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# histc

computes an histogram

### Calling Sequence

[cf, ind] = histc(n, data [,normalization]) [cf, ind] = histc(x, data [,normalization])

### Arguments

- n
positive integer (number of classes)

- x
increasing vector defining the classes (

`x`

may have at least 2 components)- data
vector (data to be analysed)

- cf
vector representing the number of values of

`data`

falling in the classes defined by`n`

or`x`

- ind
vector or matrix of same size as

`data`

, representing the repective belonging of each element of data`data`

to the classes defined by`n`

or`x`

- normalization
scalar boolean.

`normalization=%f (default)`

:`cf`

represents the total number of points in each class,`normalization=%t`

:`cf`

represents the number of points in each class, relatively to the total number of points

### Description

This function computes a histogram of the `data`

vector using the
classes `x`

. When the number `n`

of classes is provided
instead of `x`

, the classes are chosen equally spaced and
*x(1) = min(data) < x(2) = x(1) + dx < ... < x(n+1) = max(data)*
with *dx = (x(n+1)-x(1))/n*.

The classes are defined by `C1 = [x(1), x(2)]`

and `Ci = ( x(i), x(i+1)]`

for i >= 2.
Noting `Nmax`

the total number of `data`

(Nmax = length(data))
and `Ni`

the number of `data`

components falling in
`Ci`

, the value of the histogram for `x`

in
`Ci`

is equal to *Ni/(Nmax (x(i+1)-x(i)))* when
`"normalized"`

is selected and else, simply equal to *Ni*.
When normalization occurs the histogram verifies:

when *x(1)<=min(data)* and *max(data) <= x(n+1)*

### Examples

- Example #1: variations around a histogram of a gaussian random sample
- Example #2: histogram of a binomial (B(6,0.5)) random sample
- Example #3: histogram of an exponential random sample
- Example #4: the frequency polygon chart and the histogram of a gaussian random sample

### See Also

### History

Version | Description |

5.5.0 | Introduction |

## Comments

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