Creates a histogram of the input series.
HISTOGRAM(series, nbins, width, offset, "normalization")
series 
 
Any series, table, or expression evaluating to a series or table. 

nbins 
 
An integer, the number of bins. 

width 
 
Optional. A real, the width of each bin if 

offset 
 
Optional. A real, the starting bin edge if 

"normalization" 
 
Optional. A string, the normalization method:

HISTOGRAM(series, edges, "normalization")
series 
 
Any series, table, or expression evaluating to a series or table. 

edges 
 
A series, a list of bin edges. 

"normalization" 
 
Optional. A string, the normalization method:

A series or table displayed as a bar chart.
histogram({3, 5, 1, 7, 4, 5, 7, 3, 1, 9}, 10)
returns a series {2, 0, 2, 1, 2, 0, 0, 2, 0, 1} displayed in bar chart format.
histogram({3, 5, 1, 7, 4, 5, 7, 3, 1, 9}, 1, 0.8, 1.0)
same result as above except the bin width is explicitly specified as 0.8 and the starting bin is 1.0 .
W1: grand(10000, 1)
W2: gnorm(10000, 1)
W3: histogram(w1, 30)
W4: histogram(w2, 30)
W3 and W4 graphically demonstrate the distributions of uniform random noise and normally distributed random noise.
W1: 1..10;setvunits("V")
W2: hist(w1, {1, 2, 3, 5, 9});cool
Creates a shaded histogram with the following bin attributes:
Bin Edges 
Bin Counts 
1 <= bin1 < 2 
1 
2 <= bin2 < 3 
1 
3 <= bin3 < 5 
2 
5 <= bin4 < 9 
5 
9 <= bin5 <= 10 
1 
W1: gnorm(10000, 1, 2, 5)
W2: pdfnorm(25..0.01..25, 2, 5)
W3: hist(w1, 50, "pdf");overp(w2, lred)
W1 contains 10000 samples of normally distributed values with a mean of 2 and a standard deviation of 5.
W2 computes the probability distribution of a normal distribution with a mean of 2 and a standard deviation of 5.
W3 creates a 50 sample histogram normalized based on the PDF. The analytic distribution is overplotted for comparison.
W1: gnorm(10000, 1, 2, 5)
W2: gnorm(1000, 1, 10, 5)
W3: hist(w1, 30, "prob")
W4: hist(w2, 30, "prob");overp(w3, lred)
W1 contains 10000 samples of normally distributed values with a mean of 2 and a standard deviation of 5.
W2 contains 1000 samples of normally distributed values with a mean of 10 and a standard deviation of 5.
W3 creates a 30 sample histogram of W1 normalized such that the sum of the histogram is 1.
W4 creates a 30 sample histogram of W2 normalized such that the sum of the histogram is 1. The histogram in W3 is overplotted for comparison.
For display purposes, the number of bins should be a fairly small number.
The histogram is displayed as a bar chart. By default, the left corner of each bar is located on the bin edge and no gaps are displayed between the bars. Thus, each bar starts on the bin edge and has an extent equal to the bin width.
When a series of bin edges is specified, the result is an XY series where the first and last XY pairs may be padded to yield the proper bar chart. Use YVALS to obtain the bin counts separately. For example:
C = yvals(hist(1..10, {1, 2, 3, 5, 9}));
C == {1, 1, 2, 5, 1}
By default, no normalization is performed and the sum of the result is equal the length of the input.
The "pdf" normalization divides the raw histogram by the area.
The "prob" normalization divides the raw histogram by the sum.
HISTOGRAM can be abbreviated HIST.