diff --git a/statistics/statistics.ipynb b/statistics/statistics.ipynb
index 89c25c9..8cc88ce 100644
--- a/statistics/statistics.ipynb
+++ b/statistics/statistics.ipynb
@@ -477,7 +477,18 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=3.2058134286377942, pvalue=0.0013489533484183165)"
+      ]
+     },
+     "execution_count": 21,
+     "output_type": "execute_result",
+     "metadata": {}
+    }
+   ],
    "source": [
     "ttest_ind(train_n_uterrances, test_n_uterrances, equal_var=False)   # Welch's test"
    ]
@@ -541,7 +552,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb0702e7150>"
+      "<matplotlib.figure.Figure at 0x7f0212593050>"
      ],
      "metadata": {}
     },
@@ -551,7 +562,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb0702d99d0>"
+      "<matplotlib.figure.Figure at 0x7f0212593110>"
      ],
      "metadata": {}
     },
@@ -561,7 +572,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb06f8c6ed0>"
+      "<matplotlib.figure.Figure at 0x7f020f10ba90>"
      ],
      "metadata": {}
     }
@@ -679,7 +690,7 @@
     {
      "data": {
       "text/plain": [
-       "3.9496660884359045"
+       "3.954151"
       ]
      },
      "execution_count": 30,
@@ -699,7 +710,7 @@
     {
      "data": {
       "text/plain": [
-       "2.9716330743048616"
+       "2.9767288874197129"
       ]
      },
      "execution_count": 31,
@@ -903,7 +914,7 @@
     {
      "data": {
       "text/plain": [
-       "0.0064530727624892434"
+       "0.003483564483629084"
       ]
      },
      "execution_count": 40,
@@ -925,7 +936,7 @@
     {
      "data": {
       "text/plain": [
-       "8.1934384478995769e-07"
+       "2.3723808813689662e-07"
       ]
      },
      "execution_count": 41,
@@ -946,11 +957,11 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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KyzLgqEl7cBER0ciU9WgkzQTeBtxahj4k6U5JiyQNlLE9gbVdq62lU5jGjq9jS8HaC3gA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\nRK5CYII=\n"
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8tNxyYK8m7StSO+nvYwARsQ54VtKum9hW9t70ljd1OkJTvb29nY7wKs5UjTNV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\n"
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb06f6b68d0>"
+      "<matplotlib.figure.Figure at 0x7f020f1a8a90>"
      ],
      "metadata": {}
     },
@@ -960,7 +971,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb0703d5c50>"
+      "<matplotlib.figure.Figure at 0x7f02120e4510>"
      ],
      "metadata": {}
     },
@@ -970,7 +981,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb072e45d50>"
+      "<matplotlib.figure.Figure at 0x7f020e625e50>"
      ],
      "metadata": {}
     }
@@ -1009,7 +1020,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb06ff62390>"
+      "<matplotlib.figure.Figure at 0x7f0212030490>"
      ],
      "metadata": {}
     },
@@ -1019,7 +1030,7 @@
      },
      "output_type": "display_data",
      "text": [
-      "<matplotlib.figure.Figure at 0x7fb06f782590>"
+      "<matplotlib.figure.Figure at 0x7f0212021e90>"
      ],
      "metadata": {}
     }
@@ -1114,4 +1125,4 @@
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file