diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala
index 3ddc507a2e601..57f4eec312cb7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala
@@ -49,7 +49,7 @@ private class Aggregator(
     val deltaMean = currMean
     var i = 0
     while(i < currM2n.size) {
-      currM2n(i) -= deltaMean(i) * deltaMean(i) * nnz(i) * (nnz(i)-totalCnt) / totalCnt
+      currM2n(i) += deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt-nnz(i)) / totalCnt
       currM2n(i) /= totalCnt
       i += 1
     }
@@ -61,7 +61,7 @@ private class Aggregator(
   override lazy val numNonZeros: Vector = Vectors.fromBreeze(nnz)
 
   override lazy val max: Vector = {
-    nnz.activeIterator.foreach {
+    nnz.iterator.foreach {
       case (id, count) =>
         if ((count == 0.0) || ((count < totalCnt) && (currMax(id) < 0.0)))  currMax(id) = 0.0
     }
@@ -69,7 +69,7 @@ private class Aggregator(
   }
 
   override lazy val min: Vector = {
-    nnz.activeIterator.foreach {
+    nnz.iterator.foreach {
       case (id, count) =>
         if ((count == 0.0) || ((count < totalCnt) && (currMin(id) > 0.0))) currMin(id) = 0.0
     }
@@ -88,7 +88,7 @@ private class Aggregator(
         if (currMin(id) > value) currMin(id) = value
 
         val tmpPrevMean = currMean(id)
-        currMean(id) = (currMean(id) * totalCnt + value) / (totalCnt + 1.0)
+        currMean(id) = (currMean(id) * nnz(id) + value) / (nnz(id) + 1.0)
         currM2n(id) += (value - currMean(id)) * (value - tmpPrevMean)
 
         nnz(id) += 1.0
@@ -114,11 +114,14 @@ private class Aggregator(
           (currMean(id) * nnz(id) + other.currMean(id) * other.nnz(id)) / (nnz(id) + other.nnz(id))
     }
 
-    other.currM2n.activeIterator.foreach {
-      case (id, 0.0) =>
-      case (id, value) =>
-        currM2n(id) +=
-          value + deltaMean(id) * deltaMean(id) * nnz(id) * other.nnz(id) / (nnz(id)+other.nnz(id))
+    var i = 0
+    while(i < currM2n.size) {
+      (nnz(i), other.nnz(i)) match {
+        case (0.0, 0.0) =>
+        case _ => currM2n(i) +=
+          other.currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * other.nnz(i) / (nnz(i)+other.nnz(i))
+      }
+      i += 1
     }
 
     other.currMax.activeIterator.foreach {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala
index 5eb9d8e2c3da8..b621bf79b6e8b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala
@@ -38,54 +38,59 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
     Vectors.dense(7.0, 8.0, 9.0)
   )
 
-  val sparseData = ArrayBuffer(Vectors.sparse(20, Seq((0, 1.0), (9, 2.0), (10, 7.0))))
-  for (i <- 0 until 100) sparseData += Vectors.sparse(20, Seq((9, 0.0)))
-  sparseData += Vectors.sparse(20, Seq((0, 5.0), (9, 13.0), (16, 2.0)))
-  sparseData += Vectors.sparse(20, Seq((3, 5.0), (9, 13.0), (18, 2.0)))
+  val sparseData = ArrayBuffer(Vectors.sparse(3, Seq((0, 1.0))))
+  for (i <- 0 until 97) sparseData += Vectors.sparse(3, Seq((2, 0.0)))
+  sparseData += Vectors.sparse(3, Seq((0, 5.0)))
+  sparseData += Vectors.sparse(3, Seq((1, 5.0)))
 
-  test("full-statistics") {
+  test("dense statistical summary") {
     val data = sc.parallelize(localData, 2)
-    val (summary, denseTime) =
-      time(data.summarizeStatistics())
+    val summary = data.summarizeStatistics()
 
     assert(equivVector(summary.mean, Vectors.dense(4.0, 5.0, 6.0)),
-      "Column mean do not match.")
+      "Dense column mean do not match.")
 
     assert(equivVector(summary.variance, Vectors.dense(6.0, 6.0, 6.0)),
-      "Column variance do not match.")
+      "Dense column variance do not match.")
 
-    assert(summary.totalCount === 3, "Column cnt do not match.")
+    assert(summary.totalCount === 3, "Dense column cnt do not match.")
 
     assert(equivVector(summary.numNonZeros, Vectors.dense(3.0, 3.0, 3.0)),
-      "Column nnz do not match.")
+      "Dense column nnz do not match.")
 
     assert(equivVector(summary.max, Vectors.dense(7.0, 8.0, 9.0)),
-      "Column max do not match.")
+      "Dense column max do not match.")
 
     assert(equivVector(summary.min, Vectors.dense(1.0, 2.0, 3.0)),
-      "Column min do not match.")
+      "Dense column min do not match.")
+  }
 
+  test("sparse statistical summary") {
     val dataForSparse = sc.parallelize(sparseData.toSeq, 2)
-    val (_, sparseTime) = time(dataForSparse.summarizeStatistics())
+    val summary = dataForSparse.summarizeStatistics()
+
+    assert(equivVector(summary.mean, Vectors.dense(0.06, 0.05, 0.0)),
+      "Sparse column mean do not match.")
+
+    assert(equivVector(summary.variance, Vectors.dense(0.2564, 0.2475, 0.0)),
+      "Sparse column variance do not match.")
+
+    assert(summary.totalCount === 100, "Sparse column cnt do not match.")
+
+    assert(equivVector(summary.numNonZeros, Vectors.dense(2.0, 1.0, 0.0)),
+      "Sparse column nnz do not match.")
 
-    println(s"dense time is $denseTime, sparse time is $sparseTime.")
+    assert(equivVector(summary.max, Vectors.dense(5.0, 5.0, 0.0)),
+      "Sparse column max do not match.")
+
+    assert(equivVector(summary.min, Vectors.dense(0.0, 0.0, 0.0)),
+      "Sparse column min do not match.")
   }
 }
 
 object VectorRDDFunctionsSuite {
-  def time[R](block: => R): (R, Double) = {
-    val t0 = System.nanoTime()
-    val result = block
-    val t1 = System.nanoTime()
-    (result, (t1 - t0).toDouble / 1.0e9)
-  }
 
   def equivVector(lhs: Vector, rhs: Vector): Boolean = {
     (lhs.toBreeze - rhs.toBreeze).norm(2) < 1e-9
   }
-
-  def relativeTime(lhs: Double, rhs: Double): Boolean = {
-    val denominator = math.max(lhs, rhs)
-    math.abs(lhs - rhs) / denominator < 0.3
-  }
 }