diff --git a/notebooks/Linear Regression/LR_5_3.ipynb b/notebooks/Linear Regression/LR_5_3.ipynb
index 0622c7ed21bf0ce150b44cd310aa8b3daceb160f..265aaf73fa1f229613f00bc56c7218e681f3f948 100644
--- a/notebooks/Linear Regression/LR_5_3.ipynb	
+++ b/notebooks/Linear Regression/LR_5_3.ipynb	
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "c702d302-4af9-490b-9493-ff6ecdafb1e5",
    "metadata": {
     "tags": []
@@ -114,7 +114,7 @@
        "4   19.931034   20.000000  68.009922"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "id": "ef334483-cdb2-479e-89b8-a3d82e0b0eea",
    "metadata": {
     "tags": []
@@ -171,7 +171,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 53 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 6 seconds.\n"
      ]
     }
    ],
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "b42e161d-ec93-4266-80a1-a0d6541fd289",
    "metadata": {
     "tags": []
@@ -288,7 +288,7 @@
        "sigma         3268.0    1.0  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "id": "607b0af5-46aa-42f2-8bbd-86830933a853",
    "metadata": {
     "tags": []
@@ -309,44 +309,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Default computed for conditional variable: Erfahrung, Ausbildung\n"
+      "/opt/conda/lib/python3.10/site-packages/arviz/rcparams.py:368: FutureWarning: stats.hdi_prob is deprecated since 0.18.0, use stats.ci_prob instead\n",
+      "  warnings.warn(\n",
+      "Default computed for conditional variable: Erfahrung\n",
+      "Default computed for unspecified variable: Ausbildung\n"
      ]
     },
     {
-     "ename": "KeyError",
-     "evalue": "'Radio'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Radio'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbmb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterpret\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_th\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midata_th\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mErfahrung\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAusbildung\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                               \u001b[49m\u001b[43msubplot_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgroup\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpanel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRadio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mfig_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharey\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m;\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../fig/bambi_linear_werbung_th_mean.png\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bambi/interpret/plotting.py:214\u001b[0m, in \u001b[0;36mplot_predictions\u001b[0;34m(model, idata, conditional, average_by, target, sample_new_groups, pps, use_hdi, prob, transforms, legend, ax, fig_kwargs, subplot_kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    213\u001b[0m     fig_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m fig_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m fig_kwargs\n\u001b[0;32m--> 214\u001b[0m     panels_n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(np\u001b[38;5;241m.\u001b[39munique(\u001b[43mcap_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcovariates\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpanel\u001b[49m\u001b[43m]\u001b[49m)) \u001b[38;5;28;01mif\u001b[39;00m covariates\u001b[38;5;241m.\u001b[39mpanel \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    215\u001b[0m     rows, cols \u001b[38;5;241m=\u001b[39m default_grid(panels_n)\n\u001b[1;32m    216\u001b[0m     fig, axes \u001b[38;5;241m=\u001b[39m create_axes_grid(panels_n, rows, cols, backend_kwargs\u001b[38;5;241m=\u001b[39mfig_kwargs)\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'Radio'"
-     ]
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>,\n",
+       " array([<Axes: xlabel='Erfahrung', ylabel='Einkommen'>], dtype=object))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "bmb.interpret.plot_predictions(model_th, idata_th, [\"Erfahrung\", \"Ausbildung\"],\n",
-    "                               subplot_kwargs={\"group\":None, \"panel\":\"Radio\"},\n",
-    "                               legend=False,\n",
-    "                               fig_kwargs={\"sharey\":True, \"sharex\":True});\n",
-    "plt.savefig(\"../fig/bambi_linear_werbung_th_mean.png\")"
+    "bmb.interpret.plot_predictions(model_th, idata_th, \"Erfahrung\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c431fa39-7eae-485d-bf6d-a440aa25cc36",
+   "id": "349b9c5c-03a7-4c9e-91bd-dd04c2ad4604",
    "metadata": {},
    "outputs": [],
    "source": []