diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index c87bd57b..45f99d7e 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -334,16 +334,16 @@ def robot_wireless(optim_iters=100):
 
     # optimize
     m.optimize(messages=True, max_f_eval=optim_iters)
-    Ypredict = m.predict(data['Y'])[0]
+    Xpredict = m.predict(data['Ytest'])[0]
     pb.plot(data['Xtest'][:, 0], data['Xtest'][:, 1], 'r-')
-    pb.plot(Ypredict[:, 0], Ypredict[:, 1], 'b-')
+    pb.plot(Xpredict[:, 0], Xpredict[:, 1], 'b-')
     pb.axis('equal')
     pb.title('WiFi Localization with Gaussian Processes')
     pb.legend(('True Location', 'Predicted Location'))
 
-    sse = ((data['Ytest'] - Y.predict)**2).sum()
+    sse = ((data['Xtest'] - Xpredict)**2).sum()
     print(m)
-    print('Sum of squares error on test data: ', str(sse))
+    print('Sum of squares error on test data: ' + str(sse))
     return m
 
 def sparse_GP_regression_1D(N=400, num_inducing=5, optim_iters=100):
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index cef9a2a9..ff5474a6 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -404,7 +404,7 @@ def robot_wireless(data_set='robot_wireless'):
     Y = allY[0:215, :]
 
     Xtest = allX[215:, :]
-    Ytest = allX[215:, :]
+    Ytest = allY[215:, :]
     return data_details_return({'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'addresses' : addresses, 'times' : times}, data_set)
 
 def silhouette(data_set='ankur_pose_data'):