diff --git a/scikits/learn/em/examples/utils.py b/scikits/learn/em/examples/utils.py
index 84966554ef1121895da4d7845621503537a06bf0..89f11cf19030a8e7b39cc59d2b8869b7e3e87e88 100644
--- a/scikits/learn/em/examples/utils.py
+++ b/scikits/learn/em/examples/utils.py
@@ -5,7 +5,7 @@
 
 import numpy as N
 
-from scikits.learn.datasets import oldfaithful, pendigits, iris
+from scikits.learn.datasets import oldfaithful, pendigits
 
 def get_faithful():
     """Return faithful data as a nx2 array, first column being duration, second
diff --git a/scikits/learn/feature_selection/tests/test_feature_select.py b/scikits/learn/feature_selection/tests/test_feature_select.py
index 3f2ab5401f2130caefa1a28667953ceb890ac73a..8ff6fe88d66c81c12799fb6076b77bad76dd7128 100644
--- a/scikits/learn/feature_selection/tests/test_feature_select.py
+++ b/scikits/learn/feature_selection/tests/test_feature_select.py
@@ -23,21 +23,24 @@ def make_dataset(n_samples=50, n_features=20, k=5, seed=None, classif=True,
         
     return x, y
 
-def test_compare_with_statsmodels():
-    """
-    Test whether the F test yields the same results as scikits.statmodels
-    """
-    x, y = make_dataset(classif=False)
-    F, pv = fs.f_regression(x, y)
+# 
+# this test is commented because it depends on scikits.statsmodels
+# 
+# def test_compare_with_statsmodels():
+#     """
+#     Test whether the F test yields the same results as scikits.statmodels
+#     """
+#     x, y = make_dataset(classif=False)
+#     F, pv = fs.f_regression(x, y)
 
-    import scikits.statsmodels as sm
-    nsubj = y.shape[0]
-    nfeature = x.shape[1]
-    q = np.zeros(nfeature)
-    contrast = np.array([1,0])
-    for i in range(nfeature):
-        q[i] = sm.OLS(x[:,i], np.vstack((y,np.ones(nsubj))).T).fit().f_test(contrast).pvalue 
-    assert_array_almost_equal(pv,q,1.e-6)
+#     import scikits.statsmodels as sm
+#     nsubj = y.shape[0]
+#     nfeature = x.shape[1]
+#     q = np.zeros(nfeature)
+#     contrast = np.array([1,0])
+#     for i in range(nfeature):
+#         q[i] = sm.OLS(x[:,i], np.vstack((y,np.ones(nsubj))).T).fit().f_test(contrast).pvalue 
+#     assert_array_almost_equal(pv,q,1.e-6)
 
     
 def test_F_test_classif():