m-stats
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In other words, it is a mathematical discipline to collect, summarize data.
This module provides functions for statistical data analysis.
Getting Started
Installation
npm install @marisnb/m-stats --save
How to use
Integration
const stats = require('@marisnb/m-stats');
API Documentation
stats.min(data)
Returns the min value in a given data.
stats.min([]) === 0
stats.min([-1]) === -1
stats.min([-1, 3, 5, -1]) === -1
stats.min([-1, 3, 5, 7, 5, 5, -2]) === -2
stats.min([0, 7, 3, 5, 4, 4, 4, 3, 32]) === 0
stats.max(data)
Returns the max value in a given data.
stats.max([]) === 0
stats.max([-1]) === -1
stats.max([-1, 3, 5, -1]) === 5
stats.max([-1, 3, 5, 7, 5, 5, -2]) === 7
stats.max([0, 7, 3, 5, 4, 4, 4, 3, 32]) === 32
stats.sum(data)
Sum of all values
stats.sum([]) === 0
stats.sum([-1]) === -1
stats.sum([-1, 3, 5, -1]) === 6
stats.sum([-1, 3, 5, 7, 5, 5, 7]) === 31
stats.sum([-1, 7, 3, 5, 4, 4, 4, 3, -1]) === 28
stats.avg(data)
Returns the avg value in a given data.
stats.avg([]) === NaN
stats.avg([-1]) === -1
stats.avg([-1, 3, 5, -1]) === 1.5
stats.avg([-1, 3, 5, 7, 5, 5, -2]) === 3.14
stats.avg([0, 7, 3, 5, 4, 4, 4, 3, 32]) === 6.89
stats.mode(data)
Mode is the most common value among the given observations. For example, a person who sells ice creams might want to know which flavour is the most popular.
stats.mode([]) === NaN
stats.mode([-1]) === -1
stats.mode([-1, 3, 5, -1]) === -1
stats.mode([-1, 3, 5, 7, 5, 5, 7]) === 5
stats.mode([-1, 7, 3, 5, 4, 4, 4, 3, -1, 3]) === 3
stats.range(data)
The range of a set of data is the difference between the highest and lowest values in the set. For example, Cheryl took 7 math tests in one marking period. What is the range of her test scores?
stats.range([]) === NaN
stats.range([-1]) === -1
stats.range([-1, 3, 5, -2]) === 7
stats.range([-1, 3, 5, 7, 5, 5, -7]) === 14
stats.range([-1, 7, 3, 5, 4, 4, 4, 3, -1, 3]) === 8
stats.mean(data)
Mean is the average of all the values. For example, a teacher may want to know the average marks of a test in his class.
stats.mean([]) === NaN
stats.mean([-1]) === -1
stats.mean([-1, 2, 3, 4, 4]) === 2.4
stats.mean([-1, 2.5, 3.25, 5.75]) === 2.625
stats.median(data)
Median is the middle value, dividing the number of data into 2 halves. In other words, 50% of the observations is below the median and 50% of the observations is above the median.
stats.median([]) === NaN
stats.median([-1]) === -1
stats.median([-1, 3, 5]) === 3
stats.median([-1, 3, 5, 7]) === 4
stats.median([-1, 7, 3, 5, 4]) === 4
stats.variance(data)
variance is the expectation of the squared deviation of a random variable from its mean.
stats.variance([]) === NaN
stats.variance([7]) === 0
stats.variance([1, 2, 4, 5, 7, 11]) === 11
stats.variance([3, 21, 98, 203, 17, 9]) === 5183.25
stats.variance([3, 4, 4, 5, 6, 8]) === 2.67
stats.standardDeviation(data)
the standard deviation is a measure of the amount of variation or dispersion of a set of values.
stats.standardDeviation([]) === NaN
stats.standardDeviation([7]) === 0
stats.standardDeviation([1, 2, 4, 5, 7, 11]) === 3.32
stats.standardDeviation([3, 21, 98, 203, 17, 9]) === 71.99
stats.standardDeviation([3, 4, 4, 5, 6, 8]) === 1.63
stats.harmonicMean(data)
the harmonic mean is one of several kinds of average, and in particular one of the Pythagorean means.
stats.harmonicMean([]) === NaN
stats.harmonicMean([7]) === 7
stats.harmonicMean([1, 2, 4]) === 1.71
stats.harmonicMean([-1, 3, 5, 7, 5, 5, -2]) === -16.52
stats.harmonicMean([600, 470, 430, 300, 170]) === 326.04
Running Tests
To run the test suite first install the development dependencies:
npm install
then run the tests:
npm test