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

License

MIT