Issue 6

issues6.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f482772c",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 9) (2030385567.py, line 9)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[2], line 9\u001b[1;36m\u001b[0m\n\u001b[1;33m    Middle value: The middle value simply refers to the data point located in the center of the set, regardless of the order. However, this term doesn't explicitly specify whether the data is sorted or not.\u001b[0m\n\u001b[1;37m                                                                                                                                                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 9)\n"
     ]
    }
   ],
   "source": [
    "# 1.Explain the difference between median and middle value?\n",
    "# 2.Are mean and mode values always the same for unsorted and sorted dataset? Why?\n",
    "# 3.If range is caculate with last datapoint – first datapoint, should the dataset is sorted first or not? Why?\n",
    "\n",
    "1. Difference between Median and Middle Value:\n",
    "While both terms are related to the \"center\" of a dataset, they have slight differences:\n",
    "\n",
    "Median: The median is the middle value when the data is arranged in numerical order. If you have an odd number of data points, the median is the exact middle value. If you have an even number of data points, the median is the average of the two middle values.\n",
    "Middle value: The middle value simply refers to the data point located in the center of the set, regardless of the order. However, this term doesn't explicitly specify whether the data is sorted or not.\n",
    "Example:\n",
    "\n",
    "Consider the dataset: {1, 5, 3, 7}.\n",
    "\n",
    "Median: Sorting the data gives {1, 3, 5, 7}. The median is the third value, which is 3.\n",
    "Middle value: Without sorting, the middle value is still the third data point, which is 3.\n",
    "As you can see, the median only applies when the data is sorted, while the middle value can refer to any data point in the middle, regardless of order.\n",
    "\n",
    "2. Mean and Mode in Unsorted and Sorted Data:\n",
    "Mean: The mean (average) is the sum of all data points divided by the number of data points. It calculates the \"center of mass\" of the data, regardless of order. Therefore, the mean will be the same for both unsorted and sorted datasets.\n",
    "\n",
    "Mode: The mode is the most frequent value in the dataset. Whether the data is sorted or not impacts the mode only if there are multiple \"most frequent\" values.\n",
    "\n",
    "Unsorted data: If the data has multiple values with the highest frequency, the unsorted data might not reveal all modes clearly.\n",
    "Sorted data: Sorting the data can help identify all modes easily by grouping occurrences of the same value.\n",
    "Therefore:\n",
    "* Mean: Stays the same for both sorted and unsorted data.\n",
    "* Mode: May be easier to identify with sorted data but might not change the actual mode itself.\n",
    "\n",
    "3. Calculating Range:\n",
    "The range is calculated as the difference between the largest data point and the smallest data point in the dataset.\n",
    "\n",
    "It does not require the data to be sorted first. Regardless of the order, the largest and smallest values will remain the same.\n",
    "\n",
    "Therefore, you can calculate the range by simply finding the maximum and minimum values of the data, even if the data is unsorted.\n"
   ]
  },
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7LCzsgstPnz6dby2551DQXN1ut3799dcCH38xnoPw6tWr9fjjj+uXX35Rnz59lJ2d7V3n66+/VteuXSVJM2bM0JdffqkNGzbo8ccfl6Q8PcjvExEOh8NnvaNHj6patWp51jt/WWFfa4/zeyyd67M/vS+s3bt3y+FweJ+jqNtLfkaPHq0nnnhCffr00YcffqivvvpKGzZsUGJiYqHHuFB/c/ftgQce0Geffaa0tDS5XC7NmDFDt9xyS76Pz8/IkSO1YcMGffHFF/rnP/8pl8ul3r17+zzHL7/8og8//FB2u93np3HjxpLkDSR33nmnZs2apd27d+uPf/yjYmJi1KZNG6WkpOR53vO3M4fDIel/2+Nvv/2mP/zhD/rqq680YcIErVq1Shs2bNDChQt91ruYOnXq5DnGORwO7zU4t9xyS555PfPMMzLG6NixYz5jnd/T0NBQRUVF5dmO89ve87Nw4ULddtttql69uubMmaN169Zpw4YNSk5OznfbLsy+WZCi7oulyeXxcQsUWWhoqEaPHq3Ro0fr+PHjWr58uR577DHddNNN2rt3ryIiIgp8rGdnS09PV40aNXzuO3DggKKjo33WO/+iPunchX35nVWx2Wx5ls2ZM0dJSUmaNm2az/KTJ09eeJIBkHuu5ztw4ID3uyv85TkIS1KHDh3kdDr197//XS+++KLGjBkjSZo3b57sdruWLFmi8PBw72MXLVrk9/NGRUXp4MGDeZafv6ywr3Ww7N+/Xxs3blTHjh29nw4LxPYyZ84cDRo0SBMnTvRZfuTIkUJ/ZPRC/c39C2vAgAF65JFH9NJLL+m6667TwYMHNXz48ELXWqNGDe821L59e1WrVk0DBw7U2LFj9Z///EeSFB0drWbNmunpp5/Od4zcZ7vuuusu3XXXXTp16pTWrFmjsWPHqmfPnvrxxx+VkJBQ6LpWrFihAwcOaNWqVT6fggvU94N4tr0XX3yxwE8Fnf8PooMHD6p69ere22fPntXRo0fzBIj8jkP5mTNnjmrXrq358+f7PCb3PzQCxer7YjBxRqUUqFixom655RYNHz5cx44d837Z1/n/QvK44YYbJJ3bSXPbsGGDtm3bps6dO0uS2rRpI4fDofnz5/ust379+iKd8rfZbN5aPL777jufT92UlPr166t69eqaO3euz1sMp06d0nvvvef9JFCgPPzww7r66qs1efJk7y9Wz0e0y5Qp410vKytLb731lt/P06lTJ3322Wc+ITInJyfPa1XY1zoYsrKydPfdd+vs2bN6+OGHvcsLu70UtH0XNMZHH32k/fv3F7q+gvp71VVX+fyiCQ8P17333qs33nhDU6dOVfPmzdW+fftCP8/57rjjDiUlJWnGjBne/axnz57asmWLrrrqqnzPwuYOKh5ly5ZVt27d9Pjjj+vMmTNF/nI9zy/u8/v4yiuv+DkzX+3bt1fFihX1//7f/8t3Tq1bt/aeufN4++23fW6/++67Onv2rN/fo2Oz2RQWFuYTUg4ePJjvp34Kq7jH3dKIMypXqF69eqlJkyZq3bq1qlSpot27d+v5559XQkKC94vGmjZtKkl64YUXNHjwYNntdtWvX1/169fXvffeqxdffFEhISHq1q2bdu3apSeeeELx8fF68MEHJZ17G2D06NGaNGmSKlWqpL59+2rfvn0aP368YmNjfa75uJCePXvqqaee0tixY9WxY0elpaXpySefVO3atfN8tDLQQkJC9Oyzz+qOO+5Qz549dd999yk7O1tTpkzR8ePHNXny5IA+n91u18SJE3XbbbfphRde0N///nf16NFDU6dO1YABA3Tvvffq6NGj+uc//5nnF0BR/P3vf9fixYt1ww036B//+IciIiL00ksv5XlPv7CvdUnbs2eP1q9fL7fbrRMnTni/8G337t167rnnvG+NSYXfXsqXL6+EhAR98MEH6ty5sypXrqzo6Gjvx1Nff/11NWjQQM2aNdPGjRs1ZcqUPP+SvZDo6GjdcMMNeuKJJ1S2bFm9/PLL+uGHH3w+ouwxbNgwPfvss9q4caNee+214jVL595SbNOmjZ566im99tprevLJJ5WSkqJ27drpgQceUP369XX69Gnt2rVLH3/8saZPn64aNWronnvukdPpVPv27RUbG6uDBw9q0qRJqlChgq655poi1dCuXTtVqlRJf/7znzV27FjZ7Xa9/fbb2rx5c7HnJ527TuzFF1/U4MGDdezYMd1yyy2KiYnR4cOHtXnzZh0+fDjPWbWFCxcqNDRUXbp00datW/XEE08oMTExz3V0heX5GPOwYcN0yy23aO/evXrqqacUGxur7du3+zVmcY+7pVKQL+ZFPjxX3W/YsCHf+3v06HHRT/0899xzpl27diY6OtqEhYWZmjVrmqFDh5pdu3b5PO7RRx81cXFxJiQkxEjyfrwzJyfHPPPMM6ZevXrGbreb6OhoM3DgQLN3716fx7vdbjNhwgRTo0YNExYWZpo1a2aWLFliEhMTfT6BcaFPzGRnZ5sxY8aY6tWrm/DwcNOyZUuzaNEiM3jwYJ95FvQJkYLGvlgfc1u0aJFp06aNCQ8PN2XLljWdO3c2X375ZaGeJz8XW7dNmzamUqVK5vjx48YYY2bNmmXq169vHA6HqVOnjpk0aZKZOXNmnk8HJCQkmB49euQZr2PHjj6fsjLGmC+//NL70d5q1aqZv/71r+bVV1/NM2ZhX+uOHTuaxo0b53nugmqSZIYPH15Qi4wx/3tNPT9lypQxlSpVMq1atTKjRo3K9yPghd1ejDFm+fLlpkWLFsbhcBhJ3n3k119/NUOHDjUxMTEmIiLCXH/99ebzzz/Pt4/58czt5ZdfNldddZWx2+2mQYMG5u233y7wMUlJSaZy5comMzPzouPn7k1Bn4i69dZbTWhoqPcj14cPHzYPPPCAqV27trHb7aZy5cqmVatW5vHHHze//fabMcaYN954w3Tq1MlUrVrVhIWFmbi4OHPbbbeZ7777zjtuQfuNZ5vO/ZUBa9euNW3btjURERGmSpUq5u677zbffvutkWRmz559wfkVdn9avXq16dGjh6lcubKx2+2mevXqpkePHj6P83zqZ+PGjaZXr16mXLlypnz58ub222/P82mxgrZXz33nf+pn8uTJplatWsbhcJiGDRuaGTNmeJ8vt4K29/zGLO5xt7SxGZPPJfVAMezcuVMNGjTQ2LFjfb5ICiitDh06pISEBI0YMULPPvtssMsBLisEFRTL5s2b9c4776hdu3aKjIxUWlqann32WWVkZGjLli0FfvoHKA327dunn3/+WVOmTNGKFSv0448/+lzsCeDiuEYFxVK2bFl98803mjlzpo4fP64KFSooKSlJTz/9NCEFpZ7n+pFatWrp7bffJqQAfuCMCgAAsCw+ngwAACyLoAIAACyLoAIAACzrsr6Y1u1268CBAypfvnyhvxIZAAAElzFGJ0+eVFxc3EW/HPSyDioHDhxQfHx8sMsAAAB+2Lt370W/EfqyDiqeP6e9d+9eRUZGBnRsl8ulZcuWqWvXrrLb7QEd+3JBD+iBB32gBxI98KAPxe9BRkaG4uPjvb/HL+SyDiqet3siIyNLJKhEREQoMjKyVG+I9IAeSPRBogcSPfCgD4HrQWEu2+BiWgAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFlBDSq1atWSzWbL8zN8+PBglgUAACwiqH/rZ8OGDcrJyfHe3rJli7p06aJbb701iFUBAACrCGpQqVKlis/tyZMn66qrrlLHjh2DVBEAALASy1yjcubMGc2ZM0fJycmF+muKAADgyhfUMyq5LVq0SMePH9eQIUMKXCc7O1vZ2dne2xkZGZLO/blpl8sV0Ho84wV63MsJPbBGD/bt26ejR48We5yoqCjVqFHDr8daoQ8FuVT9sXIPLhV6cE5J98EK+/zFFLcHRXmczRhj/HqWALvpppsUFhamDz/8sMB1xo0bp/Hjx+dZPnfuXEVERJRkeQAAIEAyMzM1YMAAnThxQpGRkRdc1xJBZffu3apTp44WLlyo3r17F7hefmdU4uPjdeTIkYtOtKhcLpdSUlLUpUsX2e32gI59uaAHwe/B5s2b1aFDB82QVL8Y46RJukfSmjVrlJiYWOTHB7sPBbmU/bFqDy4lenBOSfbBKvv8xRS3BxkZGYqOji5UULHEWz+zZ89WTEyMevToccH1HA6HHA5HnuV2u73EdpqSHPtyQQ+C14OQkBBlZWWpoaSWxRlHUtbv4xVnHlbbFoLRH6v1IBjowTkl0Qer7fMX428PivKYoF9M63a7NXv2bA0ePFihoZbITQAAwCKCHlSWL1+uPXv2KDk5OdilAAAAiwn6KYyuXbvKApfJAAAACwr6GRUAAICCEFQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlBT2o7N+/XwMHDlRUVJQiIiLUvHlzbdy4MdhlAQAACwgN5pP/+uuvat++vTp16qRPPvlEMTEx+umnn1SxYsVglgUAACwiqEHlmWeeUXx8vGbPnu1dVqtWreAVBAAALCWoQWXx4sW66aabdOutt2r16tWqXr26hg0bpnvuuSff9bOzs5Wdne29nZGRIUlyuVxyuVwBrc0zXqDHvZxcbj3Yt2+fjh49WuxxoqKiVKNGDUnB74Hb7ZbT6ZRbUnEqcEty/j6eP3MJdh8Kcin7Y9UeXCr79u3T4cOHJUmpqakKCfHvyoHc+5cV+HPccLvdknz7kJ2dLYfDUex60tLSLLHPX0xx94eiPM5mjDF+PUsAhIeHS5JGjx6tW2+9VV9//bVGjRqlV155RYMGDcqz/rhx4zR+/Pg8y+fOnauIiIgSrxcAABRfZmamBgwYoBMnTigyMvKC6wY1qISFhal169Zau3atd9kDDzygDRs2aN26dXnWz++MSnx8vI4cOXLRiRaVy+VSSkqKunTpIrvdHtCxLxeXUw82b96sDh06aIak+sUYJ03SPZLWrFmjxMTEoPfAM681khKLM46kDvrfvIoq2H0oyKXsj1V7cCl49y+nU+VmzVJscrJCsrKKPM75+1ew+XvccDudSs/Vh2WSJkjFPv5I8o4V7H3+Yoq7P2RkZCg6OrpQQSWob/3ExsaqUaNGPssaNmyo9957L9/1HQ5HvqfW7HZ7iR04SnLsy8Xl0IOQkBBlZWWpoaSWxRlHUtbv4+Wec7B64JlXiKTiPHtB8yoqq20LweiP1XpwKXj6XF/SfkktsrJk9yOoBGo7DBR/jxsuSen6Xx/SdG5exT3+SPKOZZV9/mL83R+K8pigfjy5ffv2SktL81n2448/KiEhIUgVAQAAKwlqUHnwwQe1fv16TZw4UTt27NDcuXP16quvavjw4cEsCwAAWERQg8o111yj999/X++8846aNGmip556Ss8//7zuuOOOYJYFAAAsIqjXqEhSz5491bNnz2CXAQAALCjoX6EPAABQEIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwLIIKAACwrKAGlXHjxslms/n8VKtWLZglAQAACwkNdgGNGzfW8uXLvbfLlCkTxGoAAICVBD2ohIaGchYFAADkK+hBZfv27YqLi5PD4VCbNm00ceJE1alTJ991s7OzlZ2d7b2dkZEhSXK5XHK5XAGtyzNeoMe9nFxOPXC73XI6nXJLKk61bknO38fLvV0FqwclNa+iCnYfCnIp+2PVHlwK3j47nZIk1+//LfI4Otfnbdu2ye12F6umqKgo1ahRo1hj+Lv9uPLpg1Mq9nYYyLFKutfF3R+K8jibMcb49SwB8MknnygzM1P16tXTL7/8ogkTJuiHH37Q1q1bFRUVlWf9cePGafz48XmWz507VxEREZeiZAAAUEyZmZkaMGCATpw4ocjIyAuuG9Sgcr5Tp07pqquu0sMPP6zRo0fnuT+/Myrx8fE6cuTIRSdaVC6XSykpKerSpYvsdntAx75cXE492Lx5szp06KA1khKLM46kDpLWrFmjxMTEoPegpOZVVMHuQ0EuZX+s2oNLwdPnVU6n0mfNUpfkZNmzsoo8zruS7pE0Q1L9YtST9vs4/m7PHv5uPy6nUym5+uCZV3G3Q0kBG6uke13c/SEjI0PR0dGFCipBf+snt7Jly6pp06bavn17vvc7HA45HI48y+12e4kdOEpy7MvF5dCDkJAQZWVlKURScSoNkZT1+3i55xysHpT0vIrKattCMPpjtR5cCrn7LEn2rCy/gop0rs8NJbUsTj0KzPZc3O0ndx+yfq8rEFtGoMa6FL32d38oymMs9T0q2dnZ2rZtm2JjY4NdCgAAsICgBpUxY8Zo9erV2rlzp7766ivdcsstysjI0ODBg4NZFgAAsIigvvWzb98+3X777Tpy5IiqVKmi6667TuvXr1dCQkIwywIAABYR1KAyb968YD49AACwOEtdowIAAJAbQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFiWX0Fl586dga4DAAAgD7+CytVXX61OnTppzpw5On36dKBrAgAAkORnUNm8ebNatGihhx56SNWqVdN9992nr7/+OtC1AQCAUs6voNKkSRNNnTpV+/fv1+zZs3Xw4EFdf/31aty4saZOnarDhw8Huk4AAFAKFeti2tDQUPXt21fvvvuunnnmGf30008aM2aMatSooUGDBik9PT1QdQIAgFKoWEHlm2++0bBhwxQbG6upU6dqzJgx+umnn7RixQrt379fvXv3DlSdAACgFAr150FTp07V7NmzlZaWpu7du+vNN99U9+7dFRJyLvfUrl1br7zyiho0aBDQYgEAQOniV1CZNm2akpOTddddd6latWr5rlOzZk3NnDmzWMUBAIDSza+gsn379ouuExYWpsGDB/szPAAAgCQ/r1GZPXu2FixYkGf5ggUL9MYbbxS7KAAAAMnPoDJ58mRFR0fnWR4TE6OJEycWuygAAADJz6Cye/du1a5dO8/yhIQE7dmzp9hFAQAASH4GlZiYGH333Xd5lm/evFlRUVF+FTJp0iTZbDaNGjXKr8cDAIArj19B5U9/+pMeeOABrVy5Ujk5OcrJydGKFSs0cuRI/elPfyryeBs2bNCrr76qZs2a+VMOAAC4QvkVVCZMmKA2bdqoc+fOcjqdcjqd6tq1q2644YYiX6Py22+/6Y477tCMGTNUqVIlf8oBAABXKL+CSlhYmObPn68ffvhBb7/9thYuXKiffvpJs2bNUlhYWJHGGj58uHr06KEbb7zRn1IAAMAVzK/vUfGoV6+e6tWr5/fj582bp2+//VYbNmwo1PrZ2dnKzs723s7IyJAkuVwuuVwuv+vIj2e81NRU7zfu+isqKko1atQIRFmXlKcHge5tSXC73XI6nXJLKk61bknO38fLvV3504N9+/bp6NGjxahGSktLK5F5FVXuPgRiXlJg9ouSet3zczntD4Hm7bPTKUly/f5ffziloG/P3nH83H5c+fQhEPMK9Fgl2evi7g9FeZzNGGOK+gQ5OTl6/fXX9dlnn+nQoUNyu90+969YseKiY+zdu1etW7fWsmXLlJiYKElKSkpS8+bN9fzzz+f7mHHjxmn8+PF5ls+dO1cRERFFnQYAAAiCzMxMDRgwQCdOnFBkZOQF1/UrqNx///16/fXX1aNHD8XGxspms/nc/69//euiYyxatEh9+/ZVmTJlvMtycnJks9kUEhKi7Oxsn/uk/M+oxMfH68iRIxedaFGlpqYqPT1dvyUnq35Wlt/jpEm6R9KaNWu8gexy4XK5lJKSoi5dushutwe7nAvavHmzOnTooDWSitPlzZI66H+vl7898NQzQ1L9YtSzTNIEKeDzKipPH2JjY5WUlFTseQVqvyip1z0/l9P+EGiePq9yOpU+a5a6JCfL7sdx8V39/roruNuzdxw/tx+X06mUXH0I1LykwPWopHtd3P0hIyND0dHRhQoqfr31M2/ePL377rvq3r27Pw+XJHXu3Fnff/+9z7K77rpLDRo00COPPJInpEiSw+GQw+HIs9xutwf8wOF5u6d+VpZaFyOohEjK+n28y/XgVhL9DbSQkBBlZWUpRFJxKi3o9SpqDzz1NJTUshj1pHnqUcnMq8jjBGhega7nUvbnctgfAi13nyXJnpXlV1CRrLk9+1tP7j4EYl4egRrrUvTa3/2hKI/xK6iEhYXp6quv9uehXuXLl1eTJk18lpUtW1ZRUVF5lgMAgNLJr6tEH3roIb3wwgvy410jAACAQvPrjMoXX3yhlStX6pNPPlHjxo3znMJZuHChX8WsWrXKr8cBAIArk19BpWLFiurbt2+gawEAAPDhV1CZPXt2oOsAAADIw+9vMjt79qyWL1+uV155RSdPnpQkHThwQL/99lvAigMAAKWbX2dUdu/erZtvvll79uxRdna2unTpovLly+vZZ5/V6dOnNX369EDXCQAASiG/zqiMHDlSrVu31q+//ipnrq8Q7tu3rz777LOAFQcAAEo3vz/18+WXX+b5A4QJCQnav39/QAoDAADw64yK2+1WTk5OnuX79u1T+fLli10UAACA5GdQ6dKli88fDrTZbPrtt980duzYYn2tPgAAQG5+vfXzr3/9S506dVKjRo10+vRpDRgwQNu3b1d0dLTeeeedQNcIAABKKb+CSlxcnDZt2qR33nlH3377rdxut4YOHao77rjD5+JaAACA4vArqEiS0+lUcnKykpOTA1kPAACAl19B5c0337zg/YMGDfKrGAAAgNz8CiojR470ue1yuZSZmamwsDBFREQQVAAAQED49amfX3/91efnt99+U1pamq6//noupgUAAAHj99/6OV/dunU1efLkPGdbAAAA/BWwoCJJZcqU0YEDBwI5JAAAKMX8ukZl8eLFPreNMUpPT9d//vMftW/fPiCFAQAA+BVU+vTp43PbZrOpSpUquuGGG/Tcc88Foi4AAAD/gorb7Q50HQAAAHkE9BoVAACAQPLrjMro0aMLve7UqVP9eQoAAAD/gkpqaqq+/fZbnT17VvXr15ck/fjjjypTpoxatmzpXc9mswWmSgAAUCr5FVR69eql8uXL64033lClSpUknfsSuLvuukt/+MMf9NBDDwW0SAAAUDr5dY3Kc889p0mTJnlDiiRVqlRJEyZM4FM/AAAgYPwKKhkZGfrll1/yLD906JBOnjxZ7KIAAAAkP4NK3759ddddd+m///2v9u3bp3379um///2vhg4dqn79+gW6RgAAUEr5dY3K9OnTNWbMGA0cOFAul+vcQKGhGjp0qKZMmRLQAgEAQOnlV1CJiIjQyy+/rClTpuinn36SMUZXX321ypYtG+j6AABAKVasL3xLT09Xenq66tWrp7Jly8oYE6i6AAAA/AsqR48eVefOnVWvXj11795d6enpkqS7776bjyYDAICA8SuoPPjgg7Lb7dqzZ48iIiK8y/v376+lS5cGrDgAAFC6+XWNyrJly/Tpp5+qRo0aPsvr1q2r3bt3B6QwAAAAv86onDp1yudMiseRI0fkcDiKXRQAAIDkZ1Dp0KGD3nzzTe9tm80mt9utKVOmqFOnTgErDgAAlG5+vfUzZcoUJSUl6ZtvvtGZM2f08MMPa+vWrTp27Ji+/PLLQNcIAABKKb/OqDRq1Ejfffedrr32WnXp0kWnTp1Sv379lJqaqquuuirQNQIAgFKqyGdUXC6XunbtqldeeUXjx48viZoAAAAk+XFGxW63a8uWLbLZbCVRDwAAgJdfb/0MGjRIM2fODHQtAAAAPvy6mPbMmTN67bXXlJKSotatW+f5Gz9Tp04NSHEAAKB0K1JQ+fnnn1WrVi1t2bJFLVu2lCT9+OOPPusU5S2hadOmadq0adq1a5ckqXHjxvrHP/6hbt26FaUsAABwhSpSUKlbt67S09O1cuVKSee+Mv/f//63qlat6teT16hRQ5MnT9bVV18tSXrjjTfUu3dvpaamqnHjxn6NCQAArhxFCirn/3XkTz75RKdOnfL7yXv16uVz++mnn9a0adO0fv16ggoAAPDvGhWP84NLceTk5GjBggU6deqU2rZtm+862dnZys7O9t7OyMiQdO4j0y6XK2C1SJLb7T73X6dTxRnZLckpadu2bd4x/RUVFZXn7yv5Y9++fTp69OhF1/PUm5qaqpCQvNddZ2dnB+RPJgRinLS0NDmdTrmlgLxebrfbZ7sq6vbldrsDUo889RRznPPnVVSexwRqXoHaLwL9ul+onovtDx5W2i+kwBw3vK+70ylJcv3+X39YYXv2juPn9uzKpw+BmFegxyrJXvt7bDz/8YVhM0VIG2XKlNHBgwdVpUoVSVL58uX13XffqXbt2kWv8nfff/+92rZtq9OnT6tcuXKaO3euunfvnu+648aNy/e7W+bOnZvv3x4CAADWk5mZqQEDBujEiROKjIy84LpFCiohISHq1q2bN+V/+OGHuuGGG/J86mfhwoWFLvbMmTPas2ePjh8/rvfee0+vvfaaVq9erUaNGuVZN78zKvHx8Tpy5MhFJ1pUqampSk9PV2xyslpkZfk9zruS7pE0Q1L9YtST9vs4a9asUWJiot/jbN68WR06dChUPW6nU+mzZik2OVkh5/VgmaQJKv68Aj3OGkn+d0faLKmD/tdnl8ullJQUdenSRXa7vfDj/N7n4tbj2X4CPa+i8vQhNjZWSUlJAZuXVV73wtRzof3h/Hqssl8E+rix6vcedElOlt2P46JVtmfvOH7upy6nUym5+hCoeUmB61FJ99rfY6NHRkaGoqOjCxVUivTWz+DBg31uDxw4sMjFnS8sLMx7MW3r1q21YcMGvfDCC3rllVfyrOtwOPI9FWq32/1q1IV4Tu2GZGX5tUPmliWpoaSWxann93FCQkKKNdeQkBBlZWUVqh6XpHRJLfLpQZoCM69AjxMiqThbQkF9Luo25ulzceuRSnZeRR4nwPOyyutemHoutD+cX49V9ouSeN0lyV6M4+KVtD3n7kOgtsNAjnUpeu3v79+iPKZIQWX27NlFLqaojDE+Z00AAEDpVayLaYvrscceU7du3RQfH6+TJ09q3rx5WrVqlZYuXRrMsgAAgEUENaj88ssvuvPOO5Wenq4KFSqoWbNmWrp0qbp06RLMsgAAgEUENajw94IAAMCF+PVHCQEAAC4FggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALAsggoAALCsoAaVSZMm6ZprrlH58uUVExOjPn36KC0tLZglAQAACwlqUFm9erWGDx+u9evXKyUlRWfPnlXXrl116tSpYJYFAAAsIjSYT7506VKf27Nnz1ZMTIw2btyoDh06BKkqAABgFUENKuc7ceKEJKly5cr53p+dna3s7Gzv7YyMDEmSy+WSy+UKaC1ut/vcf51OFXdkpyS3VKxx3L+Ps23bNm9t/khLS5PT6SxUPS6n0+e/5wvEvKw2jqfPbrfbZ7sq6vbldrsL3eeLscL243lMUbafi7HS616YcS62P1zqegoj4MeNQvbgQiw5ryLWk9+2EKjXPZBjlcQx0cPfY+P5jy8MmzHG+PUsAWaMUe/evfXrr7/q888/z3edcePGafz48XmWz507VxERESVdIgAACIDMzEwNGDBAJ06cUGRk5AXXtUxQGT58uD766CN98cUXqlGjRr7r5HdGJT4+XkeOHLnoRIsqNTVV6enpik1OVousLL/HeVfSPZLWSEosRj2ecWZIql+McZZJmlDIelxOp1JmzVKX5GTZz+tBoOdllXE2S+ogac2aNUpMTJTL5VJKSoq6dOkiu91e+HE2b1aHDh0sM6/ibj9up1Pps2ZpW3KynszKsty8LsU4F9ofglFPUcYJ1HFj1e/bwYV6UJh6rDavotZz/rYQqHlJ1tuGzj8mevh7bPTIyMhQdHR0oYKKJd76GTFihBYvXqw1a9YUGFIkyeFwyOFw5Flut9v9atSFhIScu844JCvLrx0ytyydu2q5uBVmSWooqWUxxkjzox57AT0I5LysMk6IZ5yQEJ9tqqjbWEhIiLKysiwzL884/m4/LknpkuKzsiw5r0s5TkH7Q7DqKcw4gTxuSBfvwcXqseK8/Kkndx8CNa9AjlWSx0QPf3//FuUxQQ0qxhiNGDFC77//vlatWqXatWsHsxwAAGAxQQ0qw4cP19y5c/XBBx+ofPnyOnjwoCSpQoUKchbjYi0AAHBlCOr3qEybNk0nTpxQUlKSYmNjvT/z588PZlkAAMAigv7WDwAAQEH4Wz8AAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyghpU1qxZo169eikuLk42m02LFi0KZjkAAMBighpUTp06pcTERP3nP/8JZhkAAMCiQoP55N26dVO3bt2CWQIAALCwoAaVosrOzlZ2drb3dkZGhiTJ5XLJ5XIF9Lncbve5/zqdKu7ITklu6bIbx+V0+vw32PVcinHcv4+zbds2ud1u73aQmpqqkJDCn4BMS0uT0+m0zLyKO453G3A6LVFPMMa52P5wqesJyjiF7MElqydI4+S3LQSqnkCOFchjotvt9vk96/l/f3/3FuVxNmOM8etZAsxms+n9999Xnz59Clxn3LhxGj9+fJ7lc+fOVURERAlWBwAAAiUzM1MDBgzQiRMnFBkZecF1L6ugkt8Zlfj4eB05cuSiEy2q1NRUpaenKzY5WS2ysvwe511J90haIymxGPUEYxyX06mUWbPUJTlZ9vN6cDnPqzDjzJBUX+f+BZk+a5Zik5MVUoTtYJmkCQGsJ9jjeLaF35KTdU9WVtDrCcY4F9ofglFPMMZZ9fv+cKEeXMp6gjXO+dtCoOopTk0lNc5mSR107sMviYn/G8nlciklJUVdunSR3W4v8rgZGRmKjo4uVFC5rN76cTgccjgceZbb7Xa/GnUhntP8IVlZfu2QuWXp3FXLxa0wWOPYC+jB5T6vC43TUFJLnTtlmi6pRRG3g7QA12OZcbKylJWVZZ16gjBOQftDsOq51ONIF+/BpawnmOPk7kOg6gnkWIEYJ8QzTkhIvr9n/f39W5TH8D0qAADAsoJ6RuW3337Tjh07vLd37typTZs2qXLlyqpZs2YQKwMAAFYQ1KDyzTffqFOnTt7bo0ePliQNHjxYr7/+epCqAgAAVhHUoJKUlCSLXMsLAAAsiGtUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZQU9qLz88suqXbu2wsPD1apVK33++efBLgkAAFhEUIPK/PnzNWrUKD3++ONKTU3VH/7wB3Xr1k179uwJZlkAAMAighpUpk6dqqFDh+ruu+9Ww4YN9fzzzys+Pl7Tpk0LZlkAAMAighZUzpw5o40bN6pr164+y7t27aq1a9cGqSoAAGAlocF64iNHjignJ0dVq1b1WV61alUdPHgw38dkZ2crOzvbe/vEiROSpGPHjsnlcgW0voyMDGVmZio1PFy/GeP3OGmSwiVtlJRRjHqCMY47PFyZmZn6PDxcIef14HKeV1HGuVAPglFPsMbx9GF7eLjCjQl6PcEYpzDbwuU4r6KMkxoernJ+7A8lVU+wxjl/WwhUPcWpqaTG2f77OBkZGTp69Kh3ucvlUmZmpo4ePSq73V7kcU+ePClJMoXZjkyQ7N+/30gya9eu9Vk+YcIEU79+/XwfM3bsWCOJH3744Ycffvi5An727t170bwQtDMq0dHRKlOmTJ6zJ4cOHcpzlsXj0Ucf1ejRo7233W63jh07pqioKNlstoDWl5GRofj4eO3du1eRkZEBHftyQQ/ogQd9oAcSPfCgD8XvgTFGJ0+eVFxc3EXXDVpQCQsLU6tWrZSSkqK+fft6l6ekpKh37975PsbhcMjhcPgsq1ixYkmWqcjIyFK7IXrQA3rgQR/ogUQPPOhD8XpQoUKFQq0XtKAiSaNHj9add96p1q1bq23btnr11Ve1Z88e/fnPfw5mWQAAwCKCGlT69++vo0eP6sknn1R6erqaNGmijz/+WAkJCcEsCwAAWERQg4okDRs2TMOGDQt2GXk4HA6NHTs2z1tNpQk9oAce9IEeSPTAgz5c2h7YjCnGZ28BAABKUND/1g8AAEBBCCoAAMCyCCoAAMCyCCoAAMCySnVQWbNmjXr16qW4uDjZbDYtWrTI535jjMaNG6e4uDg5nU4lJSVp69atwSm2hEyaNEnXXHONypcvr5iYGPXp00dpaWk+65SGPkybNk3NmjXzfnlR27Zt9cknn3jvLw09ON+kSZNks9k0atQo77IrvQ/jxo2TzWbz+alWrZr3/it9/rnt379fAwcOVFRUlCIiItS8eXNt3LjRe/+V3otatWrl2RZsNpuGDx8u6cqfvySdPXtWf//731W7dm05nU7VqVNHTz75pNxut3edS9IH//9az+Xv448/No8//rh57733jCTz/vvv+9w/efJkU758efPee++Z77//3vTv39/ExsaajIyM4BRcAm666SYze/Zss2XLFrNp0ybTo0cPU7NmTfPbb7951ykNfVi8eLH56KOPTFpamklLSzOPPfaYsdvtZsuWLcaY0tGD3L7++mtTq1Yt06xZMzNy5Ejv8iu9D2PHjjWNGzc26enp3p9Dhw5577/S5+9x7Ngxk5CQYIYMGWK++uors3PnTrN8+XKzY8cO7zpXei8OHTrksx2kpKQYSWblypXGmCt//sac+9t7UVFRZsmSJWbnzp1mwYIFply5cub555/3rnMp+lCqg0pu5wcVt9ttqlWrZiZPnuxddvr0aVOhQgUzffr0IFR4aRw6dMhIMqtXrzbGlN4+GGNMpUqVzGuvvVbqenDy5ElTt25dk5KSYjp27OgNKqWhD2PHjjWJiYn53lca5u/xyCOPmOuvv77A+0tTLzxGjhxprrrqKuN2u0vN/Hv06GGSk5N9lvXr188MHDjQGHPptoNS/dbPhezcuVMHDx5U165dvcscDoc6duyotWvXBrGyknXixAlJUuXKlSWVzj7k5ORo3rx5OnXqlNq2bVvqejB8+HD16NFDN954o8/y0tKH7du3Ky4uTrVr19af/vQn/fzzz5JKz/wlafHixWrdurVuvfVWxcTEqEWLFpoxY4b3/tLUC0k6c+aM5syZo+TkZNlstlIz/+uvv16fffaZfvzxR0nS5s2b9cUXX6h79+6SLt12EPRvprUqz191Pv8vOVetWlW7d+8ORkklzhij0aNH6/rrr1eTJk0kla4+fP/992rbtq1Onz6tcuXK6f3331ejRo28O1xp6MG8efP07bffasOGDXnuKw3bQps2bfTmm2+qXr16+uWXXzRhwgS1a9dOW7duLRXz9/j55581bdo0jR49Wo899pi+/vprPfDAA3I4HBo0aFCp6oUkLVq0SMePH9eQIUMklY59QZIeeeQRnThxQg0aNFCZMmWUk5Ojp59+WrfffrukS9cHgspF2Gw2n9vGmDzLrhT333+/vvvuO33xxRd57isNfahfv742bdqk48eP67333tPgwYO1evVq7/1Xeg/27t2rkSNHatmyZQoPDy9wvSu5D926dfP+f9OmTdW2bVtdddVVeuONN3TddddJurLn7+F2u9W6dWtNnDhRktSiRQtt3bpV06ZN06BBg7zrlYZeSNLMmTPVrVs3xcXF+Sy/0uc/f/58zZkzR3PnzlXjxo21adMmjRo1SnFxcRo8eLB3vZLuA2/9FMBzpb8nMXocOnQoT3q8EowYMUKLFy/WypUrVaNGDe/y0tSHsLAwXX311WrdurUmTZqkxMREvfDCC6WmBxs3btShQ4fUqlUrhYaGKjQ0VKtXr9a///1vhYaGeud6pfcht7Jly6pp06bavn17qdkOJCk2NlaNGjXyWdawYUPt2bNHUuk6LuzevVvLly/X3Xff7V1WWub/17/+VX/729/0pz/9SU2bNtWdd96pBx98UJMmTZJ06fpAUClA7dq1Va1aNaWkpHiXnTlzRqtXr1a7du2CWFlgGWN0//33a+HChVqxYoVq167tc39p6UN+jDHKzs4uNT3o3Lmzvv/+e23atMn707p1a91xxx3atGmT6tSpUyr6kFt2dra2bdum2NjYUrMdSFL79u3zfE3Bjz/+6P3L9qWpF7Nnz1ZMTIx69OjhXVZa5p+ZmamQEN+YUKZMGe/Hky9ZHwJ2We5l6OTJkyY1NdWkpqYaSWbq1KkmNTXV7N692xhz7mNXFSpUMAsXLjTff/+9uf3226+4j5/95S9/MRUqVDCrVq3y+SheZmamd53S0IdHH33UrFmzxuzcudN899135rHHHjMhISFm2bJlxpjS0YP85P7UjzFXfh8eeughs2rVKvPzzz+b9evXm549e5ry5cubXbt2GWOu/Pl7fP311yY0NNQ8/fTTZvv27ebtt982ERERZs6cOd51SkMvcnJyTM2aNc0jjzyS577SMP/Bgweb6tWrez+evHDhQhMdHW0efvhh7zqXog+lOqisXLnSSMrzM3jwYGPMuY9ejR071lSrVs04HA7ToUMH8/333we36ADLb/6SzOzZs73rlIY+JCcnm4SEBBMWFmaqVKliOnfu7A0pxpSOHuTn/KBypffB8x0QdrvdxMXFmX79+pmtW7d677/S55/bhx9+aJo0aWIcDodp0KCBefXVV33uLw29+PTTT40kk5aWlue+0jD/jIwMM3LkSFOzZk0THh5u6tSpYx5//HGTnZ3tXedS9MFmjDGBOz8DAAAQOFyjAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAsBykpKSNGrUqGCXAcACCCoAAqpXr1668cYb871v3bp1stls+vbbby9xVQAuVwQVAAE1dOhQrVixQrt3785z36xZs9S8eXO1bNkyCJUBuBwRVAAEVM+ePRUTE6PXX3/dZ3lmZqbmz5+vPn366Pbbb1eNGjUUERGhpk2b6p133rngmDabTYsWLfJZVrFiRZ/n2L9/v/r3769KlSopKipKvXv31q5duwIzKQBBQ1ABEFChoaEaNGiQXn/9deX+U2ILFizQmTNndPfdd6tVq1ZasmSJtmzZonvvvVd33nmnvvrqK7+fMzMzU506dVK5cuW0Zs0affHFFypXrpxuvvlmnTlzJhDTAhAkBBUAAZecnKxdu3Zp1apV3mWzZs1Sv379VL16dY0ZM0bNmzdXnTp1NGLECN10001asGCB3883b948hYSE6LXXXlPTpk3VsGFDzZ49W3v27PGpAcDlJzTYBQC48jRo0EDt2rXTrFmz1KlTJ/3000/6/PPPtWzZMuXk5Gjy5MmaP3++9u/fr+zsbGVnZ6ts2bJ+P9/GjRu1Y8cOlS9f3mf56dOn9dNPPxV3OgCCiKACoEQMHTpU999/v1566SXNnj1bCQkJ6ty5s6ZMmaJ//etfev7559W0aVOVLVtWo0aNuuBbNDabzedtJElyuVze/3e73WrVqpXefvvtPI+tUqVK4CYF4JIjqAAoEbfddptGjhypuXPn6o033tA999wjm82mzz//XL1799bAgQMlnQsZ27dvV8OGDQscq0qVKkpPT/fe3r59uzIzM723W7Zsqfnz5ysmJkaRkZElNykAlxzXqAAoEeXKlVP//v312GOP6cCBAxoyZIgk6eqrr1ZKSorWrl2rbdu26b777tPBgwcvONYNN9yg//znP/r222/1zTff6M9//rPsdrv3/jvuuEPR0dHq3bu3Pv/8c+3cuVOrV6/WyJEjtW/fvpKcJoASRlABUGKGDh2qX3/9VTfeeKNq1qwpSXriiSfUsmVL3XTTTUpKSlK1atXUp0+fC47z3HPPKT4+Xh06dNCAAQM0ZswYRUREeO+PiIjQmjVrVLNmTfXr108NGzZUcnKysrKyOMMCXOZs5vw3fgEAACyCMyoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCyCCoAAMCy/j999XGb/1llmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 2\n",
    "\n",
    "import random as rnd\n",
    "x = [\n",
    "    rnd.randint(10, 80)\n",
    "    for i in range(0, 100)\n",
    "]\n",
    "print(x)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x, bins=25, color='red', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Random Data by Resna Febrianto')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fb8edfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EiQEAAFaixAAAACslxXoAAM7LC+xSsNMT6zGuiTfRaO1XYz0FABtwJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWInf2AsAuCHY/JusJX6bdU+4EgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWMnxEnP+/Hn95Cc/UU5Ojvr376+bb75ZTz/9tMLhcOQYY4wCgYD8fr/69++vgoICHT161OlRAACAizleYtasWaOXXnpJlZWVeu+997R27Vr97Gc/U0VFReSYtWvXat26daqsrFRdXZ18Pp8KCwt15swZp8cBAAAu5XiJ+e///m/de++9uueee/TlL39Z3/rWt1RUVKQDBw5I+uwqTHl5uVasWKFZs2YpLy9PmzdvVnt7u6qqqpweBwAAuJTj7500YcIEvfTSSzp27JhuueUWvf3229q/f7/Ky8slSfX19WpsbFRRUVHkMV6vVxMnTlRtba0WLlzY7ZzBYFDBYDByu7W19bPHJRglJhqnI1w33gQT9aet3JDDDRkkd+RwQwbJfTlCoVCMJ7l2XbO75XNhc46ksLOze4wxjp7RGKMf//jHWrNmjRITE9XZ2alnn31WpaWlkqTa2lqNHz9eDQ0N8vv9kcc98sgjOnHihHbt2tXtnIFAQKtWreq2XlVVpdTUVCfHBwAAfaS9vV1z5sxRS0uLBg4c+IXP5/iVmF/+8pfasmWLqqqqNHLkSB0+fFjFxcXy+/2aP39+5DiPJ/qdRI0x3da6lJaWqqSkJHK7tbVV2dnZ+ulbCTqfnOh0hOvGm2D0zNiwVh5IUDBs8TuruiCHGzJI7sjhhgyS+3IUFhYqOTk51uNck1AopOrqatd8LmzOkRRy9lksjpeYxx9/XE8++aQeeOABSdKoUaN04sQJrV69WvPnz5fP55MkNTY2aujQoZHHNTU1KSsrq8dzer1eeb3ebuvBsEfnLX5b9S7BsMfqt4fv4oYcbsgguSOHGzJI7smRnJxsbYnp4pbPhc05Oh0uX44/sbe9vV0JCdGnTUxMjLzEOicnRz6fT9XV1ZH7Ozo6VFNTo/z8fKfHAQAALuX4lZgZM2bo2Wef1fDhwzVy5Ei99dZbWrdunb73ve9J+uzHSMXFxSorK1Nubq5yc3NVVlam1NRUzZkzx+lxAACASzleYioqKrRy5UotWrRITU1N8vv9Wrhwof7xH/8xcszy5ct17tw5LVq0SM3NzRo3bpx2796ttLQ0p8cBAAAu5XiJSUtLU3l5eeQl1T3xeDwKBAIKBAJO//UAAOAGwXsnAQAAKzl+JQYA4D55gV3WviLGm2i09quxngJ9gSsxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFbqkxLT0NCghx56SJmZmUpNTdUdd9yhgwcPRu43xigQCMjv96t///4qKCjQ0aNH+2IUAADgUo6XmObmZo0fP17Jycl69dVX9ac//UnPP/+8brrppsgxa9eu1bp161RZWam6ujr5fD4VFhbqzJkzTo8DAABcKsnpE65Zs0bZ2dnauHFjZO3LX/5y5H8bY1ReXq4VK1Zo1qxZkqTNmzcrKytLVVVVWrhwodMjAQAAF3K8xOzcuVNTp07V/fffr5qaGn3pS1/SokWL9IMf/ECSVF9fr8bGRhUVFUUe4/V6NXHiRNXW1vZYYoLBoILBYOR2a2vrZ49LMEpMNE5HuG68CSbqT1u5IYcbMkjuyOGGDBI54okbMkjuyJEUdnZ2jzHG0TOmpKRIkkpKSnT//ffrzTffVHFxsX7+859r3rx5qq2t1fjx49XQ0CC/3x953COPPKITJ05o165d3c4ZCAS0atWqbutVVVVKTU11cnwAANBH2tvbNWfOHLW0tGjgwIFf+HyOX4kJh8MaO3asysrKJEl33nmnjh49qhdffFHz5s2LHOfxeKIeZ4zpttaltLRUJSUlkdutra3Kzs7WT99K0PnkRKcjXDfeBKNnxoa18kCCguGes9vADTnckEFyRw43ZJDIEU/ckEFyR46kkLNPxXW8xAwdOlR/+7d/G7V222236T//8z8lST6fT5LU2NiooUOHRo5pampSVlZWj+f0er3yer3d1oNhj8532vmJvFAw7FGQHHHBDRkkd+RwQwaJHPHEDRkku3N0Oly+HH910vjx4/X+++9HrR07dkwjRoyQJOXk5Mjn86m6ujpyf0dHh2pqapSfn+/0OAAAwKUcvxLz6KOPKj8/X2VlZZo9e7befPNNbdiwQRs2bJD02Y+RiouLVVZWptzcXOXm5qqsrEypqamaM2eO0+MAAACXcrzE3HXXXdq+fbtKS0v19NNPKycnR+Xl5XrwwQcjxyxfvlznzp3TokWL1NzcrHHjxmn37t1KS0tzehwAAOBSjpcYSZo+fbqmT59+yfs9Ho8CgYACgUBf/PUAAOAGwHsnAQAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFbq8xKzevVqeTweFRcXR9aMMQoEAvL7/erfv78KCgp09OjRvh4FAAC4SJ+WmLq6Om3YsEG333571PratWu1bt06VVZWqq6uTj6fT4WFhTpz5kxfjgMAAFykz0pMW1ubHnzwQf3iF7/QoEGDIuvGGJWXl2vFihWaNWuW8vLytHnzZrW3t6uqqqqvxgEAAC6T1FcnXrx4se655x5NmTJFP/3pTyPr9fX1amxsVFFRUWTN6/Vq4sSJqq2t1cKFC7udKxgMKhgMRm63trZ+9rgEo8RE01cR+pw3wUT9aSs35HBDBskdOdyQQSJHPHFDBskdOZLCzs7eJyVm69atOnTokOrq6rrd19jYKEnKysqKWs/KytKJEyd6PN/q1au1atWqbus/uTOs1NROByaOrWfGhmM9giPckMMNGSR35HBDBokc8cQNGSS7c7S3hzXHwfM5XmI++eQT/ehHP9Lu3buVkpJyyeM8Hk/UbWNMt7UupaWlKikpidxubW1Vdna2fvpWgs4nJzozeAx4E4yeGRvWygMJCoZ7zm4DN+RwQwbJHTnckEEiRzxxQwbJHTmSQs4+i8XxEnPw4EE1NTVpzJgxkbXOzk7t27dPlZWVev/99yV9dkVm6NChkWOampq6XZ3p4vV65fV6u60Hwx6d77TzE3mhYNijIDnighsySO7I4YYMEjniiRsySHbn6HS4fDn+xN7JkyfrnXfe0eHDhyMfY8eO1YMPPqjDhw/r5ptvls/nU3V1deQxHR0dqqmpUX5+vtPjAAAAl3L8SkxaWpry8vKi1gYMGKDMzMzIenFxscrKypSbm6vc3FyVlZUpNTVVc+Y4+ZMyAADgZn326qTLWb58uc6dO6dFixapublZ48aN0+7du5WWlhaLcQAAgIWuS4nZu3dv1G2Px6NAIKBAIHA9/noAAOBCvHcSAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCXHS8zq1at11113KS0tTUOGDNF9992n999/P+oYY4wCgYD8fr/69++vgoICHT161OlRAACAizleYmpqarR48WK98cYbqq6u1vnz51VUVKSzZ89Gjlm7dq3WrVunyspK1dXVyefzqbCwUGfOnHF6HAAA4FJJTp/wd7/7XdTtjRs3asiQITp48KC+8Y1vyBij8vJyrVixQrNmzZIkbd68WVlZWaqqqtLChQudHgkAALiQ4yXmYi0tLZKkjIwMSVJ9fb0aGxtVVFQUOcbr9WrixImqra3tscQEg0EFg8HI7dbW1s8el2CUmGj6cvw+5U0wUX/ayg053JBBckcON2SQyBFP3JBBckeOpLCzs3uMMX32/4YxRvfee6+am5v1+uuvS5Jqa2s1fvx4NTQ0yO/3R4595JFHdOLECe3atavbeQKBgFatWtVtvaqqSqmpqX01PgAAcFB7e7vmzJmjlpYWDRw48Aufr0+vxCxZskRHjhzR/v37u93n8Xiibhtjuq11KS0tVUlJSeR2a2ursrOz9dO3EnQ+OdHZoa8jb4LRM2PDWnkgQcFwz9lt4IYcbsgguSOHGzJI5IgnbsgguSNHUsjZp+L2WYlZunSpdu7cqX379mnYsGGRdZ/PJ0lqbGzU0KFDI+tNTU3Kysrq8Vxer1der7fbejDs0flOOz+RFwqGPQqSIy64IYPkjhxuyCCRI564IYNkd45Oh8uX469OMsZoyZIl2rZtm37/+98rJycn6v6cnBz5fD5VV1dH1jo6OlRTU6P8/HynxwEAAC7l+JWYxYsXq6qqSr/61a+UlpamxsZGSVJ6err69+8vj8ej4uJilZWVKTc3V7m5uSorK1NqaqrmzJnj9DgAAMClHC8xL774oiSpoKAgan3jxo367ne/K0lavny5zp07p0WLFqm5uVnjxo3T7t27lZaW5vQ4AADApRwvMVfzYiePx6NAIKBAIOD0Xw8AAG4QvHcSAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYKWYlpgXXnhBOTk5SklJ0ZgxY/T666/HchwAAGCRmJWYX/7ylyouLtaKFSv01ltv6etf/7qmTZumkydPxmokAABgkZiVmHXr1mnBggX6/ve/r9tuu03l5eXKzs7Wiy++GKuRAACARZJi8Zd2dHTo4MGDevLJJ6PWi4qKVFtb2+34YDCoYDAYud3S0iJJSgqd7dtB+1hS2Ki9PaykUII6w55Yj3PN3JDDDRkkd+RwQwaJHPHEDRkkd+To+r5tjHHmhCYGGhoajCTzhz/8IWr92WefNbfccku345966ikjiQ8++OCDDz74cMHHhx9+6EifiMmVmC4eT3STNMZ0W5Ok0tJSlZSURG7/3//9n0aMGKGTJ08qPT29z+fsK62trcrOztYnn3yigQMHxnqca+aGHG7IILkjhxsySOSIJ27IILkjR0tLi4YPH66MjAxHzheTEjN48GAlJiaqsbExar2pqUlZWVndjvd6vfJ6vd3W09PTrf1EXmjgwIHkiBNuyCC5I4cbMkjkiCduyCC5I0dCgjNPyY3JE3v79eunMWPGqLq6Omq9urpa+fn5sRgJAABYJmY/TiopKdHcuXM1duxY3X333dqwYYNOnjypH/7wh7EaCQAAWCRmJebb3/62/vznP+vpp5/W6dOnlZeXp9/+9rcaMWLEFR/r9Xr11FNP9fgjJpuQI364IYPkjhxuyCCRI564IYPkjhxOZ/AY49TrnAAAAK4f3jsJAABYiRIDAACsRIkBAABWosQAAAArxW2JWb16te666y6lpaVpyJAhuu+++/T+++9HHWOMUSAQkN/vV//+/VVQUKCjR4/GaOKeXU2OCy1cuFAej0fl5eXXb8ircDU52tratGTJEg0bNkz9+/fXbbfdFldv6Pniiy/q9ttvj/yiqLvvvluvvvqqJCkUCumJJ57QqFGjNGDAAPn9fs2bN0+nTp2K8dTdXS5Hl/fee08zZ85Uenq60tLS9LWvfS2u3yF+9erV8ng8Ki4ujqzZsL8v1lOOC8Xr/r5QTxnifW9LUiAQkMfjifrw+XyS7Nrfl8vRxYb93dDQoIceekiZmZlKTU3VHXfcoYMHD0bud2p/x22Jqamp0eLFi/XGG2+ourpa58+fV1FRkc6e/fxNH9euXat169apsrJSdXV18vl8Kiws1JkzZ2I4ebSrydFlx44d+uMf/yi/3x+DSS/vanI8+uij+t3vfqctW7bovffe06OPPqqlS5fqV7/6VQwn/9ywYcP03HPP6cCBAzpw4IC++c1v6t5779XRo0fV3t6uQ4cOaeXKlTp06JC2bdumY8eOaebMmbEeu5vL5ZCkDz/8UBMmTNCtt96qvXv36u2339bKlSuVkpIS48l7VldXpw0bNuj222+PWrdhf1/oUjm6xPP+7nKpDPG+t7uMHDlSp0+fjny88847kmTV/pYunUOyY383Nzdr/PjxSk5O1quvvqo//elPev7553XTTTdFjnFsfzvyDkzXQVNTk5FkampqjDHGhMNh4/P5zHPPPRc55i9/+YtJT083L730UqzGvKKLc3T59NNPzZe+9CXz7rvvmhEjRpj169fHZsCr1FOOkSNHmqeffjrquL/7u78zP/nJT673eFdt0KBB5l/+5V96vO/NN980ksyJEyeu81S9d2GOb3/72+ahhx6K8URX58yZMyY3N9dUV1ebiRMnmh/96EfGGPv296VydLFhf18ugw17+6mnnjKjR4++6uPjdX9fKYcN+/uJJ54wEyZMuOT9Tu7vuL0Sc7GWlhZJirxpVH19vRobG1VUVBQ5xuv1auLEiaqtrY3JjFfj4hySFA6HNXfuXD3++OMaOXJkrEbrlZ5yTJgwQTt37lRDQ4OMMdqzZ4+OHTumqVOnxmrMS+rs7NTWrVt19uxZ3X333T0e09LSIo/HE/Wvh3hzcY5wOKzf/OY3uuWWWzR16lQNGTJE48aN044dO2I9ao8WL16se+65R1OmTIlat21/XyqHZM/+vlwGW/b28ePH5ff7lZOTowceeEAfffTRJY+N5/19qRy27O+dO3dq7Nixuv/++zVkyBDdeeed+sUvfhG539H9fY1F67oKh8NmxowZUc3uD3/4g5FkGhoaoo79wQ9+YIqKiq73iFelpxzGGFNWVmYKCwtNOBw2xpi4/Zdal0vlCAaDZt68eUaSSUpKMv369TP/+q//GqMpe3bkyBEzYMAAk5iYaNLT081vfvObHo87d+6cGTNmjHnwwQev84RX51I5Tp8+bSSZ1NRUs27dOvPWW2+Z1atXG4/HY/bu3RvjqaP9+7//u8nLyzPnzp0zxpiof/3btL8vl8MYO/b3lTLYsLd/+9vfmv/4j/8wR44ciVxNysrKMv/zP//T7dh43t+Xy2HL/vZ6vcbr9ZrS0lJz6NAh89JLL5mUlBSzefNmY4yz+9uKErNo0SIzYsQI88knn0TWuv5POHXqVNSx3//+983UqVOv94hXpaccBw4cMFlZWVGfzHj8j9yFesphjDE/+9nPzC233GJ27txp3n77bVNRUWH+6q/+ylRXV8do0u6CwaA5fvy4qaurM08++aQZPHiwOXr0aNQxHR0d5t577zV33nmnaWlpidGkl3epHA0NDUaS+c53vhN1/IwZM8wDDzwQo2m7O3nypBkyZIg5fPhwZK2nEhPv+/tKOWzY31fKYIwde/tibW1tJisryzz//PNR6zbs7wtdmMOW/Z2cnGzuvvvuqLWlS5ear33ta8YYZ/d33JeYJUuWmGHDhpmPPvooav3DDz80ksyhQ4ei1mfOnGnmzZt3PUe8KpfKsX79euPxeExiYmLkQ5JJSEgwI0aMiM2wl3GpHO3t7SY5Odn8+te/jlpfsGBBXH3TudjkyZPNI488Ernd0dFh7rvvPnP77bf3+C+4eNWVIxgMmqSkJPPMM89E3b98+XKTn58fo+m62759u5HU7eu+ay988MEHVuzvK+X4p3/6p7jf31fK0NbWZuXeNsaYKVOmmB/+8IeR27bu764ctuzv4cOHmwULFkStvfDCC8bv9xtjnP3+HbM3gLwSY4yWLl2q7du3a+/evcrJyYm6PycnRz6fT9XV1brzzjslSR0dHaqpqdGaNWtiMXKPrpRj7ty53X4GPXXqVM2dO1cPP/zw9Rz1sq6UIxQKKRQKKSEh+mlWiYmJCofD13PUXjHGKBgMSvosw+zZs3X8+HHt2bNHmZmZMZ7u6nXl6Nevn+66665uL38/duzYVb256vUyefLkqFdcSNLDDz+sW2+9VU888YRuvvlmK/b3lXIMHTq02/NG4m1/XylDZ2enlXs7GAzqvffe09e//nVJ9u7vC3PYsr/Hjx9/2Rkd/f59zVWrj/393/+9SU9PN3v37jWnT5+OfLS3t0eOee6550x6errZtm2beeedd8x3vvMdM3ToUNPa2hrDyaNdTY6LxdvlZmOuLsfEiRPNyJEjzZ49e8xHH31kNm7caFJSUswLL7wQw8k/V1paavbt22fq6+vNkSNHzI9//GOTkJBgdu/ebUKhkJk5c6YZNmyYOXz4cFTGYDAY69GjXC6HMcZs27bNJCcnmw0bNpjjx4+biooKk5iYaF5//fUYT355F/8Iw4b93ZOeXp10oXjc3xe7OEO8721jjFm2bJnZu3ev+eijj8wbb7xhpk+fbtLS0szHH39s1f6+XA5j7Njfb775pklKSjLPPvusOX78uPm3f/s3k5qaarZs2RI5xqn9HbclRlKPHxs3bowcEw6HzVNPPWV8Pp/xer3mG9/4hnnnnXdiN3QPribHxeLxP3JXk+P06dPmu9/9rvH7/SYlJcV85StfMc8//3zkCY2x9r3vfc+MGDHC9OvXz/z1X/+1mTx5cuQbf319/SUz7tmzJ7aDX+RyObq8/PLL5m/+5m9MSkqKGT16tNmxY0eMpr16F3/jtGF/98SNJSbe97Yxn730eOjQoSY5Odn4/X4za9asyPPdbNrfl8vRxYb9/V//9V8mLy/PeL1ec+utt5oNGzZE3e/U/vYYY0zvrt0AAADEnjW/JwYAAOBClBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWOn/AxJ8qVsEtxNwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 3\n",
    "\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(np.arange(20, 64, 4))\n",
    "plt.xlim([20, 60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8fa2581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 4\n",
    "\n",
    "N = 6000; a=1; b=6; dice = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    dice.append(xi)\n",
    "plt.hist(dice, bins=6)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38f4c7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 5\n",
    "\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100\")\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200\")\n",
    "\n",
    "mu = 300\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300\")\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 5\")\n",
    "\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 10\")\n",
    "\n",
    "mu = 100\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 20\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8998b5bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}