diff --git a/src/py/crankshaft/test/test_clustering_moran.py b/src/py/crankshaft/test/test_clustering_moran.py
index fbe11d6..29c5bde 100644
--- a/src/py/crankshaft/test/test_clustering_moran.py
+++ b/src/py/crankshaft/test/test_clustering_moran.py
@@ -52,7 +52,7 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1234)
-        result = cc.moran_local('table', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran_local('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
@@ -70,3 +70,14 @@ class MoranTest(unittest.TestCase):
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
             self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1235)
+        result = cc.moran('table', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        print 'result == None?', result == None
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)