From 2227bb70a32f9dfff99bf3d275db4d2affc23fa7 Mon Sep 17 00:00:00 2001
From: closes <22997252+closes@users.noreply.github.com>
Date: Fri, 17 Jan 2025 13:52:11 +0100
Subject: [PATCH] Added MSL exercise

---
 .../1_Simple_linear_regression.ipynb          | 532 +-----------------
 1 file changed, 17 insertions(+), 515 deletions(-)

diff --git a/practice/predictive_statistics/1_Simple_linear_regression.ipynb b/practice/predictive_statistics/1_Simple_linear_regression.ipynb
index e0a5868..697269d 100644
--- a/practice/predictive_statistics/1_Simple_linear_regression.ipynb
+++ b/practice/predictive_statistics/1_Simple_linear_regression.ipynb
@@ -22,7 +22,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "id": "2ilEHRZkNOl7"
       },
@@ -49,7 +49,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "id": "DU7xSRhsNOl8"
       },
@@ -65,37 +65,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
-        "id": "fSUdk84XNOl8",
-        "outputId": "cbff5e28-559a-4607-c645-4726af3b5468",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 479
-        }
+        "id": "fSUdk84XNOl8"
       },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0, 0.5, 'y')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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CFPccdD/XhKfp06dr+vTpF/XeTp066amnnopsgwAAFy1ab1wcCEjPP1+tOXNiVFMTw1WAHuGa8AQAcK/apQRmzQr1OEVTiJgxw1BsbJ569vyhrrqqXVR8Zq8jPAEALOHkpQQiLSXlrEaNMhyzYCfahvAEABEU6Uvz3caJSwkAZnlyqQIAcIJovDQfiAaEJwCIgOYuzWd9H8D9CE8AEAEtXZoPwN0ITwAQAbWX5tcXDZfmA9GA8AQAEVB7aX5sbOh5NF2aD3gdV9sBQIRE86X5gJcRngAggrg0H/Aehu0AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAPKyyUNm8OPQIID8ITAHhUbq7Us6c0ZkzoMTfX7hYB3kB4AgAPKiyUZs6UampCz2tqpFmz6IECwoHwBAAelJ9/PjjVqq6WDh70ReR8DA8imhCeAMCDMjKkmG/9Cx8bK6WlGWE/F8ODiDaEJwDwoEBAWrUqFJik0OPKlaHt4cTwIKJRO7sbAACIjOxsKStLOnBASk8PBadgMLznaG548MCB8Ac1wCkITwDgYYHAxYWYwsJQEMrIMBd6aocH6weo2NhQWAO8imE7AIhybZmzZNXwIOAkhCcAiGLhmLOUnS0VFISutisoCD0HvIxhOwCIYuGas3Sxw4OAF9DzBABRrLklDZizBDSP8AQAUYw5S4B5DNsBQJRrakkDAM0jPAFwvdZeZo/zmLMEXDyG7QC4GrcGAWA1whMA1+LWIADsQHgC4FotXWYPZyksDK0DRbCFFxCeALgWl9m7A0Or8BrCEwDX4jJ752NoFV7E1XYAXI3L7J0tXCuYA05CeALgelxm71y1Q6v1AxRDq3A7hu0AABHD0Cq8iJ4nAEBEMbQKryE8AQAijqFVeIlrhu127typp556SlOnTlUgEJDP55PP57vgfmvXrtWQIUOUmJioSy+9VBMnTtTWrVstaDEAAPAi1/Q8LV68WH/+859N7XPfffdpxYoV6tixozIzM3X27Fnl5eVp48aNWrdunW666abINBYAAHiWa8LTsGHD1LdvXw0ePFiDBw9Wr169VFlZ2ez7N23apBUrVqhLly7atm2bMjIyJEnbtm3T6NGjNWPGDI0ePVrJyckWfQIAgBnc8BlO5ZphuwcffFCLFi3SpEmT1K1btwu+/5lnnpEkPfroo3XBSQqFsNmzZ+v06dPKZZlbAHAkViWHk7kmPJlRUVGh9957T5J0yy23NHq9dtuGDRssbRcA4MJYlRxO58nwtG/fPlVWVqpr164KNNHXO3DgQEnSnj17rG4aAOACuOEznM41c57MOHLkiCQ1GZwkKSEhQcnJySouLlZpaak6derU5PsqKysbzKsqKSmRJAWDQQWDwTC3WnXHrv8Ia1B361Fze7ih7r16STEx7VRTc/6K6thYQz17VsnBzW6WG2ruRfXrHu7aezI8nTlzRpIUHx/f7HsSEhJ0+vTpFsPTk08+qYULFzbavnHjxhaPHQ55eXkRPT6aRt2tR83t4fS6z5nTQ88/3081NTGKianR7Nmfas+eI3LzgIHTa+5VeXl5Ki8vD+sxPRmewuXhhx/W/Pnz656XlJQoNTVVmZmZSkpKisg5g8Gg8vLyNG7cOPn9/oicA41Rd+tRc3u4pe4TJ0q/+EW1Dh6sUVqaoUDge5K+Z3ezWsUtNfea+nWvqKgI67E9GZ4SExMlqcWkWVZWJknN9jpJUlxcnOLi4hpt9/v9Ef8BsOIcaIy6W4+a28MNdb/yytCXV7ih5l7k9/tVVVUV1mN6csJ4jx49JEmFzVyaUVZWptOnT+uSSy5pMTwBAAB8myfDU58+fRQXF6eioiIdO3as0eu7du2SJPXt29fqpgEAAJfzZHjq2LGjxowZI0l69dVXG72+bt06SdKkSZMsbRcAAHA/T4YnSXUTvZcsWaL8/Py67du2bdPKlSuVnJys7Oxsu5oHAABcyjUTxt966y0tXry47vm5c+ckSUOHDq3blpOTox/96EeSpLFjx2revHlasWKF+vfvr3HjxuncuXPKy8uTYRhas2YN97UDYAvu2Qa4m2vCU1FRkbZv395oe/1tRUVFDV5bvny5+vfvr2effVZ5eXlq3769xo4dq5ycHA0fPjzibQaAb8vNPX/rkZgYadUqiU5wwF1cE56mT5+u6dOnW7YfAPt5rYemuXu2ZWV54/MB0cKzc54AuFturtSzpzRmTOgxN9fuFrUd92wDvIHwBMBxmuuhaWbpNtfIyAgN1dUXGyulp9vTHgCtQ3gC4Dhe7aEJBEJznGJjQ89jY6WVKxmyA9zGNXOeAESP2h6a+gHKKz002dmhOU4HDoQ+D8EJcB96ngA4jtd7aAIBafRo73weINrQ8wTAkeihAeBUhCcAYReuJQYCAUITAOdh2A5AWHlxiQEAqI/wBCBsvLrEAADUR3gCEDZeXWIAAOojPAEIGxaBBBANCE8AwsbrSwwAgMTVdgDCjCUGAHhdq3ueFi5cqEJmgQJoAotAAvCyNoWnK6+8UpMmTdJf/vIX1Xx7liiAsCgslDZv5oo1AHCKVoenJUuWqEePHnrrrbc0ZcoUpaamKicnRwUFBWFsHhDdWDMJAJyn1eHpkUce0cGDB7Vx40bdeuutOnXqlJ544gmlp6dr/Pjxeu2111RVVRXOtgJRhTWTwofeOwDh1Oar7caOHauXX35Zx44d07Jly9SnTx9t3LhRt912mwKBgB566CHl5+eHo61AVGHNpPCg9w5AuIVtqYIuXbpo/vz5+vzzz7Vlyxbdfvvt+te//qWlS5fqqquu0g9/+EO98cYb4Tod4HmsmdR29N4BiISwr/N08OBBbdiwQX/729/qtgUCAW3evFm33HKLhgwZoqNHj4b7tIDnsGZS29F7ByASwhKegsGgXn75Zf3whz9U79699atf/UpVVVWaP3++vvzySx0+fFgfffSRJkyYoB07dmju3LnhOC3gednZUkFBaL5OQUHoOS4evXcAIqFNi2R+8cUXWr16tV544QV9/fXXMgxDw4cP1+zZs3XrrbcqLi6u7r3Dhg3Tm2++qaFDh+r9999vc8OBaBEI0NvUWrW9d7NmhXqc6L0DEA6tDk/XX3+9tm3bJsMwlJSUpDlz5mj27Nn63ve+1+J+11xzjT755JPWnhYATGHFcwDh1urwtHXrVg0cOFCzZ8/WtGnTFB8ff1H73XPPPbrhhhtae1oAMI3eOwDh1Orw9Mknn2jQoEGm9xs2bJiGDRvW2tMCAADYqtUTxlsTnAAAANwu7EsVAAAAeBnhCQAAwATCEwAAgAmEJwAAABMITwDQRoWFoVXguWceEB0IT4DL8YvbXrm5Us+e0pgxocfcXLtbBCDSCE+Ai/GL216FhdLMmedvPlxTE7oVDEEW8DbCE+BS/OK2X37++frXqq4O3QoGgHcRngCX4he3/TIypJhv/SsaGxu6hx4A7yI8AS7FL277BQLSqlWhukuhx5UruY8e4HWEJ8Cl+MXtDNnZUkFBaNJ+QUHoOQBva/WNgQHYLztbysoKDdWlpxOc7BIIUHsgmhCeAJfjFzcAWIthOwAAABMIT4CDsOAlADgf4QlwCBa8BAB3IDwBDsCClwDgHoQnwAFY8BIA3IPwBDgAC14CgHt4PjyNHj1aPp+v2a93333X7iYCLHgJAC4SNes83XzzzUpMTGy0/YorrrChNUBjLHgJAO4QNeFp2bJl6tWrl93NAFrEgpcA4HyeH7YDAAAIJ8ITAACACVEzbJebm6tTp04pJiZGvXv31k033aQePXrY3SwAAOAyUROelixZ0uD5/fffr5ycHOXk5DS7T2VlpSorK+uel5SUSJKCwaCCwWBE2ll73EgdH02j7taj5vag7taj5vaoX/dw195nGIYR1iM6zGOPPabevXtr+PDh6t69u44ePap169ZpyZIlqqio0PLlyzVv3rwm912wYIEWLlzYaPuLL76o+Pj4SDcdAACEQXl5uaZNm6ZvvvlGSUlJbT6e58NTczZu3KisrCwlJyfr+PHj6tixY6P3NNXzlJqaqpMnT4al+E0JBoPKy8vTuHHj5Pf7I3IONEbdrUfN7UHdrUfN7VG/7hUVFUpJSQlbeIqaYbtvy8zM1Pe//33t2LFD27dv1+jRoxu9Jy4uTnFxcY22+/3+iP8AWHEONEbdrUfN7UHdrUfN7eH3+1VVVRXWY0b11XYZGRmSpBMnTtjcEgAA4BZRHZ6Ki4slSQkJCTa3BAAAuEXUhqeioiJ9+OGHkqSBAwfa3BoAF6OwUNq8OfQIAHbxdHjaunWr1q9fr+rq6gbbCwoKNGXKFJWVlenGG29UgPthAI6Xmyv17CmNGRN6zM21u0UAopWnJ4zv379fM2bMULdu3TRw4EAlJyfr8OHD2rlzp86ePatrrrlGq1evtruZAC6gsFCaOVOqqQk9r6mRZs0K3UiZv30AWM3T4em6667TnDlztH37dn3yyScqLi5WQkKC+vfvr1tvvVVz5sxpcokCAM6Sn38+ONWqrpYOHCA8AbCep8PT1Vdfrd/97nd2NwNAG2VkSDExDQNUbKyUnm5fmwBEL0/PeQLgDYGAtGpVKDBJoceVK+l1AmAPT/c8AfCO7OzQHKcDB0I9TgQnAHYhPAFwjUCA0ATAfgzbAQAAmEB4AgAAMIHwhKhkx0rVrI4NAN5AeELUsWOlalbHBgDvIDzB1cz25jS3UnUke4PsOCcAIHIIT3Ct1vTmNLdS9cGDvsg0soVzHjgQsVMCACKI8ARXam1vTu1K1fXFxkppaUZkGtrCOaNhdWzmeQHwIsITXKm1vTl2rFQdratjM88LgFexSCZcqS33OmtqpepgMHJtbe6cXtZcz2BWlvc/OwDvo+cJrtTW3pxAQBo92tpf5Hac0y7M8wLgZfQ8wbWirTfHTXbubLwtWuZ5AfA+whNcjXudOU9hofTgg423P/kk3ysA3sCwHYCwamrITpIGD7a+LQAQCYQnAGEVzUszAIgOhCfA5Zy2llK0Ls0AIHoQngAXc+paStnZUkFBKNQVFISeA4BXEJ4Al3L6PfOiaWkGANGF8AS4FGspAYA9CE9wHKfN4XEqJmYDgD0IT3AUp87hcSImZgOAPQhPcAynz+FxIiZmA4D1WGEcjtHSHB4n9KYUFobamJHhjPbUYpV1ALAWPU9wDCfP4WE4EQBQi/AEx3DqHB6GEwEA9TFsB0fJzpayskJDdenp9gcnyfnDiQAAaxGe4DhOm8NTO5xYP0A5ZTgRAGA9hu2AC3DqcCIAwB70PAEXwYnDiQAAexCePMipl9S7ndOGEwEA9mDYzmO4pB4AgMgiPHkIl9QDABB5hCcPaemSegAAEB6EJw9x8grdAAB4BeHJQ7ikHgCAyONqO4/hknoAACKL8ORBXFIPAEDkMGwHAABgAuEJAADABMITAACACYQn1CkslDZvZlFNAABaQniCJG7rAgDAxfJ8eKqoqNBjjz2m3r17q0OHDrr88st1991369ixY3Y3zTFae1sXeqoAANHI0+Hp7NmzGjNmjBYvXqwzZ85o8uTJSk1N1Zo1azRgwAB99dVXdjfREVpzWxd6qgAA0crT4WnJkiX6+OOPNWzYMO3fv1+vvPKKtm/frqefflpFRUW6++677W6iI5i9rQs3IAYARDPPhqdz587p2WeflSQ999xzSkxMrHtt/vz56tu3r95//33t3LnTriY6htnbunADYgBANPNseProo4/0zTffKC0tTQMGDGj0+i233CJJ2rBhg9VNc6TsbKmgIDSHqaAg9Lw53IAYABDNPBuePv30U0nSwIEDm3y9dvuePXssa5PTBQLS6NEXvrULNyAGAEQzz97b7siRI5KkQDO/0Wu3Hz58uNljVFZWqrKysu55SUmJJCkYDCoYDIarqQ3UHjdSxw+XO+8MTRY/eNCntDRDgYDk8Ca3yC119xJqbg/qbj1qbo/6dQ937T0bns6cOSNJio+Pb/L1hIQESVJpaWmzx3jyySe1cOHCRts3btzY7HHDJS8vL6LHD6c9e0JfXuCmunsFNbcHdbceNbdHXl6eysvLw3pMz4ancHj44Yc1f/78uuclJSVKTU1VZmamkpKSInLOYDCovLw8jRs3Tn6/PyLnQGPU3XrU3B7U3XrU3B71615RURHWY3s2PNVeXddc2iwrK5MkderUqdljxMXFKS4urtF2v98f8R8AK86Bxqi79ai5Pai79ai5Pfx+v6qqqsJ6TM9OGO/Ro4ckqbCZxYdqt/fs2dOyNgEAAPfzbHjq16+fJGnXrl1Nvl67vW/fvpa1CQAAuJ9nw9OIESPUuXNnHTx4ULt37270+rp16yRJkyZNsrhlAADAzTwbntq3b6+5c+dKkn72s5/VzXGSpGeeeUZ79uzRqFGjNGjQILuaaAlu3gsAQHh5dsK4JD366KPatGmTtm7dqoyMDI0cOVKHDx/W9u3b1bVrV/3hD3+wu4kRlZt7/h50MTGhhS1bWjkcAABcmGd7niSpQ4cO2rx5s3JychQfH6/169fr8OHDmj59unbt2qXvfOc7djcxYrh5LwAAkeHpnidJ6tixoxYtWqRFixbZ3RRLtXTzXm6jAgBA63m65ymacfNeAAAig/DkUdy8FwCAyPD8sF00y86WsrJCQ3Xp6QQnAADCgfDkcYEAoQkAgHBi2A4AAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8oYHCQmnz5tAjAABojPCEOrm5Us+e0pgxocfcXLtbBACA8xCeHMLuHp/CQmnmTKmmJvS8pkaaNYseKAAAvo3w5AD1e3zS09spL6+H5W3Izz8fnGpVV0sHDljeFAAAHI3wZLPGPT4+Pf98P8t7fDIypJhv/d8QGyulp1vbDgAAnI7wZLOmenxqamJ08KDP0nYEAtKqVaHAJIUeV64MbQcAAOe1s7sB0a62x6d+gIqJqVFammF5W7Kzpays0FBdejrBCQCAptDzZLPGPT6G5sz51LbgEghIo0cTnAAAaA7hyQGys6WCgtDVdvn5VRo37ojdTQIAAM1g2M4hAoHQVzAo7dljd2sAAEBz6HkCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGCCZ8PT3//+d/l8vma/hg4dancTAQCAC7WzuwGRlpaWpuuvv77J7QAAAGZ5Pjxdf/31Wrt2rd3NAAAAHuHZYTsAAIBIIDzB0QoLpc2bQ48AADiB54ft8vPz9fDDD+vUqVNKSUnR9ddfr/HjxysmhtzodLm50syZUk2NFBMjrVolZWfb3SoAQLTzfHjaunWrtm7d2mDbtddeq9dee00ZGRkt7ltZWanKysq65yUlJZKkYDCoYDAY/sb+/2PXf4xWhYXSzJntVFPjkxQKULNmGRozpkqBQPjPR92tR83tQd2tR83tUb/u4a69zzAMI6xHdIj//d//1UsvvaSbb765LiTt3r1bv/zlL/Xxxx+rV69e2r17tzp37tzsMRYsWKCFCxc22v7iiy8qPj4+Ym2H9NlnKcrJGdFo++LFW3TttadsaBEAwK3Ky8s1bdo0ffPNN0pKSmrz8RwbnqZMmaIvvvjC1D5/+tOfNGTIkBbfU11drR/84Af68MMP9R//8R96+OGHm31vUz1PqampOnnyZFiK35RgMKi8vDyNGzdOfr8/Iudwg8JCKT39fM+TJMXGGsrPj1zPE3W3FjW3B3W3HjW3R/26V1RUKCUlJWzhybHDdocOHdK+fftM7VNeXn7B98TGxurBBx/Uhx9+qL/+9a8thqe4uDjFxcU12u73+yP+A2DFOZzsyitDc5xmzZKqq6XYWGnlSp+uvJK6ew01twd1tx41t4ff71dVVVVYj+nY8LR79+6IHbt2GO/EiRMROwfaLjtbysqSDhyQ0tMVkR4nAADMcmx4iqTi4mJJUkJCgs0twYUEAoQmAICzROX1+q+99pokaeDAgTa3BAAAuI1nw9Py5ct19OjRBtsMw9DKlSv1m9/8Rj6fT3PmzLGpdQAAwK08O2y3fPly3X///Ro4cKCuvPJKnT17Vp999pkOHTqkmJgY/fa3v9WgQYPsbiZaUFgo5edLGRkM3QEAnMOz4ekXv/iFNm7cqM8//1x79+5VMBhU9+7ddccdd+jnP/+5Bg8ebHcT0QJWFwcAOJVnw9O9996re++91+5moBVCq4uHgpNUu7p46Mo7eqAAAHbz7JwnuFd+/vngVKu6OrRkAQAAdiM8wXEyMkJDdfXFxobWegIAwG6EJzhOIBCa4xQbG3oeWl2cITsAgDN4ds4T3I3VxQEATkV4gmOxujgAwIkYtgMAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABO5tZ4JhGJKkkpKSiJ0jGAyqvLxcJSUl8vv9ETsPGqLu1qPm9qDu1qPm9qhf94qKCknnf4+3FeHJhNLSUklSamqqzS0BAABmlZaWqnPnzm0+js8IVwyLAjU1NTp+/Lg6deokn88XkXOUlJQoNTVVR48eVVJSUkTOgcaou/WouT2ou/WouT3q171Tp04qLS3V5ZdfrpiYts9YoufJhJiYGAUCAUvOlZSUxA+ZDai79ai5Pai79ai5PWrrHo4ep1pMGAcAADCB8AQAAGAC4clh4uLi9PjjjysuLs7upkQV6m49am4P6m49am6PSNadCeMAAAAm0PMEAABgAuEJAADABMITAACACYQnh6ioqNBjjz2m3r17q0OHDrr88st1991369ixY3Y3zdV27typp556SlOnTlUgEJDP57uoBU7Xrl2rIUOGKDExUZdeeqkmTpyorVu3WtBidysvL9f69euVnZ2tPn36qEOHDkpISFC/fv20aNEinTlzptl9qXnbPPPMM5o6daoyMjLUuXNnxcXFqWfPnrrzzjv12WefNbsfdQ+fU6dO6d/+7d/k8/mUnp7e4nupe+uNHj267t/ypr7efffdJvcLa80N2K6iosIYOnSoIcno3r27cdtttxlDhgwxJBldu3Y1Dh48aHcTXWvy5MmGpEZfLZk3b54hyejYsaMxefJkIysry2jXrp0RGxtrvPHGG9Y03KVWr15dV+Orr77auPXWW42srCyjU6dOhiTjqquuMv75z3822o+at12XLl2MDh06GEOGDDGmTJliTJkyxejdu7chyfD7/caGDRsa7UPdw+uuu+4yfD6fIclIS0tr9n3UvW1GjRplSDJuvvlm46677mr0tWfPnkb7hLvmhCcH+OUvf2lIMoYNG2aUlpbWbX/66acNScaoUaPsa5zLPfXUU0ZOTo7xl7/8xThx4oQRFxfXYnjKy8szJBldunQx9u/fX7d969atRvv27Y3k5GSjuLjYgpa709q1a42ZM2cae/fubbD9+PHjxoABAwxJxu23397gNWoeHlu2bDEqKioabX/uuecMScZll11mBIPBuu3UPbw2bdpkSDJmzpzZYnii7m1XG54OHTp0Ue+PRM0JTzarrKw0OnfubEgydu3a1ej1vn37GpKMHTt22NA677lQeJowYYIhyfjNb37T6LWf//znhiRj2bJlEWyhd23dutWQZMTFxRmVlZV126l55KWlpRmSjE8//bRuG3UPn/LyciMtLc347ne/a+zfv7/F8ETd285seIpEzZnzZLOPPvpI33zzjdLS0jRgwIBGr99yyy2SpA0bNljdtKhTUVGh9957T9L5utfH96Jt+vXrJ0mqrKzUqVOnJFFzq/j9fklS+/btJVH3cFu4cKG++uor/f73v6+rdVOou/UiVXNuDGyzTz/9VJI0cODAJl+v3b5nzx7L2hSt9u3bp8rKSnXt2rXJG0DzvWibr776SlLoF/mll14qiZpb4YUXXtC+ffuUkZGhjIwMSdQ9nPbs2aOnn35aM2bM0MiRI1VQUNDse6l7eOXm5urUqVOKiYlR7969ddNNN6lHjx4N3hOpmhOebHbkyBFJavKbWn/74cOHLWtTtLrQ9yIhIUHJyckqLi5WaWmpOnXqZGXzXG/FihWSpPHjx9fdLoGah9/SpUv1+eefq6ysTF988YU+//xzXX755XrppZcUGxsribqHS01Nje655x4lJyfr17/+9QXfT93Da8mSJQ2e33///crJyVFOTk7dtkjVnGE7m9Veuh0fH9/k6wkJCZKk0tJSy9oUrS70vZD4frTW22+/rdzcXPn9fi1evLhuOzUPv7/+9a/64x//qHXr1unzzz9Xz5499dJLL2nQoEF176Hu4fGf//mf+uSTT7R06VJ16dLlgu+n7uFxww036IUXXtDBgwdVXl6uffv26YknnlC7du302GOP1f2hJkWu5oQnABH15Zdf6o477pBhGFq6dGnd3CdExqZNm2QYhoqLi/XBBx8oIyNDo0aN0hNPPGF30zzlyJEjevTRRzVq1ChNnz7d7uZElUWLFumOO+7Qd77zHXXs2FG9e/fWI488ovXr10uSFixYoIqKioi2gfBks8TEREmhxQWbUlZWJkl031rgQt8Lie+HWceOHdP48eNVXFys+fPna968eQ1ep+aRk5ycrJEjR+rtt9/WoEGDlJOTo08++UQSdQ+Hn/3sZzp37px+//vfX/Q+1D2yMjMz9f3vf1+nT5/W9u3bJUWu5sx5slnt5LbCwsImX6/d3rNnT8vaFK0u9L0oKyvT6dOndckll/AP20X4+uuvlZmZqcOHD2vGjBlatmxZo/dQ88jz+/368Y9/rJ07d2rDhg0aPHgwdQ+DN998U8nJyZo9e3aD7WfPnpUU+sNh9OjRkqSXX35Z3bp1o+4WyMjI0I4dO3TixAlJkfs3hvBks9ohjF27djX5eu32vn37WtamaNWnTx/FxcWpqKhIx44d0xVXXNHgdb4XF+/MmTOaMGGC9u7dq6lTp2r16tVN3haHmlsjJSVFklRUVCSJuofL6dOn9f777zf52tmzZ+teqw1U1D3yiouLJZ2fxxSpmjNsZ7MRI0aoc+fOOnjwoHbv3t3o9XXr1kmSJk2aZHHLok/Hjh01ZswYSdKrr77a6HW+FxensrJSkydP1j/+8Q9lZWU1uMrr26i5NWp/iaelpUmi7uFghBaZbvR16NAhSaFa127r1auXJOoeaUVFRfrwww8lnV+CIGI1N7WkJiKi9vYsw4cPN86cOVO3nduzhF9bbs8SFxfHrRMuoKqqypgyZYohyRg5cqRRVlZ2wX2oedtt2bLFeOedd4zq6uoG28+dO2f89re/NWJiYoyOHTsaR44cqXuNukfGoUOHWn17Fup+YR999JHxxhtvGFVVVQ22Hzp0yBgxYoQhybjxxhsbvBaJmhOeHKCiosK47rrrGtwYuPY5NwZumzfffNO47rrr6r5qb9pZf9ubb77ZYJ/aG0jGx8cbkydPNiZMmMBNOy/S8uXL624MPGXKlCZv2nnXXXcZRUVFDfaj5m2zZs0aQ5KRkpJiZGVlGdOmTTMyMzON7t27G5KMDh06GK+88kqj/ah7+F0oPBkGdW+L2v/Xu3XrZkycONGYNm2aMWLECKNDhw6GJOOaa65p8ebj4ao54ckhysvLjZycHCMtLc1o37690a1bN2P69OnG0aNH7W6aq9X+oLX0tWbNmib3GzRokBEfH28kJycb48ePNz766CPrP4DLPP744xest5q5JxU1b72vvvrKeOSRR4wRI0YY3bt3N/x+v5GQkGBcc801xr333mvk5+c3uy91D6+LCU+GQd1ba+/evcacOXOMgQMHGl27djXatWtndO7c2Rg6dKjx9NNPG+Xl5c3uG86a+wzDMMwN9AEAAEQvJowDAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwBR6cc//rF8Pp8eeOCBRq/t379fiYmJSkxMVH5+vg2tA+BkPsMwDLsbAQBWKy4uVt++fXX8+HFt2rRJP/jBDyRJwWBQw4cP144dO7R69Wrdc889NrcUgNPQ8wQgKl1yySX605/+JEm68847VVxcLElasGCBduzYoZtuuongBKBJ9DwBiGoPPPCAli5dqttuu01z587V6NGjddlll2nPnj1KSUmxu3kAHIjwBCCqnTt3Ttddd512796tpKQklZaW6p133lFWVpbdTQPgUAzbAYhq7du31x//+EdJUklJiWbPnk1wAtAiwhOAqPfKK6/U/ffu3btVXV1tY2sAOB3hCUBU27Jli371q1+pW7duGjt2rLZt26YnnnjC7mYBcDDmPAGIWiUlJerXr58KCgr0zjvvaMCAAbr22mtVXFysLVu26LrrrrO7iQAciJ4nAFFr7ty5Kigo0Ny5czV+/Hhddtll+q//+i9VVVXpjjvuUFlZmd1NBOBAhCcAUenVV1/VCy+8oO9+97v69a9/Xbf9xhtv1E9+8hMdOHBA8+bNs7GFAJyKYTsAUefYsWO69tprVVZWpu3bt6t///4NXi8rK9OAAQOUn5+v119/XVOmTLGnoQAcifAEAABgAsN2AAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADDh/wF0FFHrRTicHAAAAABJRU5ErkJggg==\n"
-          },
-          "metadata": {}
-        }
-      ],
+      "outputs": [],
       "source": [
         "# plot the data\n",
         "plt.rcParams.update({'font.size': 15})\n",
@@ -118,445 +92,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {
-        "id": "wCyk1k6SNOl8",
-        "outputId": "2c36de43-f776-4e7d-969c-a9ba51d9eaab",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        }
+        "id": "wCyk1k6SNOl8"
       },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-1 {\n",
-              "  /* Definition of color scheme common for light and dark mode */\n",
-              "  --sklearn-color-text: #000;\n",
-              "  --sklearn-color-text-muted: #666;\n",
-              "  --sklearn-color-line: gray;\n",
-              "  /* Definition of color scheme for unfitted estimators */\n",
-              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-              "  --sklearn-color-unfitted-level-3: chocolate;\n",
-              "  /* Definition of color scheme for fitted estimators */\n",
-              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-              "\n",
-              "  /* Specific color for light theme */\n",
-              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-icon: #696969;\n",
-              "\n",
-              "  @media (prefers-color-scheme: dark) {\n",
-              "    /* Redefinition of color scheme for dark theme */\n",
-              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-icon: #878787;\n",
-              "  }\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 pre {\n",
-              "  padding: 0;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-hidden--visually {\n",
-              "  border: 0;\n",
-              "  clip: rect(1px 1px 1px 1px);\n",
-              "  clip: rect(1px, 1px, 1px, 1px);\n",
-              "  height: 1px;\n",
-              "  margin: -1px;\n",
-              "  overflow: hidden;\n",
-              "  padding: 0;\n",
-              "  position: absolute;\n",
-              "  width: 1px;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-              "  border: 1px dashed var(--sklearn-color-line);\n",
-              "  margin: 0 0.4em 0.5em 0.4em;\n",
-              "  box-sizing: border-box;\n",
-              "  padding-bottom: 0.4em;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-container {\n",
-              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-              "     so we also need the `!important` here to be able to override the\n",
-              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-              "  display: inline-block !important;\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-parallel-item,\n",
-              "div.sk-serial,\n",
-              "div.sk-item {\n",
-              "  /* draw centered vertical line to link estimators */\n",
-              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-              "  background-size: 2px 100%;\n",
-              "  background-repeat: no-repeat;\n",
-              "  background-position: center center;\n",
-              "}\n",
-              "\n",
-              "/* Parallel-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item::after {\n",
-              "  content: \"\";\n",
-              "  width: 100%;\n",
-              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-              "  flex-grow: 1;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel {\n",
-              "  display: flex;\n",
-              "  align-items: stretch;\n",
-              "  justify-content: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-              "  align-self: flex-end;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-              "  align-self: flex-start;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-              "  width: 0;\n",
-              "}\n",
-              "\n",
-              "/* Serial-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-serial {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "  align-items: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  padding-right: 1em;\n",
-              "  padding-left: 1em;\n",
-              "}\n",
-              "\n",
-              "\n",
-              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-              "clickable and can be expanded/collapsed.\n",
-              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-              "*/\n",
-              "\n",
-              "/* Pipeline and ColumnTransformer style (default) */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable {\n",
-              "  /* Default theme specific background. It is overwritten whether we have a\n",
-              "  specific estimator or a Pipeline/ColumnTransformer */\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable label */\n",
-              "#sk-container-id-1 label.sk-toggleable__label {\n",
-              "  cursor: pointer;\n",
-              "  display: flex;\n",
-              "  width: 100%;\n",
-              "  margin-bottom: 0;\n",
-              "  padding: 0.5em;\n",
-              "  box-sizing: border-box;\n",
-              "  text-align: center;\n",
-              "  align-items: start;\n",
-              "  justify-content: space-between;\n",
-              "  gap: 0.5em;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-              "  font-size: 0.6rem;\n",
-              "  font-weight: lighter;\n",
-              "  color: var(--sklearn-color-text-muted);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-              "  /* Arrow on the left of the label */\n",
-              "  content: \"▸\";\n",
-              "  float: left;\n",
-              "  margin-right: 0.25em;\n",
-              "  color: var(--sklearn-color-icon);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable content - dropdown */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content {\n",
-              "  max-height: 0;\n",
-              "  max-width: 0;\n",
-              "  overflow: hidden;\n",
-              "  text-align: left;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-              "  margin: 0.2em;\n",
-              "  border-radius: 0.25em;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-              "  /* Expand drop-down */\n",
-              "  max-height: 200px;\n",
-              "  max-width: 100%;\n",
-              "  overflow: auto;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-              "  content: \"▾\";\n",
-              "}\n",
-              "\n",
-              "/* Pipeline/ColumnTransformer-specific style */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific style */\n",
-              "\n",
-              "/* Colorize estimator box */\n",
-              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  /* The background is the default theme color */\n",
-              "  color: var(--sklearn-color-text-on-default-background);\n",
-              "}\n",
-              "\n",
-              "/* On hover, darken the color of the background */\n",
-              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Label box, darken color on hover, fitted */\n",
-              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator label */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  font-family: monospace;\n",
-              "  font-weight: bold;\n",
-              "  display: inline-block;\n",
-              "  line-height: 1.2em;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label-container {\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific */\n",
-              "#sk-container-id-1 div.sk-estimator {\n",
-              "  font-family: monospace;\n",
-              "  border: 1px dotted var(--sklearn-color-border-box);\n",
-              "  border-radius: 0.25em;\n",
-              "  box-sizing: border-box;\n",
-              "  margin-bottom: 0.5em;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "/* on hover */\n",
-              "#sk-container-id-1 div.sk-estimator:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-              "\n",
-              "/* Common style for \"i\" and \"?\" */\n",
-              "\n",
-              ".sk-estimator-doc-link,\n",
-              "a:link.sk-estimator-doc-link,\n",
-              "a:visited.sk-estimator-doc-link {\n",
-              "  float: right;\n",
-              "  font-size: smaller;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1em;\n",
-              "  height: 1em;\n",
-              "  width: 1em;\n",
-              "  text-decoration: none !important;\n",
-              "  margin-left: 0.5em;\n",
-              "  text-align: center;\n",
-              "  /* unfitted */\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted,\n",
-              "a:link.sk-estimator-doc-link.fitted,\n",
-              "a:visited.sk-estimator-doc-link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "/* Span, style for the box shown on hovering the info icon */\n",
-              ".sk-estimator-doc-link span {\n",
-              "  display: none;\n",
-              "  z-index: 9999;\n",
-              "  position: relative;\n",
-              "  font-weight: normal;\n",
-              "  right: .2ex;\n",
-              "  padding: .5ex;\n",
-              "  margin: .5ex;\n",
-              "  width: min-content;\n",
-              "  min-width: 20ex;\n",
-              "  max-width: 50ex;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  box-shadow: 2pt 2pt 4pt #999;\n",
-              "  /* unfitted */\n",
-              "  background: var(--sklearn-color-unfitted-level-0);\n",
-              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted span {\n",
-              "  /* fitted */\n",
-              "  background: var(--sklearn-color-fitted-level-0);\n",
-              "  border: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link:hover span {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link {\n",
-              "  float: right;\n",
-              "  font-size: 1rem;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1rem;\n",
-              "  height: 1rem;\n",
-              "  width: 1rem;\n",
-              "  text-decoration: none;\n",
-              "  /* unfitted */\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ],
+      "outputs": [],
       "source": [
         "# create a generic instance the model\n",
         "model = LinearRegression()\n",
@@ -581,7 +121,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "id": "O_dTM435NOl9"
       },
@@ -595,37 +135,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
-        "id": "WBjr5_bbNOl9",
-        "outputId": "a2d8eef1-c77f-4501-d96f-1f6c95ef9a80",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 479
-        }
+        "id": "WBjr5_bbNOl9"
       },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0, 0.5, 'y')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
+      "outputs": [],
       "source": [
         "# plot the data and the fitted line\n",
         "fig=plt.figure()\n",
@@ -654,23 +168,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
-        "id": "pIkLZ299NOl9",
-        "outputId": "e333ce2c-c8a4-4566-85ad-74d8f400170f",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
+        "id": "pIkLZ299NOl9"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Our model has parameters a=0.48, b=-2.44\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "print('Our model has parameters a={:.2f}, b={:.2f}'.format(model.coef_[0][0],model.intercept_[0]))"
       ]
@@ -686,7 +188,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": null,
       "metadata": {
         "id": "nYSj09uRNOl-"
       },
@@ -716,7 +218,7 @@
       "metadata": {
         "id": "QKwuHjXtPg2J"
       },
-      "execution_count": 30,
+      "execution_count": null,
       "outputs": []
     },
     {