    function linear_regression() {
        var linreg = {},
            data = [];

        // Assign data to the model. Data is assumed to be an array.
        linreg.data = function(x) {
            if (!arguments.length) return data;
            data = x.slice();
            return linreg;
        };

        // Calculate the slope and y-intercept of the regression line
        // by calculating the least sum of squares
        linreg.mb = function() {
            var m, b;

            // Store data length in a local variable to reduce
            // repeated object property lookups
            var data_length = 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 (data_length === 1) {
                m = 0;
                b = data[0][1];
            } else {
                // Initialize our sums and scope the `m` and `b`
                // variables that define the line.
                var sum_x = 0, sum_y = 0,
                    sum_xx = 0, sum_xy = 0;

                // Use local variables to grab point values
                // with minimal object property lookups
                var 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 (var i = 0; i < data_length; i++) {
                    point = data[i];
                    x = point[0];
                    y = point[1];

                    sum_x += x;
                    sum_y += y;

                    sum_xx += x * x;
                    sum_xy += x * y;
                }

                // `m` is the slope of the regression line
                m = ((data_length * sum_xy) - (sum_x * sum_y)) /
                    ((data_length * sum_xx) - (sum_x * sum_x));

                // `b` is the y-intercept of the line.
                b = (sum_y / data_length) - ((m * sum_x) / data_length);
            }

            // Return both values as an object.
            return { m: m, b: b };
        };

        // a shortcut for simply getting the slope of the regression line
        linreg.m = function() {
            return linreg.mb().m;
        };

        // a shortcut for simply getting the y-intercept of the regression
        // line.
        linreg.b = function() {
            return linreg.mb().b;
        };

        // ## Fitting The Regression Line
        //
        // This is called after `.data()` and returns the
        // equation `y = f(x)` which gives the position
        // of the regression line at each point in `x`.
        linreg.line = function() {

            // Get the slope, `m`, and y-intercept, `b`, of the line.
            var mb = linreg.mb(),
                m = mb.m,
                b = mb.b;

            // Return a function that computes a `y` value for each
            // x value it is given, based on the values of `b` and `a`
            // that we just computed.
            return function(x) {
                return b + (m * x);
            };
        };

        return linreg;
    }