update Correlation godoc and comments

as discussed

stat.go

@@ -126,7 +126,7 @@ func ChiSquare(obs, exp []float64) float64 {
 
 // Correlation returns the weighted correlation between the samples of x and y
 // with the given means.
-//  sum_i {w_i (x_i - meanX) * (y_i - meanY)} / ((sum_j {w_j} - 1) * stdX * stdY)
+//  sum_i {w_i (x_i - meanX) * (y_i - meanY)} / (stdX * stdY)
 // The lengths of x and y must be equal. If weights is nil then all of the
 // weights are 1. If weights is not nil, then len(x) must equal len(weights).
 func Correlation(x, y, weights []float64) float64 {
@@ -163,8 +163,6 @@ func Correlation(x, y, weights []float64) float64 {
 		sxx -= xcompensation * xcompensation / float64(len(x))
 		syy -= ycompensation * ycompensation / float64(len(x))
 
-		// sum_i { (x_i - meanX) * (y_i - meanY)} / sqrt{\sum_i{ (x_i - \mu_x)^2 } * \sum_i{ ( y_i - \mu_y)^2}}
-		// with the implicit 1/(N-1) cancelling out.
 		return (sxy - xcompensation*ycompensation/float64(len(x))) / math.Sqrt(sxx*syy)
 
 	}
@@ -191,8 +189,6 @@ func Correlation(x, y, weights []float64) float64 {
 	sxx -= xcompensation * xcompensation / sumWeights
 	syy -= ycompensation * ycompensation / sumWeights
 
-	// sum_i {w_i * (x_i - meanX) * (y_i - meanY)} / sqrt{\sum_i{w_i * (x_i - \mu_x)^2 } * \sum_i{w_i *( y_i - \mu_y)^2}}
-	// with sum_i { w_i } cancelling out.
 	return (sxy - xcompensation*ycompensation/sumWeights) / math.Sqrt(sxx*syy)
 }