@targos/ttest

#ttest

> Perform the Student t hypothesis test

Installation

``sheel
npm install ttest
`

Example

`javascript
var ttest = require('ttest');

// One sample t-test
ttest([0,1,1,1], {mu: 1}).valid() // true

// Two sample t-test
ttest([0,1,1,1], [1,2,2,2], {mu: -1}).valid() // true
`

Documentation

`javascript
var ttest = require('ttest');
`

The ttest module supports both one and two sample t-testing, and both
equal and none equal variance.

If one array of data is given its a one sample t-test, and if two data arrays
are given its a two sample t-test.

ttest() supports data in the following format:

* an array of values, e.g. ttest([1, 2, 3])
* a Summary object,
e.g. ttest(new Summary([1, 2, 3]))
* an object with the following properties: mean, variance,
size, e.g. ttest({mean: 123, variance: 1, size: 42})

In all cases you can also pass an extra optional object, there takes the
following properties:

`javascript
const options = {
// Default: 0
// One sample case: this is the µ that the mean will be compared with.
// Two sample case: this is the ∂ value that the mean diffrence will be compared with.
mu: Number,

// Default: false
// If true don't assume variance is equal and use the Welch approximation.
// This only applies of two samples are used.
varEqual: Boolean,

// Default: 0.05
// The significance level of the test
alpha: Number,

// Default "not equal"
// What should the alternative hypothesis be:
// - One sample case: could the mean be less, greater or not equal to mu property.
// - Two sample case: could the mean diffrence be less, greater or not equal to mu property.
alternative: "less" || "greater" || "not equal"
};
`

The t-test object is finally created by calling the ttest constructor.

`javascript
const stat = ttest(sample, options);
const stat = ttest(sampleA, sampleB, options);
`

When the ttest object is created you can get the following information.

##### stat.testValue()

Returns the t value also called the statistic value.

##### stat.pValue()

Returns the p-value.

##### stat.confidence()

Returns an array containing the confidence interval, where the confidence level
is calculated as 1 - options.alpha. Where the lower limit has index 0 and
the upper limit has index 1. If the alternative hypothesis is less or
greater one of the sides will be +/- Infinity.

##### stat.valid()

Simply returns true if the p-value is greater or equal to the alpha` value.

##### stat.freedom()

Returns the degrees of freedom used in the t-test.