diff --git a/src/Numerics/Optimization/IObjectiveModel.cs b/src/Numerics/Optimization/IObjectiveModel.cs
index 2409dffe..ff117091 100644
--- a/src/Numerics/Optimization/IObjectiveModel.cs
+++ b/src/Numerics/Optimization/IObjectiveModel.cs
@@ -6,6 +6,16 @@ namespace MathNet.Numerics.Optimization
     {
         IObjectiveModel CreateNew();
 
+        /// <summary>
+        /// Get the y-values of the observations.
+        /// </summary>
+        Vector<double> ObservedY { get; }
+
+        /// <summary>
+        /// Get the values of the weights for the observations.
+        /// </summary>
+        Matrix<double> Weights { get; }
+
         /// <summary>
         /// Get the y-values of the fitted model that correspond to the independent values.
         /// </summary>
@@ -38,6 +48,11 @@ namespace MathNet.Numerics.Optimization
         /// </summary>
         Matrix<double> Covariance { get; }
 
+        /// <summary>
+        /// Get the correlation matrix.
+        /// </summary>
+        Matrix<double> Correlation { get; }
+
         /// <summary>
         /// Get the number of calls to function.
         /// </summary>
diff --git a/src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs b/src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs
index c5558f99..f1204189 100644
--- a/src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs
+++ b/src/Numerics/Optimization/ObjectiveModels/FittingObjectiveModel.cs
@@ -123,6 +123,11 @@ namespace MathNet.Numerics.Optimization.ObjectiveModels
         /// </summary>
         public Matrix<double> Covariance { get; private set; }
 
+        /// <summary>
+        /// Get the correlation matrix.
+        /// </summary>
+        public Matrix<double> Correlation { get; private set; }
+
         /// <summary>
         /// Get the number of calls to function.
         /// </summary>
@@ -484,18 +489,23 @@ namespace MathNet.Numerics.Optimization.ObjectiveModels
             EvaluateFunction(Pext);
             EvaluateJacobian(Pext);
 
+            // restore isBounded
+            this.IsBounded = (LowerBound != null || UpperBound != null);
+
             if (Hessian == null || Residuals == null || DegreeOfFreedom < 1)
             {
                 Covariance = null;
+                Correlation = null;
                 return;
             }
 
             var covariance = Hessian.PseudoInverse() * Residuals.DotProduct(Residuals) / DegreeOfFreedom;
-
             Covariance = covariance;
 
-            // restore isBounded
-            this.IsBounded = (LowerBound != null || UpperBound != null);
+            var correlation = covariance.Clone();
+            var d = correlation.Diagonal().PointwiseSqrt();
+            var dd = d.OuterProduct(d);
+            Correlation = correlation.PointwiseDivide(dd);
 
             return;
         }