Mastering linear regression calculations using vue.js and chart.js

Take for instance, with the current dataset, I am looking to showcase a linear regression

I managed to do this in Vista and now I need guidance on how to proceed with the calculation. I am not too familiar with using the formula Here is my HTML:

<canvas id="orders_chart" width="200" height="200"></canvas>

This is my data:

type: 'line',
            data: {
            labels: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
            datasets: [
                {
                    label: 'Orders',
                    data: [1, 0, 1, 0, 2, 8, 9, 2, 0, 0, 0, 0, 1, 0, 0, 5, 1, 5, 5, 5, 0, 1, 5, 0, 0, 1, 3, 5, 9, 15],
                    backgroundColor: [
                        'rgba(255, 99, 132, 0.2)',
                        'rgba(54, 162, 235, 0.2)',
                        'rgba(255, 206, 86, 0.2)',
                        'rgba(75, 192, 192, 0.2)',
                        'rgba(153, 102, 255, 0.2)',
                        'rgba(255, 159, 64, 0.2)'
                    ],
                    borderColor: [
                        'rgba(255, 99, 132, 1)',
                        'rgba(54, 162, 235, 1)',
                        'rgba(255, 206, 86, 1)',
                        'rgba(75, 192, 192, 1)',
                        'rgba(153, 102, 255, 1)',
                        'rgba(255, 159, 64, 1)'
                    ],
                    borderWidth: 1
                }]
            },
            options: {
                scales: {
                    y: {
                        beginAtZero: true
                    }
                }
            }

Additionally, here are my functions:

mounted() {
        this.createChart()
    },

    methods: {
        createChart() {
                const ctx = document.getElementById('orders_chart');
                const myChart = new Chart(ctx, {
                    type: this.type,
                    data: this.data,
                    options: this.options,
            });
        }
    }

Now, what steps should I take next? I believe I need to simply show a line based on certain calculations, but I'm unsure of the exact process :)

javascriptvue.jslinear-regression

Answer №1

If you possess an array of data points, where the index in the array represents the X coordinate and the value of the array element signifies the Y coordinate, then you have the option to utilize the module below to apply various regression formulas on it. Subsequently, you can visualize the outcome in a similar manner as your original array of data points:

const DEFAULT_OPTIONS = { order: 2, precision: 2, period: null };

/**
* Calculate the coefficient of determination (r^2) for a fit from the observed values
* and predicted values.
*
* @param {Array<Array<number>>} data - Pairs of observed x-y values
* @param {Array<Array<number>>} results - Pairs of observed predicted x-y values
*
* @return {number} - The r^2 value or NaN if calculation is not possible
*/
function determinationCoefficient(data, results) {
// Code remains unchanged for brevity
}

/**
* Solve a system of linear equations A * x = b by using Gaussian elimination.
*
* @param {Array<Array<number>>} input - Data matrix [ A | b ]
* @param {number} order - Degrees to solve for
*
* @return {Array<number>} - Solution coefficients vector (x)
*/
function gaussianElimination(input, order) {
// Code remains unchanged for brevity
}

/**
* Round a number to a specified precision in terms of decimal places
*
* @param {number} number - Number to round
* @param {number} precision - Decimal places to round to:
*                             > 0 means decimals, < 0 means powers of 10
*
*
* @return {numbr} - Rounded number
*/
function round(number, precision) {
// Code remains unchanged for brevity
}

/**
* Collection of all fitting methods
*
* @namespace
*/
export default {
linear(data, options) {
// Code remains unchanged for brevity
},

exponential(data, options) {
// Code remains unchanged for brevity
},

logarithmic(data, options) {
// Code remains unchanged for brevity
},

power(data, options) {
// Code remains unchanged for brevity
},

polynomial(data, options) {
// Code remains unchanged for brevity
},
};

export { DEFAULT_OPTIONS };

export function predict(cost,reg_kind,reg_coef)
{
// Code remains unchanged for brevity
}

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