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    @marisnb/m-stats

    1.0.4 • Public • Published

    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.

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    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

    Install

    npm i @marisnb/m-stats

    DownloadsWeekly Downloads

    5

    Version

    1.0.4

    License

    MIT

    Unpacked Size

    15 kB

    Total Files

    6

    Last publish

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