code: function coefficientOfCorrelation(actuals = [], estimates = []) {
                if (actuals.length !== estimates.length) throw new RangeError('arrays must have the same length');
                const sumX = sum(actuals);
                const sumY = sum(estimates);
                const sumProdXY = actuals.reduce((result, val, index) => { 
                  result = result + (actuals[ index ] * estimates[ index ]);
                  return result;
                }, 0);
                const sumXSquared = actuals.reduce((result, val) => { 
                  result = result + (val * val);
                  return result;
                }, 0);
                const sumYSquared = estimates.reduce((result, val) => { 
                  result = result + (val * val);
                  return result;
                }, 0);
                const N = actuals.length;
                const R = (
                  (N * sumProdXY - sumX * sumY) /
                  Math.sqrt(
                    (N * sumXSquared - Math.pow(sumX, 2)) * (N * sumYSquared - Math.pow(sumY, 2))
                  )
                );
                return R;
              }
docs: The coefficent of Correlation is given by R decides how well the given data fits a line or a curve.
  @example
  const actuals = [ 39, 42, 67, 76, ];
  const estimates = [ 44, 40, 60, 84, ];
  const R = ms.util.coefficientOfCorrelation(actuals, estimates);
  R.toFixed(4) // => 0.9408
  @memberOf util
  @see {@link https://calculator.tutorvista.com/r-squared-calculator.html}
  @param {Number[]} actuals - numerical samples
  @param {Number[]} estimates - estimates values
  @returns {Number} R