diff --git a/Exercise sheet 1/exercise_sheet_01.ipynb b/Exercise sheet 1/exercise_sheet_01.ipynb
index d98c688..f3f0c9d 100644
--- a/Exercise sheet 1/exercise_sheet_01.ipynb	
+++ b/Exercise sheet 1/exercise_sheet_01.ipynb	
@@ -537,13 +537,17 @@
     "    return np.pi**(d/2)/scipy.special.gamma(d/2 + 1)\n",
     "\n",
     "N = 10000\n",
-    "for d in range(1, 8):\n",
-    "    print(\"N = {}\".format(N),\n",
-    "          \"\\td = {}\".format(d),\n",
-    "          \"\\tV_d = {:.3f}\".format(volume_d_ball(d)),\n",
-    "          \"\\tdirect-sampled V_d = {:5.3f}\".format(number_of_hits_in_d_dimensions(N, d)/N*2**d)\n",
-    "         )\n",
-    "# TODO: Plot the results as is asked."
+    "d_range = np.array(range(1, 8))\n",
+    "V_d_mc = [number_of_hits_in_d_dimensions(N, d)/N*2**d for d in d_range]\n",
+    "\n",
+    "## Plotting\n",
+    "plt.figure()\n",
+    "plt.plot(d_range, V_d_mc, label=\"direct-sampling\")\n",
+    "plt.plot(d_range, volume_d_ball(d_range), label=\"exact\")\n",
+    "plt.xlabel(\"dimension $d$\")\n",
+    "plt.ylabel(\"volume $V_d$\")\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -572,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -592,37 +596,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N = 2000 \tdelta = 0.000 \tpi_estimate = 4.000\n",
-      "N = 2000 \tdelta = 0.333 \tpi_estimate = 3.149\n",
-      "N = 2000 \tdelta = 0.667 \tpi_estimate = 3.139\n",
-      "N = 2000 \tdelta = 1.000 \tpi_estimate = 3.135\n",
-      "N = 2000 \tdelta = 1.333 \tpi_estimate = 3.146\n",
-      "N = 2000 \tdelta = 1.667 \tpi_estimate = 3.150\n",
-      "N = 2000 \tdelta = 2.000 \tpi_estimate = 3.150\n",
-      "N = 2000 \tdelta = 2.333 \tpi_estimate = 3.141\n",
-      "N = 2000 \tdelta = 2.667 \tpi_estimate = 3.146\n",
-      "N = 2000 \tdelta = 3.000 \tpi_estimate = 3.146\n"
+      "N      delta    4*hits/trials   E[(4*hits/trials - pi)^2]\n",
+      "2000   0.0000   4.0000          0.737\n",
+      "2000   0.3333   3.1409          0.014\n",
+      "2000   0.6667   3.1351          0.005\n",
+      "2000   1.0000   3.1381          0.006\n",
+      "2000   1.3333   3.1406          0.004\n",
+      "2000   1.6667   3.1316          0.007\n",
+      "2000   2.0000   3.1377          0.007\n",
+      "2000   2.3333   3.1311          0.011\n",
+      "2000   2.6667   3.1547          0.011\n",
+      "2000   3.0000   3.1417          0.015\n"
      ]
     }
    ],
    "source": [
     "N = 2000 # number of trials\n",
     "m = 200  # number of repetitions\n",
+    "delta_range = np.linspace(0, 3, 10) # throwing ranges\n",
     "\n",
     "p0 = np.array([0., 0.]) # starting position in origin\n",
+    "hits = np.zeros((len(delta_range), m), dtype=int)\n",
     "\n",
-    "for delta in np.linspace(0, 3, 10):\n",
-    "    pi_estimate = 0\n",
+    "for index_delta, delta in enumerate(delta_range):\n",
     "    for i in range(m):\n",
-    "        hits = markov_pebble(p0, delta, N)\n",
-    "        pi_estimate += hits/N*4\n",
-    "    pi_estimate /= m\n",
-    "    print(\"N = {}\".format(N),\n",
-    "          \"\\tdelta = {:.3f}\".format(delta),\n",
-    "          \"\\tpi_estimate = {:.3f}\".format(pi_estimate)\n",
-    "         )\n",
+    "        hits[index_delta][i] = markov_pebble(p0, delta, N)\n",
     "\n",
-    "# TODO: Investigate the rejection rate and the mean square deviation. Maybe only the latter, as the next question concerns the former."
+    "print(\"{:4}   {:6}   {:8}   {:<10}\".format('N','delta','4*hits/trials','E[(4*hits/trials - pi)^2]'))\n",
+    "for index_delta, delta in enumerate(delta_range):\n",
+    "    pi_estimates = hits[index_delta]/N*4\n",
+    "    print(\"{:4}   {:1.4f}   {:1.4f}          {:1.3f}\"\n",
+    "          .format(N, delta, np.mean(pi_estimates), np.mean((pi_estimates - np.pi)**2)))"
    ]
   },
   {