diff --git a/1_Basics_IV.ipynb b/1_Basics_IV.ipynb
index 9db1210b..8069a07b 100644
--- a/1_Basics_IV.ipynb
+++ b/1_Basics_IV.ipynb
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 2,
    "metadata": {
     "hidden": true,
     "tags": [
@@ -97,6 +97,14 @@
     ]
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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MHnvsMcyfPx+ZmZlITk6uc9pTp06hf//+KCsrQ2xsLH744QcolUoMHjwYSUlJElUsPb7KrBl4eHhg9uzZAIDs7Ow6p33ttdfw2GOPYd++fbC0tAQA+Pv7w8/PD3PmzEFaWlpzlytEi13ibd++HTKZDDt37tS1abVaBAQE4IknnkBlZaXA6u759ddfcfr0aUybNk0XOuDe/VlmzpyJM2fO4NSpUwIrbD4tNniRkZF48cUXMWfOHPz5558AgLVr1yItLQ3bt2/X+6JFOXnyJAAgMDDQYNygQYMAgJd4pmj9+vWwtLTE3LlzkZ2djQULFuDDDz+s9a6eADBlyhSYm5vXOTTVTXpu3rwJAHB1dTUY17FjRwDA77//3iR9GZsWvY3n6OiIzZs349lnn0Vqair8/f0RHR1d52sWL16M119/vc5pqm/Y86jMze99/DKZzGBcdZuA6+0lIXnwCgsLAQBFRUWS9BcaGoonn3wSJ0+exNq1a2v8ku/n5uYGNzc3SWpzd3cHAOTl5Rn0WVBQoDdNSyP5qtbR0REA4ODgIEl/O3fuRHp6Ovr374833njjoTsVUq5qn3jiCQDA8ePHDcZVH4bp169fk/RlbFr0qvbatWuYMWMGFixYgEmTJsHb2xvvvPMOli9fXutrpFzVenp6IjAwEJ9//jlmzJihW/VqtVps2LABPj4+GDBgQJP0ZXRIYqdPnyYAdPr06WbtR61W06BBg6hPnz5UWVlJRESrV68mMzMzOnLkSLP2fb+srCwCQCtXrqxx/MmTJ8na2ppGjBhBSUlJdOzYMRozZgyZm5tTYmJis9cn1ffxoBa7V7t06VL8/PPP2LRpk24JNWvWLAQEBGDSpEm4c+eO4ArvefLJJ3Hs2DFoNBqMGDECw4cPx+3bt3HkyBE888wzostrNpLfpiwtLQ2+vr44ffo03zvFCIj6PlrsEo8ZNw4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHhMCA4eE0LYVWYZGRmium4yRUVFkl2m2VxEfQ+SB0+pVAK4d28TZjyqvxepSH6xD3Dv8fBSPxG6qVU/2bolPGlcqVRK+lh4QNCqVuo32Zz4SeONwzsXTAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TIgW/djQppaTk4OrV68CAC5fvgwAOHXqFO7cuQMzMzMMHjxYZHkmRch1tabq4MGDGD58eI3jBg4cWOMDj1nNeFXbAEFBQbC1ta1x3MiRIyWuxrRx8BrAysoKISEhNY4LDw+XuBrTxsFroJoC1rlzZ3h7ewuoxnRx8BpoxIgRkMlkem3h4eEGbaxuHLwGat++Pfz8/PTaeDXbcBy8Rrg/aHZ2dhgyZIjAakwTB68R7t+DDQkJgZWVlcBqTBMHrxH69OmDTp06AeDVbGNx8BpBJpPpdijCwsJEl2OS+CezRgoPD0daWhrat28vuhSTJCR4LeFWtG3atMGgQYOQlpYmupRHJuJWtJL/VpuZmQlPT08pu2T1cPnyZUnDJ/kSr3pJ1xJuWt0SVN9EXOo1kLBtPL5pdevGe7VMCA4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHhMCA4eE4KDx4Tg4DEhOHgScnNzg0wmq3EoLy8XXZ6k+CoziVRWVuLGjRv4+OOPa7wksrVdFM7Bk8i1a9eg1WoRGBiInj17ii5HuFa1qn3hhRdgb2+P0tJSvfbdu3dDJpPhp59+ara+s7OzAYCvsPuPVhW8efPmoaSkBDExMXrtW7duRc+ePfHMM880W9/Z2dlwdnbGoUOHMGjQILRt2xaPPfYYoqOjUVxc3Gz9GqtWFTx/f3/4+/tj48aNurYbN27g4MGDmDFjRq2vmzJlCszNzescPDw86uw7OzsbN2/exIYNG/D222/j4MGDmD9/PrZv346goCDeuWhuhYWFAICioiKpuwZwb6n3wgsv4Ny5c+jTpw++/PJLWFtb4+WXX671NYsXL8brr79e53wtLCzqHB8aGgpnZ2dER0fDzOze37ufnx969uyJ0NBQbN68uc7wtzgksdOnTxMAOn36tNRdExGRWq2mLl260OzZs4mIyMvLi6ZNmyaklmpKpZImTJggpG9R30erWtUCgFwuR3R0NHbu3ImUlBScP3/+oUuapljV1kar1UKtVsPe3r5RrzdVrfJwypQpU7Bw4UJMmzYNAQEB6Nu3b53TN8WqNjY2Fn/88YfBfPbs2YO7d+82646NMWqVwVMqlZg6dSo++ugj7Nix46HTu7m5wc3N7ZH6vH79OubPn49ffvkFUVFRsLGxweHDh/H+++9j6NChGDt27CPN39S0yuABgEKhQPv27SX7wt944w14e3tj5cqVGDNmDFQqle5wyj/+8Y9Wd9f4Vhm8srIy/Otf/8Lf/vY3WFpaStbv8OHDa30kVWvTqnYuiAhbtmxBaGgoVCoVoqOjRZfUarWqJZ5MJsPKlSshl8sRFxeHDh06iC6p1WpVwQOAX375RXQJDK1sVcuMBwePCcHBY0Jw8JgQHDwmBAePCcHBY0Jw8JgQHDwmBAePCcHBY0Jw8JgQHDwmBAePCSHstKiMjAxRXTeZoqIiODg4iC7jkYj6HiQPnlKpBABERkZK3TWrQ/X3IhXJHw0P3Hs8vNRPhG5q1U+2bglPGlcqlZI+Fh4QtKqV+k02J37SeOPwzgUTgoPHhODgMSE4eEwIDh4TgoPHhODgMSE4eEwIDh4TgoPHhODgMSE4eEwIDh4TgoPHhODgMSE4eEwIDh4TgoPHhODgMSE4eEwIDh4TgoPHhGh1D1h5FHFxcVixYgUA6B7lPmPGDFhbW8PZ2Rm7d+8WWZ5J4eA1QO/evXHkyBG9thMnTgBAnY+WZ4Z4VdsAXbp0gZeXV43jwsPDJa7GtHHwGqimgFlYWCAkJERANaaLg9dANQUvKChI8pvemDoOXgP5+fnByclJr41Xsw3HwWsguVyOsLAwvTYOXsNx8Brh/qB5e3vDw8NDXDEmioPXCCEhIbCwsADAS7vG4uA1gr29PYKCggAAI0eOFFuMieLgNdLIkSPh5OQEPz8/0aWYJP7lopHCw8ORlpYGuVwuuhST1OB7ILeE+xc3lStXruDxxx8XXYbRaNC9lKkBLl++TAB44KHW4fLly/XKUoNWtdVLupZwp3PWtKrvgl/ftWGjtvH4TufsUfFeLROCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAg+EVQiVVVVOH36NP744w9UVFTAzc0N/fv3h42NjejShODgNbO8vDy8//772LZtG5RKJQoKCtClSxfcuXMHZWVlmDx5Mt5//320adNGdKmS4lVtMzp27Bj69euHX3/9FYmJibhy5Qq0Wi1WrFiB/Px87N+/HwkJCXjqqafw+++/iy5XUhy8B2RnZzfJdbIXLlzAiBEj8Morr+Do0aPw8/NDdnY2gHs3/5HJZHjmmWdw8uRJmJmZ4a9//esj92lKWnzwtm/fDplMhp07d+ratFotAgIC8MQTT6CysrLe8/r0008hk8lw9OhRvfYbN27A3Nwc7777rq5t6tSpCAoKwocffqhrO3PmDCwsLPTO3razs8M///lPHDlyBMePH2/MWzRNDbnm4vTp0wSATp8+3ZCXCffiiy+Sk5MT3bx5k4iIPvnkE7K2tqZff/3VYNqsrCzq3LlzjfMpLS0lBwcHioyM1Gtfvnw5yeVyun79OhERnThxggDQ+fPn9aabNWsW+fr6GsxXo9GQpaUlLV26tDFvzyg0NBstfokHAOvXr4elpSXmzp2L7OxsLFiwAB9++GGt97qrjY2NDV599VV8/fXXKCoq0rV/8cUXeO655+Du7g4AOHToEDw8PNC7d2+918fFxWHYsGEG8zUzM0O7du1w8+bNRrw709Qqgufo6IjNmzdj586dCA0Nhb+/P6KjowEA27Ztg7m5uW54/PHHce3aNb02c3Nz5OfnAwBmz54NjUaD7du3AwBSU1Nx/vx5zJgxQ9dffn4+XFxc9Go4ceIEsrKyMHr0aIP6qqqqUFhYCEdHx2b6BIxPqzmcEhoaiieffBInT57E2rVrIZPJAACjRo1Cenq6bro//vgDr7zyCg4ePKj3+nbt2gEAOnbsiJdeegkbN27E7Nmz8cUXX6BHjx4YOnSobloHBweDvdQ1a9agT58+Nd554ODBg6ioqMDAgQOb6u0av+ZcjxuTHTt2kKWlJfXv3598fHyooqKixunq2sardubMGQJAP//8M7Vr144++eQTvfHJyckkk8l0n1N6ejqZmZnR9u3bDeZVVFREPXv2pJ49e5JGo2ncmzMCDc1GqwhednY2tWnThhYtWkRZWVlka2tL8+fPr3Ha+gSPiOjpp5+mPn36kK2tLd2+fdtgfFBQED3xxBP03XffUffu3WnQoEGk1Wr1pklOTqZevXqRra0tnTp1qlHvzVhw8B6gVqtp0KBB1KdPH6qsrCQiotWrV5OZmRkdOXLEYPr6Bu/AgQMEgKZOnVrj+D/++IP69etHAKh///6Ul5dHREQ///wzvfvuu+Tr60sAqG/fvpSent74N2gkOHgPWLx4Mcnlcjp58qSuTavVUmBgIHXu3JmKi4sbNd/ExEQCQGfOnKl1Gq1WS9evX9db0h07dowee+wxGj9+PO3fv9+kV6/34+BJZNSoUfTUU0+JLsNoNDQbrWavtqkcPHgQMTEx2L9/P+Li4kSXY7JaxXG8prR//34kJiZi/fr1BjfhZvXHS7wG+vTTT/Hpp5+KLsPkNWiJV1hYCAB6Pxc1xu3bt3H58uU6p7l69aregd37HT58GNevX69XX5WVlfjzzz9rHX/ixAncvXu3XvNiTadBwav+ScfBwaHWaZycnCCTyfSGmJgYvWn27t370Ecwvfnmm/j8888N2u/evYvRo0fXGsoHffrppwgMDIRGo6lxfEBAAH777bc65+Hu7m7wnmobVq1aVa+6mkp+fj6sra1x9erVer+mvLwcKpWqXkNDzt5piCZf1V6+fBlarVavzd7evkHzyMrKwp49e6DRaLB+/Xq9cStXrkRxcTFGjRpl8LopU6bgX//6FwBApVKhqKgIixcvxq5du5CSkgJPT084Ozs38B3d8+WXX+r9zurl5YVly5bp3fX9mWeeqff8iouLkZaWhoKCApiZmaFbt27o27dvrfdUrqqqQkVFhUF7aWkpKioqdEF5kJWVle7RCNXGjh1b7x2jN998U+/UribTlLvMeXl5tQ4lJSW66bZs2VLnQdrQ0FB6/fXXiYjo3XffpZCQECIiunPnDrm5udH27dvp7t275OzsTDt37jR4fXl5OXXq1Il69+5Nb7zxBhUXF+um9fLyeujtVLds2aI3Pzc3N9q9e7deW+fOnWnPnj16bX5+frRy5cpa3xcR0alTpygsLIzkcjl17NiRAOh+AenYsSN98sknBr9wEBGtWbOmUbeGXbFihcG8/vzzT7p+/Xq9hvoe5xR6HK+uD+C9997TTVdX8LZt20bu7u5UVFRERESZmZkUFhZGGo2Gzpw5Q0OHDtV9MUuXLqVly5bVOJ+vv/6azM3NKTs7m+bPn0+hoaFEdC+8RUVFukEul9Px48f12h78HdfNzY3MzMxILpfrBgA1ttUVvE8//ZQsLS1pypQplJWVRd999x0BoMLCQrp79y5t2rSJ2rZtSxEREVRVVWXweo1GQ1VVVXpDbm4uAaCLFy8ajJPy4LTwA8gpKSmUlJSkN1SfgFmtruCpVCo6e/YsffnllySXy6ldu3Z1DhYWFnqhvt/bb79NzzzzDDk5OdG1a9dqnEYul9O5c+fqfN9NscT78ssvydLSkvbu3atrW7t2LbVp00ZvunPnzpGdnR29++67BvOYM2dOg5Z2M2fOrPN9NSXhB5BXr16N4uJivbY333wTixYtQmJiol579alJAPDVV1/hpZdegq2tLXx8fPDLL7/A1dVV9yj22ixfvrzWcW+//TZcXFywYcMG7N+/H46OjnjhhRcMtpXu3r2r2z6Sy+VQKBQG86qqqtI9Dh4AiKjGtprcvHkTs2bNwrJly/Dcc8/p2s+cOQMfHx+9ab29vREdHY1Vq1bhrbfe0qvlo48+Mtjeys/Ph4eHB86fP4+uXbvqjTM31/9633rrLSxbtqzGGh9m48aN+Nvf/tao19akyYO3ZMkSgz1IFxcXbNmyBaWlpQCAPXv2YO3atXpBdHNzM5iXVqutcYP5fjXtrVZUVKC4uBgLFy7EwIED8eSTT+L48eNYsmQJ/vjjD8yfP19v+gEDBuj+3bdv3xr3mCdMmGDQ9uKLLxq0jR8/3qBty5YtsLGxwezZs/XaT5w4gdDQUIPpw8LCsHTpUqSnp2PgwIHQarW6Q1kPqv5MS0tLa/2sHBwcIJfLMW/ePERFRdU4zf/+7//ixo0b2LhxY43jXV1da2xvrCYPXkREhMGHtGbNGr2Hzbm6usLCwgI9e/asc143b97EkiVL6pymoKDAoC0hIUFvb7NXr15o27YtKisrMWjQoFqXTLXJzc01aHNxccGqVavw0ksvPfT1hw4dQmhoqN4e67Vr1/Drr7/io48+Mpi+es+7+lT4nJwcdOnSpc4+7v/jeVBGRgZ69uwJZ2dn3byrqqpQWVkJW1tbAECbNm1QUlKi+04OHDiAESNG6K2VmlKT/2SWlpaG7OxsvaGxTzj09PQ0mNeDQ02HMIYMGYIPPvgAmzZtwoQJExAZGYmioiJERUXh4sWLdR7Hup+Hh0etx+vy8/Mxfvz4WsevXbtWN5+aToWPiYlBmzZt8PTTTxvUf+PGDQD/PW7q4eEBurc9rhu0Wi3ee+89uLi4YNOmTXBxcUFpaalu/PHjx3HlyhUQUY1/4J988gn69u0LtVptMO7atWt48cUXsXDhwod/SY3UpMH74YcfavwSGvOEQ4VCgVu3bqFjx44wNzeHq6sr3N3ddYOVlRWcnJyQlpYGOzs7vdfa2dkhPDwcffv2Rffu3SGXy7FlyxaMHTsWUVFRGDlyJJRKZY3D/b9ypKSk4Pr163rDunXrYG9vDxsbG/Tu3Rvdu3dHenq6wXSvvPKKbj4PngqvVquxYcMGTJo0CZaWlgbvff/+/bCysqr1WSKZmZkYNmwYvv76ayQlJWHy5Mno0aOHbvvtp59+wrBhw7B06dIaX3/t2jW8//77mDVrlsF2IAB07twZn332GRYvXoyvvvqqjm/pETTlnsv3339PHTp0oLt37+qGBQsW0IgRI/Sme9hxvPtdv36dAFBWVpZee+fOnenLL7+s9XWzZs2i2bNn03vvvUcvv/wyLV68mJ5//nkiIho6dCitWbNGb/q8vDwCYLAHfr/9+/eTQqGgb775hjp06EA7d+6kcePGkaenZ517c0uWLCFXV1dSqVREdO9EVEtLS/rtt98Mpk1LSyOFQkHTpk0zGHf27FmKiooiKysrAqB3eWZKSgopFAqaP38+WVlZ0YIFC2o8HlhRUUEBAQHk5eWld8hmypQpukNO1WbNmkU2NjZ04cKFWt9bNaO4vNHa2lo31PQXVZegoCC9peVjjz0G4L9X31cP165dw8SJE/Xa7r8DwMWLF/X28sLCwvD999836nfZqqoqLFq0CBEREVi9ejXGjBkD4N5e+Y4dOxAWFgZ/f3+89dZbBnv0APDqq6+iqqoKM2bMwI4dO/DWW29h/vz5evVpNBps27YNTz/9NDp37qy393n06FF4e3vD19cX5eXliI+PBwC9bUY/Pz8sXrwYK1aswNKlS7FkyRKD7TONRoOoqCicPXtWd3VdXVasWAE3NzeMGzdOb++9SdQrnvVMdfUS737vvfdeg5Z4FRUVekvMDRs2UOfOnfXa7t69Sz4+PrRy5Uq9tvLyciIiKisrI4VCQcnJybolXmVlJfXp04d+++23Bi3xfvzxR/L29iYnJyf69ttvde0dOnSgr776Svf/b775hlxdXcnBwYH+/ve/U3Z2tt58kpKSqF27dgSAXn75ZVKr1UREtHv3boqOjqZOnToRABozZgzdunVL77VlZWW0evVq3enzN2/eJACUkZFBREQlJSX09ddfk1qtphkzZpCVlRV9/vnnBp/tunXryNLSkn788UeDcTUt8YiIjhw5QmZmZrR//36DcfcTegD5+++/r/FAZmNWtcXFxfTRRx+RnZ2d3hdcrW/fvgbhqVZYWEjTpk2jqqoqXfDuN3To0FoPut4fvLFjx5JMJqOXXnqJbty4oTePB4NHRHT79m167733yMnJiY4dO2ZQV0VFhS481T788EPq1asXzZ49u94X/FRvfsyaNYuGDh1KVlZWelfOLV26lGQyGQUFBVFSUpLudWq1mo4ePar7f1JSEh08eJDS0tIoMDCQwsLCauwvLS3toTUJD15TbOPFx8eThYUF+fr61vjXSVR38O5XW/A+/vhjKikp0Q2//fabQfDOnj1b64deU/CqVV9U1NQSEhLIy8uL5HI5WVlZ0ZAhQ+ijjz6qcVsxOTmZ+vbtS3Z2dgbbx9Xef/99atu2LVlYWJBCoajX51kbocGrqKiggoKCh86nrKzsodPl5uY2pLRWobi4mBYtWkSHDh2iu3fvPnR6tVpNly5dkqAywT+ZWVpa1uu0I4VCUePPUver6ZeM1s7e3l7vjlQPI5fL4enp2YwVNR5fc8GE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxIRp1WlRGRkZT12GSioqK6rxlW2vS0Ew0KHhKpRIAEBkZ2aBOWOtRnZGHkRE17LL6zMxMlJSUNKqoliQjIwORkZHYvn273mNAWzOlUonu3bvXa9oGr2rrO+PWolevXrVeeM1qxzsXTAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TAgOHhOCg8eE4OAxITh4TIgmf1ByS5aTk4OrV68CAC5fvgwAOHXqFO7cuQMzMzMMHjxYZHkmpcHX1bZmBw8exPDhw2scN3DgQBw/flziikwXr2obICgoSPco9Qc15mHQrRkHrwGsrKwQEhJS47jw8HCJqzFtHLwGqilgnTt3hre3t4BqTBcHr4FGjBhh8OTr8PBwgzZWNw5eA7Vv3x5+fn56bbyabTgOXiPcHzQ7OzsMGTJEYDWmiYPXCPfvwYaEhMDKykpgNaaJg9cIffr0QadOnQDwaraxOHiNIJPJdDsUYWFhossxSfyTWSOFh4cjLS0N7du3F12KSWoRP5lVVlYiMzMTWVlZyM/Ph0qlgkajadY+1Wo1fv75ZwQGBjZrPwBgbW0NBwcHuLu7w9PTE+3btzf5wzcmGTyNRoPk5GQcOHAAhw8fxtmzZ1FVVaUbr1AoYG7e/AtzImr2ABARysvLoVardW0uLi4ICAhASEgIRo0aBRcXl2atoTmYVPByc3Oxbt06bN26FXl5eXBxcUFwcDD8/f3h4+ODrl27okOHDpKETkpEBJVKhZycHFy6dAmnTp3C0aNHkZKSAiJCcHAwpk+fjvDwcMjlctHl1g+ZgNzcXJo6dSqZm5uTvb09zZw5k1JSUkir1YouTahbt27Rxo0byd/fnwBQjx49KDY21iQ+F6MOXlVVFa1YsYJsbGzIycmJli9fTnfu3BFdllFKSUmhsLAwAkCDBg2iCxcuiC6pTkYbvKysLBo4cCCZmZnRnDlz6Pbt26JLMgkJCQnk6elJVlZWtHr1aqNd+hll8BITE8nR0ZE8PDzo2LFjossxOWVlZRQdHU0AaPz48VRWVia6JANGF7yYmBiysLCgYcOG0a1bt0SXY9JiY2NJoVBQYGCg0a0xjCp4MTE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-       "  <clipPath id=\"pcd1d4bc47a\">\n",
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        "   <rect x=\"3.6\" y=\"3.6\" width=\"106.92\" height=\"328.68\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<schemdraw.backends.mpl.Figure object at 0x116354490>"
+       "<schemdraw.backends.mpl.Figure object at 0x1167bb610>"
       ]
      },
      "metadata": {},
@@ -6076,7 +6084,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
     "hidden": true
    },
    "source": [
@@ -15201,42 +15208,35 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true
-   },
+   "metadata": {},
    "source": [
     "## `for`ループ"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "### 説明"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "ループとは同じコードを複数回続けて実行できるように書かれたコードを指す。ループには２つのタイプがあるが，`for`ループは指定した回数だけ処理を繰り返す計算手続きである。次のような書き方となる。\n",
     "\n",
     "```\n",
     "for ＜イタラブルの要素を割り当てる変数＞ in ＜イタラブル＞:\n",
-    "    ＜毎回実行したい内容１＞\n",
+    "    ＜毎回実行したいコード＞\n",
     "```\n",
     "\n",
     "* 1行目\n",
-    "    * `＜イタラブル＞`（iterable）とはリストやタプルのように要素を１つずつ返すことができる反復可能なデータ型（オブジェクト）を指す。文字列や後に説明する`Numpy`の`array`も含まれる。    \n",
-    "    * `＜イタラブルの要素を割り当てる変数＞`とはループを１回実行する際に`＜イタラブル＞`の要素の値を割り当てる変数のこと。よく`i`や`j`などが使われ，再割り当てされても問題がない変数名を使おう。\n",
+    "    * `＜イタラブル＞`（iterable）とはリストやタプルのように要素を１つずつ返すことができる反復可能なデータ型（オブジェクト）を指す。文字列や後に説明する`Numpy`の`array`も含まれる。\n",
+    "    * `＜イタラブルの要素を割り当てる変数＞`とはループを１回実行する毎に`＜イタラブル＞`の要素の値を割り当てる変数のこと。よく`i`や`j`などが使われ，再割り当てされても問題がない変数名を使おう。\n",
     "    * `for`で始まり`:`で終わり，`＜イタラブル＞`の前に`in`が入る。\n",
     "* 2行目以降\n",
-    "    * 慣例では4つの半角スペースのインデント後に実行したいコードを書く。\n",
+    "    * 慣例では4つの半角スペースのインデント後に毎回実行したいコードを書く。\n",
     "\n",
     "```{note}\n",
     "`for`ループでは無限ループは発生しない。リストなどのイタラブルの最後の要素が使われると，自動でループが終了する仕様となっている。\n",
@@ -15247,249 +15247,2343 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
-   },
-   "source": [
-    "### `print()`を使う例"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "次のリストにはGDPの構成要素が並んでいる。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "gdp_components = ['消費', '投資', '政府支出', '純輸出']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "このリストにある文字列を表示したいとしよう。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "消費\n",
-      "投資\n",
-      "政府支出\n",
-      "純輸出\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in gdp_components:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "＜コードの説明＞\n",
-    "* １回目のループ\n",
-    "    * 1行目で`gdp_components`の0番目の要素`消費`を`i`に割り当てる。\n",
-    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
-    "* ２回目のループ\n",
-    "    * 1行目で`gdp_components`の1番目の要素`投資`を`i`に割り当てる。\n",
-    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
-    "* ３回目のループ\n",
-    "    * 1行目で`gdp_components`の2番目の要素`政府支出`を`i`に割り当てる。\n",
-    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
-    "* ４回目のループ\n",
-    "    * 1行目で`gdp_components`の最後の要素`純輸出`を`i`に割り当てる。\n",
-    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
-    "\n",
-    "この例では`gdp_components`の要素の数だけループが行われる。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### `.append()`を使う例"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "リストにはメソッド`.append()`が実装されており，これを使うとリストに値を追加することができる。`.append()`と`for`ループを使い，空のリストに値を追加し新たなリストを作成する方法を紹介する。まず元になるリストを作成しよう。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "var_lst = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
-    "以下では，`var_lst`のそれぞれの要素の10倍からなるリストを新たに作成する。"
+    "フローチャートで表すと次のようになる。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[10, 20, 30, 40, 50]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_lst = []              # 1\n",
-    "\n",
-    "for i in var_lst:        # 2\n",
-    "    x = 10*i              # 3\n",
-    "    my_lst.append(x)     # 4\n",
-    "\n",
-    "my_lst                   # 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "＜コードの説明＞\n",
-    "\n",
-    "1. 空のリストの作成（`my_lst`に10倍にした数字を格納する）\n",
-    "2. ここから`for`ループの始まり。`i`はリスト`[1,2,3,4,5]`の要素を割り当てる変数。\n",
-    "    * １回目のループでは`i`に`1`を割り当てる。\n",
-    "    * ２回目のループでは`i`に`2`を割り当てる。\n",
-    "    * ３回目のループでは`i`に`3`を割り当てる。\n",
-    "    * ４回目のループでは`i`に`4`を割り当てる。\n",
-    "    * ５回目のループでは`i`に`5`を割り当てる。\n",
-    "3. `10*i`を計算し`x`に割り当てる。\n",
-    "1. `.append()`を使い`x`の値を`my_lst`に追加する。\n",
-    "1. `my_lst`を表示する。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 45,
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
     "tags": [
-     "remove-cell"
+     "hide-input"
     ]
    },
-   "source": [
-    "### 消費関数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "(sec:4-consumption)=\n",
-    "### 消費関数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "`for`ループを使い，所得によって消費がどのように変化するかを考えてみよう。まず`y`を所得として消費関数を次のように仮定する。\n",
-    "```\n",
-    "消費 = 100 + 0.7 * y\n",
-    "```\n",
-    "ここで\n",
-    "* `100`：自発的消費（autonomous consumption）\n",
-    "    * 可処分所得がゼロであっても発生する消費支出\n",
-    "* `0.7`：限界消費性向（marginal propensity to consume）\n",
-    "    * 可処分所得が`1`単位増加した場合に，どれだけ消費が増えるかを示す。例えば，月給が`10,000`円増えたとすると，その場合，`7000`円を消費に支出することを意味する。\n",
-    "\n",
-    "所得は次のリストで与えられるとする。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "income_lst = [1000, 1100, 1500, 2000, 2300, 3000] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "hidden": true
-   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[800.0, 870.0, 1150.0, 1500.0, 1710.0, 2200.0]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c_lst = []               # 1\n",
-    "\n",
+      "image/png": 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+       "z\n",
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+       "\" transform=\"scale(0.015625)\"/>\n",
+       "     </defs>\n",
+       "     <use xlink:href=\"#IPAexGothic-6700\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-5f8c\" x=\"100\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-306e\" x=\"200\"/>\n",
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+       "     <use xlink:href=\"#IPAexGothic-7d20\" x=\"400\"/>\n",
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+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_5\">\n",
+       "    <!-- 毎回実行したい -->\n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "      <path id=\"IPAexGothic-3057\" d=\"M 1750 4884 \n",
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+       "L 1750 4884 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "      <path id=\"IPAexGothic-305f\" d=\"M 684 3994 \n",
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+       "L 3144 2859 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "      <path id=\"IPAexGothic-3044\" d=\"M 3369 1447 \n",
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+       "L 5378 1294 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "     </defs>\n",
+       "     <use xlink:href=\"#IPAexGothic-6bce\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-56de\" x=\"100\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-5b9f\" x=\"200\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-884c\" x=\"300\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3057\" x=\"400\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-305f\" x=\"500\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3044\" x=\"600\"/>\n",
+       "    </g>\n",
+       "    <!-- コードを実行 -->\n",
+       "    <g transform=\"translate(282.72 118.209609) scale(0.125 -0.125)\">\n",
+       "     <defs>\n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "     </defs>\n",
+       "     <use xlink:href=\"#IPAexGothic-30b3\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-30fc\" x=\"100\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-30c9\" x=\"200\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3092\" x=\"300\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-5b9f\" x=\"400\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-884c\" x=\"500\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_6\">\n",
+       "    <!-- END -->\n",
+       "    <g transform=\"translate(307.020781 174.404141) scale(0.125 -0.125)\">\n",
+       "     <defs>\n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
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+       "L 4244 63 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "      <path id=\"IPAexGothic-44\" d=\"M 609 4634 \n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "     </defs>\n",
+       "     <use xlink:href=\"#IPAexGothic-45\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-4e\" x=\"59.521484\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-44\" x=\"135.498047\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_7\">\n",
+       "    <!-- 毎回実行したい -->\n",
+       "    <g transform=\"translate(89.27 207.198672) scale(0.125 -0.125)\">\n",
+       "     <use xlink:href=\"#IPAexGothic-6bce\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-56de\" x=\"100\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-5b9f\" x=\"200\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-884c\" x=\"300\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3057\" x=\"400\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-305f\" x=\"500\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3044\" x=\"600\"/>\n",
+       "    </g>\n",
+       "    <!-- コードを実行 -->\n",
+       "    <g transform=\"translate(95.52 220.809609) scale(0.125 -0.125)\">\n",
+       "     <use xlink:href=\"#IPAexGothic-30b3\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-30fc\" x=\"100\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-30c9\" x=\"200\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-3092\" x=\"300\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-5b9f\" x=\"400\"/>\n",
+       "     <use xlink:href=\"#IPAexGothic-884c\" x=\"500\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pad94b22513\">\n",
+       "   <rect x=\"3.6\" y=\"3.6\" width=\"392.04\" height=\"235.62\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<schemdraw.backends.mpl.Figure object at 0x1177e5210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with schemdraw.Drawing() as sd:\n",
+    "    flow.Start(w=2, h=1).label('START')\n",
+    "    flow.Arrow().down(sd.unit/4)\n",
+    "    \n",
+    "    d = flow.Decision(w=4.2, h=1.7, S='False', E='True'\n",
+    "                     ).label('イタラブルの\\n最後の要素？').drop('E')\n",
+    "    \n",
+    "    flow.Arrow().right(sd.unit/2)\n",
+    "    flow.Box(w=3.2, h=1).label('毎回実行したい\\nコードを実行').drop('S')\n",
+    "    flow.Arrow().down(sd.unit/4)\n",
+    "    flow.Start(w=2, h=1).label('END')\n",
+    "    \n",
+    "    flow.Arrow().down(sd.unit/2).at(d.S)\n",
+    "    b = flow.Box(w=3.2, h=1).label('毎回実行したい\\nコードを実行').drop('W')\n",
+    "    \n",
+    "    flow.Line().left(sd.unit/2).at(b.W)\n",
+    "    flow.Wire('|-', arrow='->').linewidth(1).to(d.W)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "この図から，`for`ループの裏では`if`文が動いていることが分かると思う。「毎回実行したいコード」を実行する度に，イタラブルの最後の要素を使うかどうかを判断している。では，例を使いコードを確認してみよう。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `print()`を使う例"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "次のリストにはGDPの構成要素が並んでいる。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdp_components = ['消費', '投資', '政府支出', '純輸出']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "このリストにある文字列を表示したいとしよう。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "消費\n",
+      "投資\n",
+      "政府支出\n",
+      "純輸出\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in gdp_components:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "＜コードの説明＞\n",
+    "* １回目のループ\n",
+    "    * 1行目で`gdp_components`の0番目の要素`消費`を`i`に割り当てる。\n",
+    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
+    "* ２回目のループ\n",
+    "    * 1行目で`gdp_components`の1番目の要素`投資`を`i`に割り当てる。\n",
+    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
+    "* ３回目のループ\n",
+    "    * 1行目で`gdp_components`の2番目の要素`政府支出`を`i`に割り当てる。\n",
+    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
+    "* ４回目のループ\n",
+    "    * 1行目で`gdp_components`の最後の要素`純輸出`を`i`に割り当てる。\n",
+    "    * 2行目で`print()`関数を使い変数`i`の値を表示する。\n",
+    "\n",
+    "この例では`gdp_components`の要素の数だけループが行われる。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `.append()`を使う例"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "リストにはメソッド`.append()`が実装されており，これを使うとリストに値を追加することができる。`.append()`と`for`ループを使い，空のリストに値を追加し新たなリストを作成する方法を紹介する。まず元になるリストを作成しよう。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var_lst = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "以下では，`var_lst`のそれぞれの要素の10倍からなるリストを新たに作成する。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20, 30, 40, 50]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_lst = []              # 1\n",
+    "\n",
+    "for i in var_lst:        # 2\n",
+    "    x = 10*i              # 3\n",
+    "    my_lst.append(x)     # 4\n",
+    "\n",
+    "my_lst                   # 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "＜コードの説明＞\n",
+    "\n",
+    "1. 空のリストの作成（`my_lst`に10倍にした数字を格納する）\n",
+    "2. ここから`for`ループの始まり。`i`はリスト`[1,2,3,4,5]`の要素を割り当てる変数。\n",
+    "    * １回目のループでは`i`に`1`を割り当てる。\n",
+    "    * ２回目のループでは`i`に`2`を割り当てる。\n",
+    "    * ３回目のループでは`i`に`3`を割り当てる。\n",
+    "    * ４回目のループでは`i`に`4`を割り当てる。\n",
+    "    * ５回目のループでは`i`に`5`を割り当てる。\n",
+    "3. `10*i`を計算し`x`に割り当てる。\n",
+    "1. `.append()`を使い`x`の値を`my_lst`に追加する。\n",
+    "1. `my_lst`を表示する。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true,
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "### 消費関数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "(sec:4-consumption)=\n",
+    "### 消費関数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "`for`ループを使い，所得によって消費がどのように変化するかを考えてみよう。まず`y`を所得として消費関数を次のように仮定する。\n",
+    "```\n",
+    "消費 = 100 + 0.7 * y\n",
+    "```\n",
+    "ここで\n",
+    "* `100`：自発的消費（autonomous consumption）\n",
+    "    * 可処分所得がゼロであっても発生する消費支出\n",
+    "* `0.7`：限界消費性向（marginal propensity to consume）\n",
+    "    * 可処分所得が`1`単位増加した場合に，どれだけ消費が増えるかを示す。例えば，月給が`10,000`円増えたとすると，その場合，`7000`円を消費に支出することを意味する。\n",
+    "\n",
+    "所得は次のリストで与えられるとする。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "income_lst = [1000, 1100, 1500, 2000, 2300, 3000] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[800.0, 870.0, 1150.0, 1500.0, 1710.0, 2200.0]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c_lst = []               # 1\n",
+    "\n",
     "for y in income_lst:     # 2\n",
     "    con = 100 + 0.7 * y  # 3\n",
     "    c_lst.append(con)    # 4\n",
@@ -15521,8 +17615,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
+    "heading_collapsed": true
    },
    "source": [
     "### `for`ループの２つの書き方（動学分析の基礎）"
@@ -15859,7 +17952,6 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true,
     "tags": [
      "remove-cell"
     ]
@@ -15972,8 +18064,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
+    "heading_collapsed": true
    },
    "source": [
     "### `for`ループのお友達１：`enumerate()`関数"
@@ -16146,8 +18237,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true
+    "heading_collapsed": true
    },
    "source": [
     "### `for`ループのお友達２：`zip()`関数"