diff --git a/pyerrors/fits.py b/pyerrors/fits.py
index 44ce8ae4..f5f0850e 100644
--- a/pyerrors/fits.py
+++ b/pyerrors/fits.py
@@ -703,12 +703,8 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
         jacobian = auto_jacobian
         hessian = auto_hessian
 
-    x_all = []
-    y_all = []
-    for key in x.keys():
-        y_all += y[key]
-
     x_all = np.concatenate([np.array(o) for o in x.values()])
+    y_all = np.concatenate([np.array(o) for o in y.values()])
 
     if len(x_all.shape) > 2:
         raise Exception('Unknown format for x values')
diff --git a/tests/fits_test.py b/tests/fits_test.py
index d275a848..9cf7b23a 100644
--- a/tests/fits_test.py
+++ b/tests/fits_test.py
@@ -610,18 +610,19 @@ def test_ks_test():
 
 def test_combined_fit_list_v_array():
     res = []
-    y_test = {'a': [pe.Obs([np.random.normal(i, 0.5, 1000)], ['ensemble1']) for i in range(1, 7)]}
-    for x_test in [{'a': [0, 1, 2, 3, 4, 5]}, {'a': np.arange(6)}]:
-        for key in y_test.keys():
-            [item.gamma_method() for item in y_test[key]]
-        def func_a(a, x):
-            return a[1] * x + a[0]
+    for y_test in [{'a': [pe.Obs([np.random.normal(i, 0.5, 1000)], ['ensemble1']) for i in range(1, 7)]},
+               {'a': np.array([pe.Obs([np.random.normal(i, 0.5, 1000)], ['ensemble1']) for i in range(1, 7)])}]:
+        for x_test in [{'a': [0, 1, 2, 3, 4, 5]}, {'a': np.arange(6)}]:
+            for key in y_test.keys():
+                [item.gamma_method() for item in y_test[key]]
+            def func_a(a, x):
+                return a[1] * x + a[0]
 
-        funcs_test = {"a": func_a}
-        res.append(pe.fits.least_squares(x_test, y_test, funcs_test))
+            funcs_test = {"a": func_a}
+            res.append(pe.fits.least_squares(x_test, y_test, funcs_test))
 
-    assert (res[0][0] - res[1][0]).is_zero(atol=1e-8)
-    assert (res[0][1] - res[1][1]).is_zero(atol=1e-8)
+        assert (res[0][0] - res[1][0]).is_zero(atol=1e-8)
+        assert (res[0][1] - res[1][1]).is_zero(atol=1e-8)
 
 
 def fit_general(x, y, func, silent=False, **kwargs):