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/** * [Simple linear regression](http://en.wikipedia.org/wiki/Simple_linear_regression) * is a simple way to find a fitted line * between a set of coordinates. This algorithm finds the slope and y-intercept of a regression line * using the least sum of squares. * * @param {Array<Array<number>>} data an array of two-element of arrays, * like `[[0, 1], [2, 3]]` * @returns {Object} object containing slope and intersect of regression line * @example * linearRegression([[0, 0], [1, 1]]); // => { m: 1, b: 0 } */function linearRegression(data) {    let m, b;
    // Store data length in a local variable to reduce    // repeated object property lookups    const dataLength = data.length;
    //if there's only one point, arbitrarily choose a slope of 0    //and a y-intercept of whatever the y of the initial point is    if (dataLength === 1) {        m = 0;        b = data[0][1];    } else {        // Initialize our sums and scope the `m` and `b`        // variables that define the line.        let sumX = 0,            sumY = 0,            sumXX = 0,            sumXY = 0;
        // Use local variables to grab point values        // with minimal object property lookups        let point, x, y;
        // Gather the sum of all x values, the sum of all        // y values, and the sum of x^2 and (x*y) for each        // value.        //        // In math notation, these would be SS_x, SS_y, SS_xx, and SS_xy        for (let i = 0; i < dataLength; i++) {            point = data[i];            x = point[0];            y = point[1];
            sumX += x;            sumY += y;
            sumXX += x * x;            sumXY += x * y;        }
        // `m` is the slope of the regression line        m =            (dataLength * sumXY - sumX * sumY) /            (dataLength * sumXX - sumX * sumX);
        // `b` is the y-intercept of the line.        b = sumY / dataLength - (m * sumX) / dataLength;    }
    // Return both values as an object.    return {        m: m,        b: b    };}
export default linearRegression;