Simple statistics is a JavaScript library that does descriptive statistics,
regression, and classification. It can tell you basic things
like **minimum** and **maximum**,
but it can also compute tricky things like **standard deviation**
and **sample correlation**.
But more importantly, it's **see-through**:
you can read it, and it's written in a friendly, literate manner.

edit the left

to change the right

`npm install simple-statistics`

`var ss = require('simple-statistics');`

`git clone git://github.com/tmcw/simple-statistics.git`

Basic contracts of functions:

- Functions do not modify their arguments e.g. change their order
- Invalid input, like empty lists to functions that need 1+ items to work, will cause functions to return
`null`

.

*Optionally* mix in the following functions into the `Array`

prototype. Otherwise
you can use them off of the simple-statistics object itself.

If given a particular array instance as an argument, this adds the functions
only to that array rather than the global `Array.prototype`

. Without an argument,
it runs on the global `Array.prototype`

.

Mean of a single-dimensional Array of numbers. *Also available as .average(x)*

Sum of a single-dimensional Array of numbers.

Returns the number that appears most frequently in a single-dimensional Array of numbers. If there are multiple modes, the one that appears last is returned.

Variance of a single-dimensional Array of numbers.

Standard Deviation of a single-dimensional Array of numbers.

Return a simple random sample
of the given array. The sampling is *without replacement*, and uses a Fisher-Yates
sample to randomize.

The Median Absolute Deviation (MAD) is a robust measure of statistical dispersion. It is more resilient to outliers than the standard deviation. Accepts a single-dimensional array of numbers and returns a dispersion value.

Also aliased to `.mad(x)`

for brevity.

Median of a single-dimensional array of numbers.

Geometric mean of a single-dimensional array of **positive** numbers.

Harmonic mean of a single-dimensional array of **positive** numbers.

Finds the minimum of a single-dimensional array of numbers. This runs in linear `O(n)`

time.

Finds the maximum of a single-dimensional array of numbers. This runs in linear `O(n)`

time.

Does a student's t-test of a dataset `sample`

, represented by a single-dimensional array of numbers. `x`

is the known value, and the result is a measure of statistical significance.

The two-sample t-test is used to compare samples from two populations or groups, confirming or denying the suspicion (null hypothesis) that the populations are the same. It returns a t-value that you can then look up to give certain judgements of confidence based on a t distribution table.

This implementation expects the samples `sample_x`

and `sample_y`

to be given
as one-dimensional arrays of more than one number each.

Produces sample variance of a single-dimensional array of numbers.

Produces sample covariance of two single-dimensional arrays of numbers.

Produces sample correlation of two single-dimensional arrays of numbers.

Does a quantile of a dataset `sample`

,
at p. For those familiary with the `k/q`

syntax, `p == k/q`

. `sample`

must
be a single-dimensional array of numbers. p must be a number greater than or equal to
than zero and less or equal to than one, or an array of numbers following that rule.
If an array is given, an array of results will be returned instead of a single
number.

Given a `sample`

array, and a positive integer `chunkSize`

, splits an array
into chunks of `chunkSize`

size and returns an array of those chunks. This
does not change the input value. If the length of `sample`

is not divisible
by `chunkSize`

, the last array will be shorter than the rest.

Given a `sample`

array (with any type of contents), return a random permutation
of that array, using the Fisher-Yates shuffle
algorithm.

Given a `sample`

array (with any type of contents), return a random permutation
of that array, using the Fisher-Yates shuffle
algorithm.

This changes the input array in-place, as well as returns it - unlike `.shuffle()`

,
it does not create a shallow copy of the array.

Does a quantile of a dataset `sample`

,
at p. `sample`

must be a one-dimensional *sorted* array of numbers, and
`p`

must be a single number from zero to one.

Calculates the Interquartile range of a sample - the difference between the upper and lower quartiles. Useful as a measure of dispersion.

*Also available as .interquartile_range(x)*

Calculates the skewness of a sample, a measure of the extent to which a probability distribution of a real-valued random variable "leans" to one side of the mean. The skewness value can be positive or negative, or even undefined.

This implementation uses the Fisher-Pearson standardized moment coefficient, which means that it behaves the same as Excel, Minitab, SAS, and SPSS.

Skewness is only valid for samples of over three values.

Find the Jenks Natural Breaks for
a single-dimensional array of numbers as input and a desired `number_of_classes`

.
The result is a single-dimensional with class breaks, including the minimum
and maximum of the input array.

Find the r-squared value of a particular dataset, expressed as a two-dimensional `Array`

of numbers, against a `Function`

.

```
var r_squared = ss.r_squared([[1, 1]], function(x) { return x * 2; });
```

Look up the given `z`

value in a standard normal table
to calculate the probability of a random variable appearing with a given value.

The standard score is the number of standard deviations an observation or datum is above or below the mean.

A standard normal table from which to pull values of Φ (phi).

Create a new linear regression solver.

Set the data of a linear regression. The input is a two-dimensional array of numbers, which are treated as coordinates, like `[[x, y], [x1, y1]]`

.

Get the linear regression line: this returns a function that you can
give `x`

values and it will return `y`

values. Internally, this uses the `m()`

and `b()`

values and the classic `y = mx + b`

equation.

```
var linear_regression_line = ss.linear_regression()
.data([[0, 1], [2, 2], [3, 3]]).line();
linear_regression_line(5);
```

Just get the slope of the fitted regression line, the `m`

component of the full
line equation. Returns a number.

Just get the y-intercept of the fitted regression line, the `b`

component
of the line equation. Returns a number.

Create a naïve bayesian classifier.

Train the classifier to classify a certain item, given as an object with keys, to be in a certain category, given as a string.

Get the classifications of a certain item, given as an object of
`category -> score`

mappings.

```
var bayes = ss.bayesian();
bayes.train({ species: 'Cat' }, 'animal');
bayes.score({ species: 'Cat' });
// { animal: 1 }
```