcharts. Barcharts are plots of categorical variables, and the discontinuity should be indicated by having gaps between the rectangles. Often this is neglected which may lead to a barchart being confused for a histogram.

Etymology[edit]

An example histogram of the heights of 31 Black Cherry trees.

The etymology of the word histogram is uncertain. Sometimes it is said to be derived from the Greek histos 'anything set upright' (as the masts of a ship, the bar of a loom, or the vertical bars of a histogram); and gramma 'drawing, record, writing'. It is also said that Karl Pearson, who introduced the term in 1891, derived the name from "historical diagram".[5]

Examples[edit]

This is a toy example

Example histogram.png
BinCount
-3.59
-2.532
-1.5109
-0.5180
0.5132
1.534
2.54
3.59

The language 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 your data on several different binwidths to learn more about it. Here is an example on tips given in a restaurant.

Here are a couple more examples.

Prices of houses sold in Ames in 2009, exhibits some right-skew.

Housingprice.png

Aces by players in a grand slam tennis tournament, facetted by gender. There are more aces in the mens game.

Tennis-aces.png

The U.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, Table 2 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 arbitrarily rounded numbers is a common phenomenon when collecting data from people.[citation needed]

Histogram of travel time (to work), US 2000 census. Area under the curve equals the total number of cases. This diagram uses Q/width from the table.
Data by absolute numbers
IntervalWidthQuantityQuantity/width
054180836
55136872737
105186183723
155196343926
205179813596
25571901438
305163693273
3553212642
4054122824
45159200613
60306461215
9060343557

This histogram shows the number of cases per unit 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 the curve represents the total number of cases (124 million). This type of histogram shows absolute numbers, with Q in thousands.

Histogram of travel time (to work), US 2000 census. Area under the curve equals 1. This diagram uses Q/total/width from the table.
Data by proportion
IntervalWidthQuantity (Q)Q/total/width
0541800.0067
55136870.0221
105186180.0300
155196340.0316
205179810.0290
25571900.0116
305163690.0264
35532120.0052
40541220.0066
451592000.0049
603064610.0017
906034350.0005

This histogram differs from the first only in the vertical 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 simple density 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]

Mathematical definition[edit]

An ordinary and a cumulative histogram of the same data. The data shown is a random sample of 10,000 points from a normal distribution with a mean of 0 and a standard deviation of 1.

In a more general mathematical sense, a histogram is a function mi that counts the number of observations that fall into each of the disjoint categories (known as bins), whereas the graph of a histogram is merely one way to represent a histogram. Thus, if we let n be the total number of observations and k be the total number of bins, the histogram mi meets the following conditions:

n = \sum_{i=1}^k{m_i}.

Cumulative histogram[edit]

A cumulative histogram is a mapping that counts the cumulative number of observations in all of the bins up to the specified bin. That is, the cumulative histogram Mi of a histogram mj is defined as:

M_i = \sum_{j=1}^i{m_j}.

Number of bins and width[edit]

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 as Graunt's work in the 17th century, but no systematic guidelines were given[8] until Sturges's work in 1926.[9]

Using wider bins where the density 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.[10]

The number of bins k can be assigned directly or can be calculated from a suggested bin width h as:

k = \left \lceil \frac{\max x - \min x}{h} \right \rceil.

The braces indicate the ceiling function.

Square-root choice
k = \sqrt{n}, \,

which takes the square root of the number of data points in the sample (used by Excel histograms and many others).[11]

Sturges' formula

Sturges' formula[9] is derived from a binomial distribution and implicitly assumes an approximately normal distribution.

k = \lceil \log_2 n + 1 \rceil, \,

It implicitly bases the bin sizes on the range of the data and can perform poorly if n < 30, because the number of bins will be small—less than seven—and unlikely to show trends in the data well. It may also perform poorly if the data are not normally distributed.

Rice Rule
k = \lceil 2 n^{1/3}\rceil,

The Rice Rule [12] is presented as a simple alternative to Sturges's rule.

Doane's formula

Doane's formula[13] is a modification of Sturges' formula which attempts to improve its performance with non-normal data.

 k = 1 + \log_2( n ) + \log_2 \left( 1 +  \frac { |g_1| }{\sigma_{g_1}} \right)

where g_1 is the estimated 3rd-moment-skewness of the distribution and

 \sigma_{g_1} = \sqrt { \frac { 6(n-2) }{ (n+1)(n+3) } }
Scott's normal reference rule
h = \frac{3.5 \hat \sigma}{n^{1/3}},

where \hat \sigma is the sample standard deviation. Scott's normal reference rule[14] is optimal for random samples of normally distributed data, in the sense that it minimizes the integrated mean squared error of the density estimate.[8]

Freedman–Diaconis' choice

The Freedman–Diaconis rule is:[15][8]

h = 2 \frac{\operatorname{IQR}(x)}{n^{1/3}},

which is based on the interquartile 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.

Choice based on minimization of an estimated L2[16] risk function
 \underset{h}{\operatorname{arg\,min}} \frac{ 2 \bar{m} - v } {h^2}

where \textstyle \bar{m} and \textstyle v are mean and biased variance of a histogram with bin-width \textstyle h, \textstyle \bar{m}=\frac{1}{k} \sum_{i=1}^{k}  m_i and \textstyle v= \frac{1}{k} \sum_{i=1}^{k} (m_i - \bar{m})^2 .

Remark

A good reason why the number of bins should be proportional to n^{1/3} is the following: suppose that the data are obtained as n independent realizations of a bounded probability distribution with smooth density. Then the histogram remains equally »rugged« as n tends to infinity. If s 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 n h/s and the relative standard error is of order \sqrt{s/(n h)}. Comparing to the next bin, the relative change of the frequency is of order h/s provided that the derivative of the density is non-zero. These two are of the same order if h is of order s/n^{1/3}, so that k is of order n^{1/3}.

This simple cubic root choice can also be applied to bins with non-constant width.

See also[edit]

References[edit]

  1. ^ Pearson, K. (1895). "Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 186: 343–414. Bibcode:1895RSPTA.186..343P. doi:10.1098/rsta.1895.0010.  edit
  2. ^ Howitt, D. and Cramer, D. (2008) Statistics in Psychology. Prentice Hall
  3. ^ Charles Stangor (2011) "Research Methods For The Behavioral Sciences". Wadsworth, Cengage Learning. ISBN 9780840031976.
  4. ^ Nancy R. Tague (2004). "Seven Basic Quality Tools". The Quality Toolbox. Milwaukee, Wisconsin: American Society for Quality. p. 15. Retrieved 2010-02-05. 
  5. ^ M. Eileen Magnello (December 2006). "Karl Pearson and the Origins of Modern Statistics: An Elastician becomes a Statistician". The New Zealand Journal for the History and Philosophy of Science and Technology. 1 volume. OCLC 682200824. 
  6. ^ US 2000 census.
  7. ^ Dean, S., & Illowsky, B. (2009, February 19). Descriptive Statistics: Histogram. Retrieved from the Connexions Web site: http://cnx.org/content/m16298/1.11/
  8. ^ a b c Scott, David W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. New York: John Wiley. 
  9. ^ a b Sturges, H. A. (1926). "The choice of a class interval". Journal of the American Statistical Association: 65–66. JSTOR 2965501. 
  10. ^ e.g. § 5.6 "Density Estimation", W. N. Venables and B. D. Ripley, Modern Applied Statistics with S (2002), Springer, 4th edition. ISBN 0-387-95457-0.
  11. ^ EXCEL 2007: Histogram
  12. ^ Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University (chapter 2 "Graphing Distributions", section "Histograms")
  13. ^ Doane DP (1976) Aesthetic frequency classification. American Statistician, 30: 181–183
  14. ^ Scott, David W. (1979). "On optimal and data-based histograms". Biometrika 66 (3): 605–610. doi:10.1093/biomet/66.3.605. 
  15. ^ Freedman, David; Diaconis, P. (1981). "On the histogram as a density estimator: L2 theory". Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 57 (4): 453–476. doi:10.1007/BF01025868. 
  16. ^ Shimazaki, H.; Shinomoto, S. (2007). "A method for selecting the bin size of a time histogram". Neural Computation 19 (6): 1503–1527. doi:10.1162/neco.2007.19.6.1503. PMID 17444758. 

Further reading[edit]

External links[edit]