diff --git a/Principal_component_analysis/demo/Principal_component_analysis/linear_least_squares_fitting.cpp b/Principal_component_analysis/demo/Principal_component_analysis/linear_least_squares_fitting.cpp
index c379ff118db..cecfcdce656 100644
--- a/Principal_component_analysis/demo/Principal_component_analysis/linear_least_squares_fitting.cpp
+++ b/Principal_component_analysis/demo/Principal_component_analysis/linear_least_squares_fitting.cpp
@@ -95,9 +95,10 @@ public:
     if(list_of_points.size()>1){
       Line_2 line;
       linear_least_squares_fitting_2(list_of_points.begin(),
-						                         list_of_points.end(),
-						                         line);
-
+				     list_of_points.end(),
+				     line,
+				     CGAL::PCA_dimension_0_tag());
+      
       *widget << line;
     }
     widget->unlock();
diff --git a/Principal_component_analysis/include/CGAL/centroid.h b/Principal_component_analysis/include/CGAL/centroid.h
index f9780352818..68194691bba 100644
--- a/Principal_component_analysis/include/CGAL/centroid.h
+++ b/Principal_component_analysis/include/CGAL/centroid.h
@@ -51,7 +51,7 @@ centroid(InputIterator begin,
          InputIterator end, 
          const K&,
          const typename K::Point_2*,
-	 const CGAL::PCA_dimension_0_tag& tag)
+	 const CGAL::PCA_dimension_0_tag&)
 {
   typedef typename K::Vector_2 Vector;
   typedef typename K::FT FT;
diff --git a/Principal_component_analysis/include/CGAL/linear_least_squares_fitting_segments_2.h b/Principal_component_analysis/include/CGAL/linear_least_squares_fitting_segments_2.h
index 6ca55dbf163..2d42ba56209 100644
--- a/Principal_component_analysis/include/CGAL/linear_least_squares_fitting_segments_2.h
+++ b/Principal_component_analysis/include/CGAL/linear_least_squares_fitting_segments_2.h
@@ -111,10 +111,6 @@ linear_least_squares_fitting_2(InputIterator first,
   covariance[0] += mass * (-1.0 * c.x() * c.x());
   covariance[1] += mass * (-1.0 * c.x() * c.y());
   covariance[2] += mass * (-1.0 * c.y() * c.y());
-  std::cout<<covariance[0]<<" "<<covariance[1]<<" "<<covariance[2]<<" "<<(std::sqrt(2)/3.0)<<std::endl;
-
-  // to remove later
-  //  std::cout<<covariance[0]<<" "<<covariance[1]<<" "<<covariance[2]<<" "<<(std::sqrt(2)/3.0)<<std::endl;
 
   // solve for eigenvalues and eigenvectors.
   // eigen values are sorted in descending order,