I added the option for residuals to be added to the prediction of the original points.

README.md

@@ -44,6 +44,7 @@ Below are the default values for the configuration parameter.
 {
   order: 2,
   precision: 2,
+  residuals: false
 }
 ```
 

dist/regression.js

@@ -57,14 +57,14 @@
   var DEFAULT_OPTIONS = { order: 2, precision: 2, period: null };
 
   /**
-  * Determine the coefficient of determination (r^2) of a fit from the observations
-  * and predictions.
-  *
-  * @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 one cannot be calculated.
-  */
+   * Determine the coefficient of determination (r^2) of a fit from the observations
+   * and predictions.
+   *
+   * @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 one cannot be calculated.
+   */
   function determinationCoefficient(data, results) {
     var predictions = [];
     var observations = [];
@@ -96,14 +96,14 @@
   }
 
   /**
-  * Determine the solution of a system of linear equations A * x = b using
-  * Gaussian elimination.
-  *
-  * @param {Array<Array<number>>} input - A 2-d matrix of data in row-major form [ A | b ]
-  * @param {number} order - How many degrees to solve for
-  *
-  * @return {Array<number>} - Vector of normalized solution coefficients matrix (x)
-  */
+   * Determine the solution of a system of linear equations A * x = b using
+   * Gaussian elimination.
+   *
+   * @param {Array<Array<number>>} input - A 2-d matrix of data in row-major form [ A | b ]
+   * @param {number} order - How many degrees to solve for
+   *
+   * @return {Array<number>} - Vector of normalized solution coefficients matrix (x)
+   */
   function gaussianElimination(input, order) {
     var matrix = input;
     var n = input.length - 1;
@@ -143,25 +143,51 @@
   }
 
   /**
-  * Round a number to a precision, specificed in number of decimal places
-  *
-  * @param {number} number - The number to round
-  * @param {number} precision - The number of decimal places to round to:
-  *                             > 0 means decimals, < 0 means powers of 10
-  *
-  *
-  * @return {numbr} - The number, rounded
-  */
+   * Round a number to a precision, specificed in number of decimal places
+   *
+   * @param {number} number - The number to round
+   * @param {number} precision - The number of decimal places to round to:
+   *                             > 0 means decimals, < 0 means powers of 10
+   *
+   *
+   * @return {numbr} - The number, rounded
+   */
   function round(number, precision) {
     var factor = Math.pow(10, precision);
     return Math.round(number * factor) / factor;
   }
 
   /**
-  * The set of all fitting methods
-  *
-  * @namespace
-  */
+   * Predicts the points used for function calculation based on the predct function generated.
+   * @param  {Array<Array<number>>} data - The original points input
+   * @param  {function(number)} predict - The prediction function generated
+   * @param  {bool} enableResiduals - Indicates whether residuals added to the response
+   *
+   * @return {predictedPoints} - Returns the predicted points.
+   */
+  function predictPoints(data, predict, enableResiduals) {
+    var points = [];
+    if (enableResiduals === true) {
+      points = data.map(function (point) {
+        var predictedCoordinates = predict(point[0]);
+        return {
+          coordinates: predictedCoordinates,
+          residuals: point[1] - predictedCoordinates[1] // Residual = Actual - Predicted
+        };
+      });
+    } else {
+      points = data.map(function (point) {
+        return predict(point[0]);
+      });
+    }
+    return points;
+  }
+
+  /**
+   * The set of all fitting methods
+   *
+   * @namespace
+   */
   var methods = {
     linear: function linear(data, options) {
       var sum = [0, 0, 0, 0, 0];
@@ -187,9 +213,7 @@
         return [round(x, options.precision), round(gradient * x + intercept, options.precision)];
       };
 
-      var points = data.map(function (point) {
-        return predict(point[0]);
-      });
+      var points = predictPoints(data, predict, options.residuals);
 
       return {
         points: points,
@@ -222,9 +246,7 @@
         return [round(x, options.precision), round(coeffA * Math.exp(coeffB * x), options.precision)];
       };
 
-      var points = data.map(function (point) {
-        return predict(point[0]);
-      });
+      var points = predictPoints(data, predict, options.residuals);
 
       return {
         points: points,
@@ -255,9 +277,7 @@
         return [round(x, options.precision), round(round(coeffA + coeffB * Math.log(x), options.precision), options.precision)];
       };
 
-      var points = data.map(function (point) {
-        return predict(point[0]);
-      });
+      var points = predictPoints(data, predict, options.residuals);
 
       return {
         points: points,
@@ -289,9 +309,7 @@
         return [round(x, options.precision), round(round(coeffA * Math.pow(x, coeffB), options.precision), options.precision)];
       };
 
-      var points = data.map(function (point) {
-        return predict(point[0]);
-      });
+      var points = predictPoints(data, predict, options.residuals);
 
       return {
         points: points,
@@ -343,9 +361,7 @@
         }, 0), options.precision)];
       };
 
-      var points = data.map(function (point) {
-        return predict(point[0]);
-      });
+      var points = predictPoints(data, predict, options.residuals);
 
       var string = 'y = ';
       for (var _i = coefficients.length - 1; _i >= 0; _i--) {