SLR/Assignment4Qn1 (1).ipynb

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      "execution_count": 2,
      "id": "98567393",
      "metadata": {
        "id": "98567393"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "434594e0",
      "metadata": {
        "id": "434594e0"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/delivery_time (1).csv\")"
      ]
    },
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            "text/plain": [
              "   Delivery Time  Sorting Time\n",
              "0          21.00            10\n",
              "1          13.50             4\n",
              "2          19.75             6\n",
              "3          24.00             9\n",
              "4          29.00            10"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delivery Time</th>\n",
              "      <th>Sorting Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>10</td>\n",
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              "      <td>4</td>\n",
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              "      <td>19.75</td>\n",
              "      <td>6</td>\n",
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              "      <th>3</th>\n",
              "      <td>24.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>29.00</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "299f5015",
      "metadata": {
        "id": "299f5015"
      },
      "outputs": [],
      "source": [
        "data['Delivery Time']=pd.to_numeric(data['Delivery Time'],errors = 'coerce')\n",
        "data['Sorting Time']=pd.to_numeric(data['Sorting Time'],errors = 'coerce')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4ad46492",
      "metadata": {
        "id": "4ad46492"
      },
      "outputs": [],
      "source": [
        "data.duplicated()\n",
        "data=data.drop_duplicates()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "771560ba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "771560ba",
        "outputId": "7ebf6d93-ebdc-46bc-a000-fb64821a0b9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 21 entries, 0 to 20\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Delivery Time  21 non-null     float64\n",
            " 1   Sorting Time   21 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 504.0 bytes\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "c7838531",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7838531",
        "outputId": "95b07d8c-3674-45fa-f3bd-d52e234b3918"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e4b6e47a",
      "metadata": {
        "id": "e4b6e47a"
      },
      "outputs": [],
      "source": [
        "Sorting= data.iloc[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "4358c399",
      "metadata": {
        "id": "4358c399"
      },
      "outputs": [],
      "source": [
        "Delivery= data.iloc[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "0eabbbf9",
      "metadata": {
        "id": "0eabbbf9"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "cbe7b23e",
      "metadata": {
        "colab": {
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            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7d9ccb9af1c0>,\n",
              "  <matplotlib.lines.Line2D at 0x7d9ccb9af460>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7d9ccb9af700>,\n",
              "  <matplotlib.lines.Line2D at 0x7d9ccb9af9a0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7d9ccb9aef20>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7d9ccb9afc40>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7d9ccb9afee0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pyplot.boxplot(Sorting)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "21974e6a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "21974e6a",
        "outputId": "fa915326-3942-4036-9049-22af45cd5e68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7d9cc99004f0>,\n",
              "  <matplotlib.lines.Line2D at 0x7d9cc9900790>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7d9cc9900a30>,\n",
              "  <matplotlib.lines.Line2D at 0x7d9cc9900cd0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7d9cc9900250>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7d9cc9900f70>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7d9cc9900eb0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pyplot.boxplot(Delivery)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8087a4dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "8087a4dd",
        "outputId": "f8104399-c554-4608-8ce9-fec00b2338d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7d9cc975e0e0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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6CZiuWfX5SAT6fADpIxK1dOPDz1/wxvXiAx9l2W0SG5s0LCnm5zj2E2O1C6T47t/s7QIgYbKzXGqoLZf0lxvVmLH3DbXlBI8kV13hU/PGSnndsU+ovO5cggdmhCcfABKOPh/pgUZxOJ947t+EDwBGcOMC0pux9uoAMF3ZWS5VlRU6XQaAJED4AACb8HQHmB7CBwDYgHktwPSx2gUAZon9a4D4ED4AYBbYvwaIH+EDAGaB/WvSRyRqqfPYKe3peVOdx04RGBOIOR8AMAvsX5MemLNjFk8+AGAW2L8m9TFnxzzCBwDMwtjGa1MtqHVp9C9oNl5LTszZcQbhAwBmgf1rUhtzdpxB+ACAWWLjtdTFnB1nMOEUAGxQXeHT2nIvHU5TDHN2nEH4AACbsH9N6hmbszMQDE8678Ol0SdYzNmxF8MuAICMxZwdZxA+AAAZjTk75jHsAgDIeMzZMYvwAQA2iUQtbl4pjDk75hA+AMAGtOcGpo85HwAwS7TnBuJD+ACAWaA9N1JJsuzcy7ALAMxCPO25mU8AJyXT0CBPPgBgFmjPjVSQbEODhA8AmAXacyPZJePQIOEDAGZh+YLLdKHVtFmu0fMAJyTjzr2EDwCYhcN/fFsX+oMxao2eBzghGYcGCR8AMAvJ+IsdOFcyDg0SPgBgFpLxFztwrrGde6caHXRpdNWLyZ17CR8AMAvJ+IsdOFcy7txL+ACAWUjGX+zAeyXbzr0uy7KSqu1eKBSS2+1WMBhUQUGB0+UAwLQkUwMnYCqJ3Pwwnvs34QMAbMKutshk8dy/aa8OADZhS3ZgepjzAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAo+nwAgE1oMgZMD+EDAGxAe3Vg+hh2AYBZau8NqK6lOyZ4SNJAMKy6lm619wYcqgxIToQPAJiFSNRSY1ufJtska+xYY1ufItGk2kYLcBThAwBmoat/cMITj3NZkgLBsLr6B80VBSQ5wgcAzMLJoamDx0zOAzIB4QMAZqEoP9fW84BMQPgAgFlY4ffI587VVAtqXRpd9bLC7zFZFpDUCB8AMAvZWS411JZL0oQAMva+obacfh/AOQgfADBL1RU+NW+slNcdO7TideeqeWMlfT6A96DJGADYoLrCp7XlXjqcAtNA+AAAm2RnuVRVVuh0GUDSY9gFAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEWHUwBGRKIWrccBSCJ8ADCgvTegxrY+BYLh8WM+d64aasvZdA3IQAy7AEio9t6A6lq6Y4KHJA0Ew6pr6VZ7b8ChygA4hfABIGEiUUuNbX2yJvls7FhjW58i0cnOAJCuCB8AEqarf3DCE49zWZICwbC6+gfNFQXAcYQPAAlzcmjq4DGT8wCkh7jDx8GDB1VbW6uSkhK5XC7t3r075vO7775bLpcr5lVdXW1XvQBSSFF+rq3nAUgPcYeP4eFhLV26VI8//viU51RXVysQCIy/nn766VkVCUij8wc6j53Snp431XnsFPMEUsAKv0c+d66mWlDr0uiqlxV+j8myADgs7qW2NTU1qqmpOe85OTk58nq9My4KeC+Waqam7CyXGmrLVdfSLZcUM/F0LJA01JbT7wPIMAmZ83HgwAEVFRVp0aJFqqur06lTp6Y8d2RkRKFQKOYFnIulmqmtusKn5o2V8rpjh1a87lw1b6wkPAIZyPYmY9XV1brtttvk9/t17NgxfeUrX1FNTY06OzuVnZ094fympiY1NjbaXQbSxIWWaro0ulRzbbmXv56TWHWFT2vLvXQ4BSBJclmWNeOBc5fLpV27dmn9+vVTnvOHP/xBZWVl+uUvf6nVq1dP+HxkZEQjIyPj70OhkObPn69gMKiCgoKZloY00XnslO78wUsXPO/pz1+vqrJCAxUBACYTCoXkdrundf9O+FLbhQsX6v3vf7+OHj066ec5OTkqKCiIeQFjWKoJAOkn4eHjT3/6k06dOiWfj3FdxI+lmgCQfuKe83HmzJmYpxj9/f3q6emRx+ORx+NRY2OjNmzYIK/Xq2PHjukf/uEfdMUVV2jdunW2Fo7MMLZUcyAYnnTeh0ujExdZqgkAqSPuJx+vvPKKli1bpmXLlkmStm3bpmXLlumhhx5Sdna2Xn31VX384x/XVVddpXvuuUfLly/Xr371K+Xk5NhePNLf2FJNSRN6RbBUEwBS06wmnCZCPBNWkDno8wEAyS2e+7ftS22BRGCpJgCkD8IHUkZ2lovltACQBtjVFgAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGsdQWgBGRqEWfFgCSCB8ADKBDLYBzMewCIKHaewOqa+mOCR6SNBAMq66lW+29AYcqA+AUwgeAhIlELTW29U26I/HYsca2PkWiSbXFFIAEI3wgZUSiljqPndKenjfVeewUN6wU0NU/OOGJx7ksSYFgWF39g+aKAuA45nwgJTBnIDWdHJo6eMzkPADpgScfSHrMGUhdRfm5tp4HID0QPpDUmDOQ2lb4PfK5czXVglqXRp9grfB7TJYFwGGEDyQ15gyktuwslxpqyyVpQgAZe99QW06/DyDDED6Q1JgzkPqqK3xq3lgprzt2aMXrzlXzxkrm7AAZiAmnSGrMGUgP1RU+rS330uEUgCTCB5Lc2JyBgWB40nkfLo3+Bc2cgeSXneVSVVmh02UASAIMuyCpMWcAANIP4QNJjzkDAJBeGHZBSmDOAACkD8IHUgZzBgAgPTDsAgAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIqN5dJEJGqx4ysAICUQPtJAe29AjW19CgTD48d87lw11JarusLnYGUAAEzEsEuKa+8NqK6lOyZ4SNJAMKy6lm619wYcqgwAgMkRPlJYJGqpsa1P1iSfjR1rbOtTJDrZGQAAOIPwkcK6+gcnPPE4lyUpEAyrq3/QXFEAAFwA4SOFnRyaOnjM5DwAAEwgfKSwovxcW88DAMAEwkcKW+H3yOfO1VQLal0aXfWywu8xWRYAAOdF+Ehh2VkuNdSWS9KEADL2vqG2nH4fAICkQvhIcdUVPv3dTX653pMvXC7p727y0+cDAJB0CB8prr03oB0H+/Xe1bRRS9pxsJ8+HwCApEP4SGHn6/Mxhj4fAIBkQ/hIYfT5AACkIsJHCqPPBwAgFRE+Uhh9PgAAqYjwkcLo8wEASEWEjxRGnw8AQCoifKS46gqfmjdWyuuOHVrxunPVvLGSPh8AgKQzx+kCMHvVFT6tLfeqq39QJ4fCKsofHWrhiQcAIBkRPtJEdpZLVWWFTpcBAMAFET6QMiJRi6c7AJAGCB9ICe29ATW29cU0VfO5c9VQW868FgBIMUw4RdJr7w2orqV7QjfXgWBYdS3d7F8DACmG8IGkdr79a8aOsX8NAKQWwgeSGvvXAED6IXwgqbF/DQCkH8IHkhr71wBA+iF8IKmxfw0ApB/CB5Ia+9cAQPohfCDpsX8NAKQXmowhJbB/DQCkD8IHUgb71wBAemDYBQAAGBV3+Dh48KBqa2tVUlIil8ul3bt3x3xuWZYeeugh+Xw+5eXlac2aNXr99dftqhcAAKS4uMPH8PCwli5dqscff3zSzx955BE99thjevLJJ/Xyyy/r4osv1rp16xQO0wQKAADMYM5HTU2NampqJv3Msiz98z//s772ta/p1ltvlST9+7//u4qLi7V79259+tOfnl21AAAg5dk656O/v18DAwNas2bN+DG3262VK1eqs7Nz0n8zMjKiUCgU8wIAAOnL1vAxMDAgSSouLo45XlxcPP7ZezU1Ncntdo+/5s+fb2dJAAAgyTi+2mX79u0KBoPjr+PHjztdEgAASCBbw4fX65UknThxIub4iRMnxj97r5ycHBUUFMS8AABA+rI1fPj9fnm9Xu3fv3/8WCgU0ssvv6yqqio7/1N4j0jUUuexU9rT86Y6j51SJGo5XRIAAJOKe7XLmTNndPTo0fH3/f396unpkcfjUWlpqbZu3apvfOMbuvLKK+X3+/Xggw+qpKRE69evt7NunKO9N6DGtj4Fgn9Zzuxz56qhtpx9TwAAScdlWVZcfyIfOHBAH/nIRyYc37Rpk5566ilZlqWGhgbt2LFDp0+f1o033qgnnnhCV1111bS+PxQKye12KxgMMgQzDe29AdW1dOu9P8SxHU/YeA0AYEI89++4w0eiET6mLxK1dOPDz8c88TiXS6M7v774wEfZgA0AkFDx3L8dX+2CmevqH5wyeEiSJSkQDKurf9BcUQAAXADhI4WdHJpey/rpngcAgAmEjxRWlJ9r63kAAJhA+EhhK/we+dy5mmo2h0ujq15W+D0mywIA4LwIHyksO8ulhtpySZoQQMbeN9SWM9kUAJBUCB8prrrCp+aNlfK6Y4dWvO5cltkCAJJS3E3GkHyqK3xaW+5VV/+gTg6FVZQ/OtTCEw8AQDIifKSJ7CyXqsoKnS4DAIALYtgFAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgVMZsLBeJWuz6CgBAEsiI8NHeG1BjW58CwfD4MZ87Vw215aqu8DlYGQAAmSfth13aewOqa+mOCR6SNBAMq66lW+29AYcqAwAgM6V1+IhELTW29cma5LOxY41tfYpEJzsDAAAkQlqHj67+wQlPPM5lSQoEw+rqHzRXFAAAGS6tw8fJoamDx0zOAwAAs5fW4aMoP9fW8wAAwOyldfhY4ffI587VVAtqXRpd9bLC7zFZFgAAGS2tw0d2lksNteWSNCGAjL1vqC2n3wcAAAaldfiQpOoKn5o3Vsrrjh1a8bpz1byxkj4fAAAYlhFNxqorfFpb7qXDKQAASSAjwoc0OgRTVVbodBkAAGS8tB92AQAAyYXwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADAq6TqcWpYlSQqFQg5XAgAApmvsvj12Hz+fpAsfQ0NDkqT58+c7XAkAAIjX0NCQ3G73ec9xWdOJKAZFo1G99dZbys/Pl8tl78ZvoVBI8+fP1/Hjx1VQUGDrdyeDdL8+Kf2vketLfel+jVxf6kvUNVqWpaGhIZWUlCgr6/yzOpLuyUdWVpbmzZuX0P9GQUFB2v5PJaX/9Unpf41cX+pL92vk+lJfIq7xQk88xjDhFAAAGEX4AAAARmVU+MjJyVFDQ4NycnKcLiUh0v36pPS/Rq4v9aX7NXJ9qS8ZrjHpJpwCAID0llFPPgAAgPMIHwAAwCjCBwAAMIrwAQAAjMqI8NHU1KTrrrtO+fn5Kioq0vr163XkyBGny7JNc3OzlixZMt4wpqqqSs8995zTZSXMt7/9bblcLm3dutXpUmzzj//4j3K5XDGvxYsXO12Wrd58801t3LhRhYWFysvL04c+9CG98sorTpdliw9+8IMTfn4ul0v19fVOl2abSCSiBx98UH6/X3l5eSorK9PXv/71ae3jkSqGhoa0detWLViwQHl5ebrhhht06NAhp8uasYMHD6q2tlYlJSVyuVzavXt3zOeWZemhhx6Sz+dTXl6e1qxZo9dff91IbRkRPjo6OlRfX6+XXnpJ+/bt09mzZ3XzzTdreHjY6dJsMW/ePH3729/W4cOH9corr+ijH/2obr31Vv3P//yP06XZ7tChQ/r+97+vJUuWOF2K7a655hoFAoHx14svvuh0SbZ5++23tWrVKl100UV67rnn1NfXp+985zu67LLLnC7NFocOHYr52e3bt0+S9MlPftLhyuzz8MMPq7m5Wf/6r/+q//3f/9XDDz+sRx55RP/yL//idGm2+dznPqd9+/bpxz/+sV577TXdfPPNWrNmjd58802nS5uR4eFhLV26VI8//viknz/yyCN67LHH9OSTT+rll1/WxRdfrHXr1ikcDie+OCsDnTx50pJkdXR0OF1Kwlx22WXWD3/4Q6fLsNXQ0JB15ZVXWvv27bP++q//2rrvvvucLsk2DQ0N1tKlS50uI2EeeOAB68Ybb3S6DGPuu+8+q6yszIpGo06XYptbbrnF2rx5c8yx2267zbrrrrscqshe77zzjpWdnW09++yzMccrKyutr371qw5VZR9J1q5du8bfR6NRy+v1Wo8++uj4sdOnT1s5OTnW008/nfB6MuLJx3sFg0FJksfjcbgS+0UiET3zzDMaHh5WVVWV0+XYqr6+XrfccovWrFnjdCkJ8frrr6ukpEQLFy7UXXfdpTfeeMPpkmzzi1/8Qtdee60++clPqqioSMuWLdMPfvADp8tKiHfffVctLS3avHmz7ZtjOumGG27Q/v379fvf/16S9Nvf/lYvvviiampqHK7MHv/3f/+nSCSi3NzcmON5eXlp9RRyTH9/vwYGBmJ+n7rdbq1cuVKdnZ0J/+8n3cZyiRaNRrV161atWrVKFRUVTpdjm9dee01VVVUKh8O65JJLtGvXLpWXlztdlm2eeeYZdXd3p/T46/msXLlSTz31lBYtWqRAIKDGxkb91V/9lXp7e5Wfn+90ebP2hz/8Qc3Nzdq2bZu+8pWv6NChQ/riF7+ouXPnatOmTU6XZ6vdu3fr9OnTuvvuu50uxVZf/vKXFQqFtHjxYmVnZysSieib3/ym7rrrLqdLs0V+fr6qqqr09a9/XVdffbWKi4v19NNPq7OzU1dccYXT5dluYGBAklRcXBxzvLi4ePyzRMq48FFfX6/e3t60S7KLFi1ST0+PgsGgfv7zn2vTpk3q6OhIiwBy/Phx3Xfffdq3b9+Ev0rSxbl/PS5ZskQrV67UggUL9NOf/lT33HOPg5XZIxqN6tprr9W3vvUtSdKyZcvU29urJ598Mu3Cx49+9CPV1NSopKTE6VJs9dOf/lQ/+clP1NraqmuuuUY9PT3aunWrSkpK0uZn+OMf/1ibN2/WBz7wAWVnZ6uyslJ33nmnDh8+7HRpaSejhl22bNmiZ599Vi+88ILmzZvndDm2mjt3rq644gotX75cTU1NWrp0qb73ve85XZYtDh8+rJMnT6qyslJz5szRnDlz1NHRoccee0xz5sxRJBJxukTbXXrppbrqqqt09OhRp0uxhc/nmxCEr7766rQaWpKkP/7xj/rlL3+pz33uc06XYrsvfelL+vKXv6xPf/rT+tCHPqS//du/1f3336+mpianS7NNWVmZOjo6dObMGR0/flxdXV06e/asFi5c6HRptvN6vZKkEydOxBw/ceLE+GeJlBHhw7IsbdmyRbt27dLzzz8vv9/vdEkJF41GNTIy4nQZtli9erVee+019fT0jL+uvfZa3XXXXerp6VF2drbTJdruzJkzOnbsmHw+n9Ol2GLVqlUTlrf//ve/14IFCxyqKDF27typoqIi3XLLLU6XYrt33nlHWVmxt4zs7GxFo1GHKkqciy++WD6fT2+//bb27t2rW2+91emSbOf3++X1erV///7xY6FQSC+//LKR+YIZMexSX1+v1tZW7dmzR/n5+ePjWW63W3l5eQ5XN3vbt29XTU2NSktLNTQ0pNbWVh04cEB79+51ujRb5OfnT5ifc/HFF6uwsDBt5u38/d//vWpra7VgwQK99dZbamhoUHZ2tu68806nS7PF/fffrxtuuEHf+ta39KlPfUpdXV3asWOHduzY4XRptolGo9q5c6c2bdqkOXPS71drbW2tvvnNb6q0tFTXXHONfvOb3+if/umftHnzZqdLs83evXtlWZYWLVqko0eP6ktf+pIWL16sz372s06XNiNnzpyJeXra39+vnp4eeTwelZaWauvWrfrGN76hK6+8Un6/Xw8++KBKSkq0fv36xBeX8PU0SUDSpK+dO3c6XZotNm/ebC1YsMCaO3eudfnll1urV6+2/vu//9vpshIq3Zba3nHHHZbP57Pmzp1rfeADH7DuuOMO6+jRo06XZau2tjaroqLCysnJsRYvXmzt2LHD6ZJstXfvXkuSdeTIEadLSYhQKGTdd999VmlpqZWbm2stXLjQ+upXv2qNjIw4XZpt/uM//sNauHChNXfuXMvr9Vr19fXW6dOnnS5rxl544YVJ732bNm2yLGt0ue2DDz5oFRcXWzk5Odbq1auN/f/rsqw0ak8HAACSXkbM+QAAAMmD8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMCo/wepQx0M9Yt8EQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pyplot.scatter(Sorting,Delivery)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "88df2e04",
      "metadata": {
        "id": "88df2e04"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "86c44384",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "86c44384",
        "outputId": "9fc87cda-b948-4789-a3f9-200f7fa7d6cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Delivery Time  Sorting Time\n",
              "Delivery Time       1.000000      0.825997\n",
              "Sorting Time        0.825997      1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-873c2618-dc4c-47fc-aeba-33e6486ca839\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delivery Time</th>\n",
              "      <th>Sorting Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delivery Time</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.825997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sorting Time</th>\n",
              "      <td>0.825997</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-873c2618-dc4c-47fc-aeba-33e6486ca839')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-873c2618-dc4c-47fc-aeba-33e6486ca839 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-873c2618-dc4c-47fc-aeba-33e6486ca839');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d1e07084-ab8e-4afc-80c0-2d4fadefe4aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1e07084-ab8e-4afc-80c0-2d4fadefe4aa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1e07084-ab8e-4afc-80c0-2d4fadefe4aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "data.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "b1e5d92a",
      "metadata": {
        "id": "b1e5d92a"
      },
      "outputs": [],
      "source": [
        "import statsmodels.formula.api as smf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "1b627273",
      "metadata": {
        "id": "1b627273"
      },
      "outputs": [],
      "source": [
        "model = smf.ols(\"Delivery~Sorting\", data=data).fit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "7ffcde4c",
      "metadata": {
        "id": "7ffcde4c"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "b0435a2c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "b0435a2c",
        "outputId": "280f0d34-78c6-471c-a621-e3bab894563f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sorting Time', ylabel='Delivery Time'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UVeDt6haU7XHjw8auqPVchxmr5hViyfRcTRraXSwmMkRElPB4GilaKKLizx804Zm9btS2d0etF2fZUDrfhUUTs+LmBNKFYCJDREQJK6KoaPeH0RVgU7te3kAEfzhUjx0H6tDmC0WtzxmTitISF+aOTYu7E0gXgokMERElnIiioqM7jK5AhMepTzvVFcTz+2vx8uEG+M+6MiVLwGcvyUJpiQuTcu0aRTg8mMgQEVHCUFSBDn8IHiYwfT5p9WF7RS3+crQJkbOOIBl0EpZMz8WquS4UpKVoFOHwYiJDRERxT1V7TiJ1dvMkUq/36zqxvcKNt6tbo9ZsJj2WXZqPFbMLkG41ahDdyGEiQ0REcUsIAU8ggg5/CMrZDU9GIVUIvHeiDWUVNais80StZ9lMuHVeIW6YkQuLcXS8xY+OV0lERAmnK9DTC4ZHqYGwouKND5uxvcKNj1v9UevjMiwoLXHh85OzYdDJGkSoHSYyREQUV/yhnl4woQgTGH8ogj8ebsBz++pwyhuMWp9R4ERpiQuXTUiHnAQnkC4EExkiIooLgbCCdn+IvWAAtPtD2LG/Di8erId3gBELVxRnoLTEhWn5Tg2iiy9MZIiISFNhRUW7LzTgG/ZoU9fejWf2ufHq+40IK/1rgvSyhGum5mD1PBfGZFg0ijD+MJEhIiJNKKpAuz/EXjAAPmrsQlmFG387fipqiKPFqMNNM/Owcm4hMm0mbQKMY0xkiJKUqgocqfegzR9CusWIafkOyAnchpySB49S9xBCYO8n7SircONATUfUerrViJVzCnDTzHzYzHy7Phd+Z4iS0DtVLdhcXo3qZi/CioBBJ6Eo24Z1i4qwsDhT6/BolOJR6h6KKlB+7BTKKtyoavZGrRempWDVPBeunZoDo350nUC6EExkiJLMO1Ut2LizEt5gBGkWI4w6GSFFxdGGLmzcWYlHVsxgMkMjjlOpe4qZX32/Ec/uq0VDZyBqfXKuHaXzXbiiKDOhhziONCYyRElEVQU2l1fDG4wg12HuGwhnlnXIdcho9ASxubwal0/I4DYTjYiwoqLVG4I/NHoLeTu7w3jxYB12HqhHZ3f0cMv549Nxe4kLMwudSTHEcaQxkSFKIkfqPahu9iLNYoz6hShJElItBlQ3e3Gk3oMZhTy2ScNHCIF2f08dzGgt5G30BPDc3lq8UtmAwFk9cWQJuGpyNlaXuFCUZdMowuTARIYoibT5QwgrAsZzdPY06WR0qgJt/tAIR0ajyWjfRqo+5cX2Cjfe+LA56gSSWS/j+hl5uHVeIXIdZm0CTDJMZIiSSLrFCINOQkhRYZZ1UetBRYVBlpBuSe4hcqSN0byNJITAodpOlO2pwZ6P26PWnSkGrJidj2WXFsCZYtAgwuR1QYlMJBLB7t27UV1djTVr1sBut6O+vh4OhwM2Gy+REWllWr4DRdk2HG3oQq5D7re9JIRAhz+MKXl2TMt3aBglJZvef1sdo3AbSVEF3q5uQdkeNz5s7Ipaz3WYsWpeIZZMz4XZEP3HBV28IScyn3zyCZYsWYKamhoEg0Fcc801sNvt+OEPf4hgMIhf/vKXwxEnEQ2CLEtYt6gIG3dWotETRKrFAJNORlBR0eEPw2bSYd2iIhb6UsyM1m2kUETFrg+asH2vG7Xt3VHrxVk2lM53YdHErKQ9gaQKgaomH441eVGQmqJZr6ohJzJf+9rXMG/ePBw6dAgZGRl9t69YsQL33HNPTIMjoqFbWJyJR1bM6Osj06kKGGQJU/Ls7CNDMROMKGjzjb65SN5gBH84WI8dB+rQ5ouuNZszJhWlJS7MHZuW1CeQDtS0Y9seN9ytPigCMOllzXpVDTmR+dvf/oZ33nkHRmP/PfZx48ahrq4uZoER0YVbWJyJyydksLMvxZyiCrT5QugKRB8jTmYt3iCe31eLlw43wH9W8iZLwGcvyUJpiQuTcu0aRThyDtS047Fdx+APKXCYDUgx6KAIoVmvqiEnMqqqQlGiM/Da2lrY7cn/AyRKFLIs8Yg1xVRnd3jUdeWtafVj+143dn3QhMhZr9ugk7Bkei5WzXWhIC1FowhHlioEtu1xwx9SkGkzQoIEWZZgkGXNelUNOZG59tpr8dOf/hRPPvkkgJ7eFF6vFw899BCuv/76mAdIRETaCoQVtHiDCEVGTx3MkfpOlFW48XZVa9SazaTHskvzsWJ2AdKto+sEYFWTD+5WHxxmAyTER6+qIScyP/7xj3Hddddh6tSpCAQCWLNmDY4fP47MzEw8/fTTwxEjERFpQFEFWn1BeAOj4zi1KgTeO9GGsgo3Kus6o9azbCbcOq8QN8zIhcU4OruXdAZCCKsCDt3AV1u06FU15J9EYWEhDh06hLKyMhw+fBherxd333037rjjDqSkjI5La0REya6zO4x2X2hUTKcOKyre+LAZ2yvc+LjVH7U+LsOC0hIXPj85G4ZzNJscLZxmIwyyhLAiYNJHJzNa9Kq6oJRSr9fjC1/4QqxjISIijQXCClp9IQTDyX8ayR+K4I+HG/Dcvjqc8gaj1mcUOFBaMgaXTUiHnMQnkIaiOMcKV4YVJ055+2pkemnVq+qCEpn6+nq89dZbaG5uhqr23zP953/+55gERkREI2c0nUZq94ewY38dXjxYD28wetvsiqIMrC5xYXoBi+XPJksS1sx34bFdx9DiDcFuNsAi9XQT16pXlSSG2IZx69at+MpXvgKj0YiMjIx+5+QlScKJEydiHuTF8Hg8cDqd6OzshMPBbqZERGfzBHq2kZL9NFJdRzee2evGa0eaogqX9bKEa6bmYNW8QozNsGoUYeLo10cGPbUxse4jM9j37yEnMi6XC1/96lfxwAMPQJbjf6+QiQwR0cCCEQUt3uTfRjrW1IWyPW68efxU1BBHi1GHG2fmYeWcQmTZTdoEmKB6O/tCwrB09h3s+/eQt5b8fj9KS0sTIokhIqJo6ulTJZ7u5N1GEkJg3yftKKtwY39NR9R6msWAlXMKcfOsfNjMo/ME0sWSJQkTc23IsptgN2s3CHPIP727774bzz77LL797W8PRzxERDSMkn0bSVEFyo+dQlmFG1XN3qj1wrQUrJrnwrVTc2DU8w/yZDDkrSVFUXDjjTeiu7sbM2bMgMHQPwt77LHHYhrgxeLWEhFR8p9GCoQVvPp+I57dV4uGzkDU+qRcO24vceGK4sykHeKoleG6IjNsW0ubNm3Ca6+9hkmTJgFAVLEvERHFj7Ciot0XGvB0TjLo7A7jxYN12HmgHp0DbJXNH5eG0vljMKvQyfeoJHVBnX1/85vf4K677hqGcIiIKBYUVaDdH0JXIIIhXnhPCI2eAJ7bW4tXKhsQOOsEkiwBV03OxuoSF4qybBpFODroZEnzJoFDfnaTyYQrrrgiJk++adMmlJSUwG63Izs7G8uXL8dHH33U7z6BQADr169HRkYGbDYbVq5ciaamppg8PxFRslFVgQ5/CO42Pzzd4aRLYk6c8uKRV47iC79+DzsO1PVLYsx6GbfMLsD/+/Jl2Hj9FCYxw0QnS7CbDchzpmBshhVmg07TeIZcI7Np0yY0NDTgiSeeuOgnX7JkCUpLS1FSUoJIJIKNGzfi/fffxwcffACrtecc/7p16/DHP/4RW7duhdPpxL333gtZlvH2228P6jlYI0NEo4EQAp5ABJ3+MCJqcg13FELgcG3PEMf3TrZFrTtTDFgxOx/LLi2AM0W70zPJTJYkWEw62Ex6pBh0I7JNN2x9ZFasWIE33ngDGRkZmDZtWlSx744dOy4sYgCnTp1CdnY2ysvL8bnPfQ6dnZ3IysrCtm3bcOuttwIAPvzwQ0yZMgXvvvsuLr/88k99TCYyRJTsfMEI2nwhhJXESmB6+5B0BkJwmo0ozrH2GwWgCoG3qlqwvcKNow1dUV+f6zBj1bxCLJmeq/lVgWQkSRJSDDrYzHpYjSOTvJxp2Ip9U1NTccstt1xUcOfS2dkzbTQ9PR0AsG/fPoTDYSxevLjvPpMnT8aYMWPOmcgEg0EEg/+YmeHxeIYlViLSlqoKHKn3oM0fQrrFGPNmXIkgGFHQ5guhO5R4J5HO7AwbVgUMsgRXhhVr5rswLd+JXR80YfteN2rbu6O+tijLitKSMbhyUhZPIMWYLEmwGHWwmPSwGHQJ8X9qyInMli1bhiMOqKqK+++/H1dccQWmT58OAGhsbITRaERqamq/++bk5KCxsXHAx9m0aRMefvjhYYmRiOLDO1Ut2FxejepmL8KKgEEnxbw9ejzrLeRN1IZ2B2ra8diuY/CHFDjMBjh0PdOUq5u78PBLH0AA6ApEn7KaPSYVpSUuzBubxhNIMaSXZaQYdbCadCO2bRRLcdPOcP369Xj//ffx1ltvXdTjPPDAA9iwYUPf5x6PBy6X62LDI6I48U5VCzburIQ3GEGaxQijTkZIUXG0oQsbd1bikRUzkjqZ6QqE0ZbADe1UIbBtjxv+kNI3PTmiqPAEwvAEIlEjBGQJ+OwlWSgtcWFSrl2boJOQQSfDZtLDYtLBpE/sbblBJTJz5szB66+/jrS0NMyePfu82dr+/fuHHMS9996Ll19+GW+++SYKCwv7bs/NzUUoFEJHR0e/qzJNTU3Izc0d8LFMJhNMJs7LIEpGqiqwubwa3mAEuQ5z3+8is6xDrkNGoyeIzeXVuHxCRkJcEh+KiKKixRuCP5TY/WCqmnxwt/rgMBsQjgi0+YM9R8TPup9elrB0Ri5WzXWhIC1Fk1iTjV6WYTX11LwkevJypkElMsuWLetLDpYvXx6zJxdC4L777sPOnTuxe/dujB8/vt/63LlzYTAY8Prrr2PlypUAgI8++gg1NTVYsGBBzOIgosRwpN6D6mYv0izGqD+oJElCqsWA6mYvjtR7MKPQqVGUsecJhNHmDUFNgqPUnYEQusMKfKGej7PJEmDUy/jGtRNx1eQcDSJMLmeeNrIY42YTJqYG9aoeeughrF27Fo8//jgeeuihmD35+vXrsW3bNrz44ouw2+19dS9OpxMpKSlwOp24++67sWHDBqSnp8PhcOC+++7DggULBnViiYiSS5s/hLAiYDxHAy6TTkbn6YGIySAUUdHiDSKQBGMFVCHw3ok2bHn7Y3QFo1+PXpaQZjHAZNAhFFZQmGrVIMrkYTHqYTXpYDXqk+7q5NkGnZ799re/xQ9+8APY7bHbo9y8eTMA4Morr+x3+5YtW/o6B//kJz+BLMtYuXIlgsEgrrvuOvziF7+IWQxElDjSLUYYdBJCigqzHH1pPKioMMgS0i1GDaKLHSEE2v1hdCZBQ7uwouKvHzajrMKNj1v9UetGnYw0iwEOsx6QgBZvCBOybCjOYSIzVD0Fu3pYjfpRdZpr0InMcPxnGsxjms1m/PznP8fPf/7zmD8/ESWWafkOFGXbcLShC7kOud/2khACHf4wpuTZMS0/cXtG+UMRtHoTryfM2bpDCl6ubMBze2txyhuMWjfqJBj0MtItRpj0MoKKQFcgDItRhzXzXf36ydDA9LIMs1GG2aAbdcnLmYa0YdbV1QWz2Xze+7DpHBENF1mWsG5RETburESjJ4hUiwEmnYygoqLDH4bNpMO6RUUJeSk9FFHR5kv8Yt52fwg79tfhD4fqBzxCfUVRBlaXuBBW1L4+Mt5QBAZJwoQsG9bMd2H2mDQNIo9/kiTBatQhxaiD2aDTfMZRvBh0Z19Zls97WkkIAUmSoCjxtZfLzr5EyadfH5nTzdQStY+M2tsTJsGHO9Z1dOPZvbV49UgjQmcNcdTLEq6ZmoNV8woxNuMfW0af1tmX/pG8WEzadNfV0rB09n3uuef6uu4SEWllYXEmLp+QkfCdfT2BMNoTuCcMABxr6kLZHjfePH4qqgeMxajDjTPzsHJOIbLs0W0xZEnCxFwOdjybQSfDcvrKSyI2qBtpQ0pkrrjiCmRnZw9XLEREgybLUsIesQ5GFLR4Qwgm6GkkIQT2fdKOsgo39td0RK2nWQxYOacQN8/Kh82cnEd+Y81s6C3U1UHPLaMh4b8wIqIRMpTRAvG47aKoAuXHTqGswo2qZm/UekFqClaXFOLaqbkw6vlm/GlMBh1sp49JM3m5cINOZMaOHQudLnk6ARIRjaShjBY430BFLQphA2EFr77fiGf31aKhMxC1PinXjttLXLiiOHPUnpwZDEmSYDbIPT1eeOUlZgadyJw8eXI44yAiSkphpaep3WAnVJ9roOKJU148tusYNlwzccSSmc7uMP5wsB47DtShc4CrSPPHp6O0xIVZhU7WcZxDIk6TTjTcWiIiGgZCCHR2h9HuH3xTu4EGKgKASS8h02ZEizeEbXvcmOVKHdZtpiZPAM/uq8UrlQ0IhPufQJIl4KrJ2Vhd4kJRFgt1B2LQnZ4mbdTDbDj/iV+6eExkiIhirDukoNUXjDqG/GnOHKjYm8T0kiDBbjbA3epDVZNvWE77nDjlRVmFG2982Bx1Asmsl3H9jDzcOq8QuY7z9xMbjUwGXV+Pl96BjKoq8H5dYp+sSwRMZIiIYuRim9p1BkIIqwIO3cBvdkadhC4h0BmI3SwpIQQO13airMKN9062Ra07UwxYMTsfy2YVwGkxxOx5E50kSUgx6GAxDdxVt1+vI0XAoEvcXkfxbsiJzIkTJzBhwoThiIWIKCHFqqmd02yEQe6piTHpo5OZkCJgkCQ4zRc/S0oVAm9XtaKsogZHG7qi1nMdZqyaV4gl03NhNvCgB9C/Od356l3eqWrBxp2V8AYjSLMYYdTJCCkqjjZ0YePOSjyyYgaTmRgaciJTXFyMRYsW4e6778att976qSMLiIiSlRACnkAEHf7YNLUrzrHClWHFiVPefjUyACDQM4voYgcqhiIqdn3QhO173aht745aL8qyorRkDK6clMUTSKelGHWwnR7G+GlbQ6oqsLm8Gt5gBLkOc199jFnWIdcho9ETxObyalw+IYPbTDEy5LNf+/fvx8yZM7Fhwwbk5ubiK1/5Cvbs2TMcsRERxS1/KILa9m60eoMx68wrSxLWzHfBYtShxRtCIKJCFQKBiIoWb+iiBip6gxE8vacGa379Hn6861hUEjN7TCp+uHIGnvziXFw9JXvUJzFGvYwMqwlj0i3Ic6bAbjYMKvE4Uu9BdbMXaRZjVJGvJElItRhQ3ezFkXrPcIU+6gz5isyll16Kxx9/HD/+8Y/xhz/8AVu3bsVnPvMZTJw4EWvXrsUXv/hFZGVlDUesRESa6w4p6OgODfo49VDNHpOGDddM7Osj0yXERQ1UbPEGsWN/HV46VA/fWTHLEvDZS7JQWuLCpFx7LF9GQtLLMmxmPWwm/QU39GvzhxBWBIzn6BFj0snoVAXa/LGrcxrtBj008lyCwSB+8Ytf4IEHHkAoFILRaMSqVavwwx/+EHl5ebGK84JxaCQRxYI3GEFnd3jExgpcbGffmjY/nqlwY9fRJoSV/r/mDToJS6bnYtVcFwrSUmIdekLRyRIsRj3sZn1MaoEqazvxld/thdU08ON1hxX4gxH86ovzEnbExkgZlqGRZ9q7dy9+85vfoKysDFarFd/4xjdw9913o7a2Fg8//DCWLVvGLSciSmhCCHQFI+j0hxFWhnaU+mJd6EDFD+o9eLqiBu9UteLsv1JtJj2WXZqPFbMLkG69+ILhRNVbtGs16WGJ8UTpafkOFGXbcLShC7mO/j1khBDo8IcxJc+Oafn8wzpWhpzIPPbYY9iyZQs++ugjXH/99Xjqqadw/fXXQ5Z7LqONHz8eW7duxbhx42IdKxHRiNAygbkQqhDYc7INT+9xo7KuM2o9y2bCrXMLcMPMPFiMo7frRsrp5MU2iKLdCyXLEtYtKsLGnZVo9ASRajHApJMRVFR0+MOwmXRYt6iIhb4xNOR/0Zs3b8batWtx1113nXPrKDs7G//7v/970cEREY20rkAYHQmSwEQUFW982IyyCjc+bvVHrY/NsKC0xIWrJmfDMErn+hh0Muyn615GarbRwuJMPLJiRl8fmc7T87Km5NnZR2YYDKlGJhKJ4Hvf+x6+/OUvo7CwcDjjihnWyBDRYATCClp9oRGrgfk056uR6Q4peLmyAc/vq0VzVzDqa6fnO1A639VzxHcUtseXJQlWU+zqXi6UqgocqWdn3ws12PfvIRf72u12VFZWJszWERMZIjofRRVo84XQFYgeiqiVc02/vmlmHqpOefHiwXp0BaK7By8sykBpiQvTC0ZnEanFqIfN3DNZmvONEt+wFfteddVVKC8vT5hEhohoIEIIeLoj6OiOTTO7WBlo+rU/pOD9uk7s+6Q96v56WcLiKTlYXVKIsRkX3igvUZkMOtiMelhNuhHbOqL4MuREZunSpfj2t7+NyspKzJ07F1Zr//84N998c8yCIyKKtd5uvJ3+MCJqfNXBnD39OhhW0egJwRuMvvpiMepw48w8rJxTiCy7SYNotWPUyz2ddk36UVv7Q/8w5K2l3tNJAz6YJEFR4mN/uRe3loiolycQjuuTSMcavXjwhcOQZBneQAT+Aep1ZAm4eWY+1n5mPGzm0XMCyaCTe04cXUSzOoqt4a4BGratJTXO/oIhIvo03SEFrb4gQpH4/f2lqAJvHm9Gmz8MZYA/Lw06CakpBoRVFQuKM0ZFEqOTpb7khYMr40s8Tfe+qP8JgUCAQyOJKG6FIirafCH4Q9FbM/EiGFbw6pFGPLO3Fg2dgQHvI0uAXgIkCTDKckymX8er3mZ1NrMeKQYW7cajeJvuPeTrc4qi4Lvf/S4KCgpgs9lw4sQJAMCDDz7I3jFEFBcUVaDFG0Rtuz9ukxhPdxi/e/cT3P4/7+Hx16sGTGJ0MmCQe65MBBWBU10hpFoMFzX9Ol5ZjHpk2U0Ym25BtsMMi1HPJCYOnT3d22zQQZYlmA065DpM8AYVbC6vhjqCBfRDTmS+//3vY+vWrXj00UdhNP7jr4Lp06fj17/+dUyDIyIaCiEEOrvDqG33w9MdP8epz9TkCeDnf61C6f/8HVve+RgdZ8QpS4Dd1LOFIqOnH4rUW3MQPwerYsZk0CHDasLYDCtyneZBT5gm7cTjdO8hby099dRTePLJJ3H11Vfjq1/9at/ts2bNwocffhjT4IiIBqs7pKDFG4zbQt4Tp7zYvrcWb3zYHHXc26yXcf2MPMwZk4YnXj+GFKMe3kAEIUWBqvZsKZkNPdstHf4wqpp8FzSHKR4YdP84ccSi3cQTj9O9h5zI1NXVobi4OOp2VVURDsfnX0BElJwUVcAbiKArGI7LQl4hBA7XdaJsjxvvnWyLWneY9VgxuwDLLy2A02JAxcdtCKsCGVYDnCl6BMMCilChk2SYDBKEAFr9IXQGRu5NIhZYtJs80i1GGHQSQooKsxz9swwqKgyyhHTLyNVxDTmRmTp1Kv72t79h7Nix/W5/7rnnMHv27JgFRkR0Lt0hBV2BMHwhBUPsIDEiVCHwdlUryipqcLShK2o912HGbfMKsXR6br83dqfZCIMsIawImPQyzAYJZ1YABBUVBklKiGJfFu0mp3ic7j3kROY73/kO7rzzTtTV1UFVVezYsQMfffQRnnrqKbz88svDESMRUUJMpA5FVOz6oAnb97pR294dtV6UZUVpiQtXTsqGboBakOIcK1wZVpw45UWmzQgJZ7xJQKArEMaELFtcF/umGHU9W0fDOGGatBOP072H3BAPAP72t7/hP//zP3Ho0CF4vV7MmTMH3/nOd3DttdcOR4wXhQ3xiBKbogp4usPwBMJxNUrgTN5gBC8dqsfz++vQ5ove9rnUlYrb57swb2zap16ZOHNEgd1sgFEnIaT0JDEWow4brpmI2WPShuulXBCjXobdZOCYgFGkXx+Z0/PAYt1HZtiGRiYaJjJEiSkQVuDpjt/tIwBo8QaxY38dXjpUD1+ofxdeCcBnJ2aitMSFyblD+93Tb2ikEDBIPUMj18x3xU0So5Ml2Ew9QxpNeta9jEYJ29n3y1/+Mr7whS/gyiuvvJj4iIgG1BUIo7M7Pot3e9W0+fFMhRu7jjYhfFYbXoNOwpJpuVg1z4WCtJQLevzZY9Iwy5WKqiYfOgMhOM1GFOdYIWtcZyJLEiyse6HTZFnCjELtJ60POZE5deoUlixZgqysLJSWluKOO+7ApZdeOgyhEdFo4g9F0OYLxXUC80G9B09X1OCdqtaoti42kx7LLs3HitkFSLdefDGuLElxccRaOp28WE16WI1MXij+XNDWUnt7O5599lls27YNf/vb3zB58mTccccdWLNmDcaNGzcMYV44bi0RxbdgREGbL4TuUHwNnO0lhMB7J9tQVuHG4drOqPVMmxG3zS3EDTPzYDEmz/yj3r41VqN+wMJkouE2YjUytbW1ePrpp/Gb3/wGx48fRyQSX+3AmcgQxSd/KIKuQAS+YHz9zugVUVS88WEztu+txckWX9T62AwLVs9z4eop2TAkSYGrQSfDbu5pVpcsr4kS17DVyJwpHA5j7969eO+99/Dxxx8jJyfnYh6OiJJcRFHhCUTgDUQQUeNzC6k7pOCPlQ14bl8tmruCUeszChxYXeLC5RMyNK9ZiQW9LMNq0rFolxLWBSUyf/3rX7Ft2zY8//zzUFUVt9xyC15++WVcddVVsY6PiJKAEALt/p4i3ng9gdTuD2HngTq8eLAeXYHoq0RXFGVgdYkL0wu0L268WLIkwWLSwW4yIMXI5IUS25ATmYKCArS1tWHJkiV48skncdNNN8FkMg1HbESUBHzBniLeeG1iV9/RjWf21uLVI41RhcZ6WcLiKTlYXVKIsRnx24RuMCRJQkpf3QuLdil5DDmR+Y//+A/cdtttSE1NHYZwiChZhCIq2nwh+EPxWQNzrKkL2yvcKD92Cmf32bMYdbhxZh5WzilElj1x/1DrPS5tMelhMejYaZeS0pATmXvuuWc44iCiJBEIK+jsDsdlEa8QAvtrOlC2pwb7ajqi1tMsBqycU4ibZ+XDZtb2BJIqxAX1keGMIxptBvU/9ZZbbsHWrVvhcDhwyy23nPe+O3bsiElgRJRYukMKOrrj8xi1ogq8eewUyircON7sjVovSE3B6pJCXDs1F0a99qd1+nX2Pd3+/dM6+/aOCbCZeVyaRpdBJTJOp7Mvq3c6E7/QjYhipzukoN0fQiAcfwlMIKzgtSONeGZvLRo6A1Hrk3LsKJ3vwmeKM+Pmzf/MWUsOswEOXc807BOnvHhs17F+s5Z0sgSrSQ+bSd9vijbRaMJZS0R0QeI5gfF0h/HiwXrsOFCHzu5w1Pr8cWlYXeLCpa7UuNp6UYXAt56vPOf06xZvCBOybHii9FI4LUYW7VJSG5E+MkQ0+viCEXR2h+MygWnyBPDsvlq8UtmAQLj/CSRZAj4/KRulJS4UZWvf+n8gVU0+uFt9cJgN/ZIYAJAlGakWI+rb/WjzhVGQZtEoSqL4MqhEZvbs2YPO+vfv339RARFR/FFVga5ABJ5AOC6PUZ845cX2vbV448NmKGcdQTLpZVw/Iw+3zS1ErtOsUYSD0xkIIawKOHSnf99KgE6SIMsSZEmCXpbgDUbQ5g9pGyhRHBlUIrN8+fJhDoOI4lEwoqDrdCdeNc52oYUQOFzXibI9brx3si1q3WHWY8XsAiy/tABOi0GDCIfOaTbCIEtQVAGjUQdZQr8/IoOKCoMsId1y8UMpiZLFoBKZhx56aLjjIKI4IYSAN9gzByket49UIfB2VSu2V9Tgg4auqPUchwm3zXVh6YxcpCRQAaxBJ6NkfBom5trxYaMXVpO+XxIjhECHP4wpeXZMy2e9H1GvC6qR6ejowHPPPYfq6mp885vfRHp6Ovbv34+cnBwUFBTEOkYiGgG9c5C6AuGo7Zl4EIqo+MvRJmyvcMPd3h21XpRlRWmJC1dOyo6bE0ifprfni938j1EB//fKYmzcWYlGTxCpFgNMOhlBRUWHPwybSYd1i4rY2I7oDENOZA4fPozFixfD6XTi448/xj333IP09HTs2LEDNTU1eOqpp4YjTiIaJt0hBZ5AfDawAwBvMIKXD9Xj+f11aPVF14Zc6krF7fNdmDc2LWFO8Bh0MhzmgXu+LCzOxCMrZmBzeTWqm73oPN1HZkqeHesWFWFhcaZGURPFpyEnMhs2bMBdd92FRx99FHa7ve/266+/HmvWrIlpcEQ0fHzBCDq6wwjG4fYRALR6g3h+fx1eOlQP31lN9iQAn52YidISFybnJsY2iyRJsJp0cJgNn9rzZWFxJi6fkIEj9R60+UNItxgxLd/BKzFEAxhyIlNRUYFf/epXUbcXFBSgsbExJkER0fDxBiPo8IeiBiTGi5o2P57Z68auD5oQVvpvcRl0EpZMy8WqeS4UpKVoFOHQmAw62M162Iz6ISUisixhRiEbkBJ9miEnMiaTCR6PJ+r2Y8eOISsrKyZBEVFshSIqvMEIfMFIXB6fBoAP6j0oq3Dj7aoWnF2hYzPpsezSfKyYXYB0a/yf2OntuGs362HSJ07BMVEiGnIic/PNN+M///M/8cwzzwDouVxaU1ODb33rW1i5cmXMAySiCxNWVPiCEXiDkbi9+iKEwHsn21BW4cbh2s6o9UybEbfNLcQNM/NgMcZ//06LUQ+bWc+Ou0QjaMgjCjo7O3Hrrbdi79696OrqQn5+PhobG7FgwQK88sorsFqtwxXrBeGIAhpNAmEF/pAS11degJ4TUm98dArbK9w42eKLWh+bbsHqEheunpINg077IY7nk2LUwWLQw2rSQR/nsRIlkmEbUeB0OrFr1y689dZbOHz4MLxeL+bMmYPFixdfVMBEdGGCEQXeQM+Vl3g8Nn2m7pCCP1Y24Ll9tWjuCkatT893oHS+C5dPyIAcp1c0JEmCxag7/cFJ00Ra49BIogSkqL1N68Jxu210pnZ/CC8cqMMLB+vRFYg+5r2wKAOlJS5ML4jf4laDTu4p2jXpeeWFaAQMyxUZVVWxdetW7NixAx9//DEkScL48eNx66234otf/CL3hImGkaoK+EIR+IIKusMKEuFvkPqObjy7txZ/OtIYlXDpZQmLp+RgVUkhxmXE15b0mXqLdhOhRodoNBr0/0whBG6++Wa88sormDVrFmbMmAEhBI4ePYq77roLO3bswAsvvDCMoRKNPkII+EMKvMEI/KHESF4A4FhTF7ZXuFF+7BTO3u1KMehw48w83Dq3EFl2kzYBfgqdLMFuNsBh5tUXong36ERm69atePPNN/H666/j85//fL+1N954A8uXL8dTTz2FL33pSzEPkmi0CUVUdAXCCVH30ksIgf01HSjbU4N9NR1R62kWA26ZU4CbZ+XDbo7PIY4pp8cF8NQRUeIYdI3Mtddei6uuugrf/va3B1x/5JFHUF5ejtdeey2mAV4s1shQohBCwBdS4OkOx+WwxnNRVIE3j51CWYUbx5u9UesFqSlYXVKIa6fmwqiPv6sbOlmCzaSH3WyIy/iIRquY18gcPnwYjz766DnXly5diieeeGJoURIRIoqKrkDPtOmIGv+Fu72CYQWvHmnEM3tr0dAZiFqflGtHaYkLnynOHJaTPaoQqGryoTMQgtNsRHGOdUgnnUwGHZwpvPpClOgGnci0tbUhJyfnnOs5OTlob2+PSVBEo0Eg3HP1xZdAtS8A4OkO48VD9di5vw4d3eGo9fnj0lA6fwxmFTqHLUE4UNOObXvccLf6ED49VNGVYcWa+S7MHpN2zq/rnTbtSPn0eUdElBgGncgoigK9/tx31+l0iETic3ouUTzxBSPoTLDtIwBo8gTw3L5a/LGyAYFw/ytHsgR8flI2SktcKMq2DWscB2ra8diuY/CHFDjMBjh0EsKKwIlTXjy26xg2XDMxKpnh0Wmi5DWkU0t33XUXTKaBTxkEg9HNrYioR+/2kTfOO+4O5GSLD2UVbrzxYXNU4bFJL+P6GXm4bW4hcp3mYY9FFQLb9rjhDynItBkhQTodh4RMmxEt3hC27XFjlisVOlmGxajj0WmiJDfo/9133nnnp96HJ5aI/qG3eLcrEEZ3KLGuvgghUFnXibIKN/5+oi1q3WHWY/nsAqy4tABOy8idQKpq8sHd6oPDbOhLYnpJ6DkyXdvmR0tXCCXj09l1l2gUGHQis2XLluGMgygpCCHQHT7d9yWoQE2g2heg54rHO1WtKKuowQcNXVHrOQ4TbpvrwtIZuUjRoMakMxBCWBVw6M5KYiSp5/SRLKE7rCCoqExiiEYJXm8lioHA6eTFl0B9X84Uiqj4y9EmbK9ww93eHbVelGVFaYkLV07K1jRBcJqNMMg9NTFmgwxZ6jk+3VtU3B1WYZAlpFuMmsVIRCNL06q3N998EzfddBPy8/MhSVJUZ+C77roLkiT1+1iyZIk2wRKdRVEFOv1huNv8qO/ohqc7nHBJjDcYQdmeGtzx6/fwX38+FpXEXOpy4ge3zMCTX5yLq6fkaH6VY1KuDeOzbPAGIzDoJOh1cl8SI4RAhz+MomwbpuWzZxTRaKHpFRmfz4dZs2Zh7dq1uOWWWwa8z5IlS/pta52r2JhoKFRV4Ei9B23+ENItRkzLd0AexJt0Is47GkirN4jn99fhpUP18J1VvyMB+OzETJSWuDA5V/uEoPfItNWkh8Wow9euvgQbd1ai0RNEqsUAk05GUFHR4Q/DZtJh3aKiQf0siSg5aJrILF26FEuXLj3vfUwmE3Jzc0coIhoN3qlqwebyalQ3exFWBAw6CUXZNqxbVISFxZlR9++dd+QLRhKu58vZatr8eKbCjV1HmxBW+r8Og07CddNysWpeIQrTLBpF+A9mgw42sx42o75fYrKwOBOPrJjR9zPsPN1HZkqe/Zw/QyJKXnFfI7N7925kZ2cjLS0NV111Fb73ve8hIyPjnPcPBoP9joJ7PJ6RCJMSxDtVLdi4sxLeYARpFiOMOhkhRcXRhi5s3FmJR1bM6Hsj7A79o+4l0Yp2z/ZBvQdlFW68XdWCs1+J1aTDsln5uGVOIdKt2taW9A5rtJn05x0XsLA4E5dPyLigq2pElFziOpFZsmQJbrnlFowfPx7V1dXYuHEjli5dinfffRc63cAnJjZt2oSHH354hCOlRKCqApvLq+ENRpDrMPfVVphlHXIdMho9QfxidxUm5tjhDykJNS5gIEIIvHeyDWUVbhyu7Yxaz7QZcevcQtwwIw9Wk7a/CkwGHRynG9YNthuwLEuYUegc5siIKN4NemjkcJMkCTt37sTy5cvPeZ8TJ06gqKgIf/nLX3D11VcPeJ+Brsi4XC4OjSRU1nbiK7/bC6tJH9WeXhUCvmAE/mAE/7lsBibmDm932uEUUVT89aNT2F7hxokWX9T62HQLVpe4cPWUbBg07HIrSxKsJj3s5uifBxFRzIdGxoMJEyYgMzMTVVVV50xkTCYTC4JpQG3+EMKKgPH0m7cQAqroOX0khIBelhAWAp2BkMaRXpjukIJX3m/As3tr0dwV3Wl7er4DpfNduHxCxpCGK8aaxaiHzaznsEYiiomESmRqa2vR2tqKvLw8rUOhBJRuMcKgkxCMKDDoZChC4MyCkZAiYJAkOM2J1YOkwx/CzgN1ePFgPTyB6HlnCyZk4Pb5Lkwv0G4bhrOOiGi4aJrIeL1eVFVV9X1+8uRJHDx4EOnp6UhPT8fDDz+MlStXIjc3F9XV1fjXf/1XFBcX47rrrtMwakpEwYiCglQzCtNScLzZ229ODwAICHQFwpiQZUNxjlXDSAevvqMbz+6txZ+ONCIU6V/Po5clXD0lG6vmuTA+U5vXI0kSrCYdHGZOmiai4aNpIrN37158/vOf7/t8w4YNAHrmOm3evBmHDx/Gb3/7W3R0dCA/Px/XXnstvvvd73LriD6VqvaMCvCHFHSfUbi7usSFx3YdQ4s3BLvZAKNOQkjpSWIsRh3WzHdpuu0yGMebulBW4Ub5sVM4u/9eikGHG2fm4da5hciya/P/RCdLPVOpUwyaN9AjouQXN8W+w2WwxUKU+MKK2pe4nK9Z3YGadmzb44a71Yew6NlOcmVYsWa+C7PHpI1w1IMjhMCBmg48XeHGvk/ao9bTLAasnFOIm2blwW4euSGOZ9LLMpwpBtjNeh6DJqKLlpTFvkRnC5y+6uIPRaK2V85l9pg0zHKloqrJh85ACE6zEcU51ri8EqOoAn87fgplFW4ca/JGrRekpmDVvEJcNy33vH1XhpPJoIMzxcDiXSLSBBMZSjiBM6ZLX2ivF1mS4vqIdTCs4NUjTXhmrxsNnYGo9Uk5dpTOd+EzxZmabd9YTXo4U1j/QkTaYiJDCSEYUeAL9owJCCuJ3ajufDzdYbx4qB4799ehozsctV4yLg2rS1yY7UrV5OqHJEmwm3sSGC170BAR9WIiQ3ErrKjwBSPoCiR38gIAzZ4Ant1Xiz9WNiAQ7v9aZQm4clI2SktcKM7W5iqSXpbhSNHDbmYBLxHFFyYyFFfCigp/UIE3FEEwrHz6FyS4ky0+bK9w4/UPm6GcdQTJpJexdHoubptXiDxniibxmQ26vv4vrH8honjERIY0N9qSFyEEKus6UVbhxt9PtEWtO8x6LL+0AMtn5yPVMvLN+XpHBzhS9DDpWf9CRPGNiQxpIhRR4Q9F4A0O/rRRolOFwDtVrSirqMEHDV1R6zkOE26b68LSGblI0aCA1qCT4TDz+DQRJRYmMjRiQpGemhdvkhfsni0UUfGXo03YXuGGu707an1ClhW3l7iwaGKWJu37U4w93Xe1noA9VKoqcKTegzZ/COkWI6blO5iAEY1CifWbixJOb8HuaLry0ssbjODlQ/V4fn8dWn3RgygvdaWitMSFknFpI15/IksSbGY9HGaDZv1nLsY7VS3YXF6N6mYvwoqAQSehKNuGdYuKsLA4U+vwiGgEMZGhmIsoKnyhnl4vo6Hm5Wyt3iCe31+Hlw7Vwxfq//olAJ+9JBOrS1yYkjfynaaNehmOFANsxsTdPnqnqgUbd1bCG4wgzWKEUScjpKg42tCFjTsr8ciKGUxmiEYRJjIUE4oq4AtF4AtG0B0afckLALjb/Ni+141dHzQhrPQ/gWTQSbhuWi5WzStEYZplROOSJAlWow6OJGhep6oCm8ur4Q1GkOsw913JMss65DpkNHqC2FxejcsnZCRsokZEQ8NEhi6Yqgr4wwq8gch5ZxvFI1WImI0oONrgQVmFG28db8HZ3wGrSYdls/Jxy5xCpFtH9gRSMvZ+OVLvQXWzF2kWY9R2nCRJSLUYUN3sxZF6D2YUOjWKkohGEhMZGhIhBPyhng67vlBiJS+9+g2NVAUM8tCHRgoh8N7JNmyvcONQbWfUeqbNiFvnFuKGGXkjXkRr0MlItRiSsvdLmz+EsCJgPEdRtEkno1MVaPNH1yQRUXJiIkOD0h1S0BUMwx9UoCZg8tLrQE07Htt1DP6QAofZAIdOQlgROHHKi8d2HcOGayaeN5mJKCre+OgUnqlw40SLL2p9bLoFq0pcWDwle8Rb+Jt7hzcm2OmjoUi3GGHQSQgpKsxy9DZZUFFhkCWka9B/h4i0kby/8eii9Q5n9AUjUV1nE5EqBLbtccMfUpBpM0JCz9UKk15Cps2IFm8I2/a4McuVGrXN1B1S8MfKBjy3rxbNXcGox56W70BpiQsLijJGfIp2ilGHNIsx4etfBmNavgNF2TYcbehCrkPud8VJCIEOfxhT8uyYlj/yhdREpA0mMtRPMg9nrGrywd3qg8Ns6EtiekmQYDcb4G71oarJ1zcZu8Mfws4DdXjhYD26ApGox1wwIQO3z3dhesHI12NYTXqkWgyjqvuuLEtYt6gIG3dWotET7Hn9OhlBRUWHPwybSYd1i4pY6Es0ijCRoVHTqK4zEEJYFXDoBn6TM+okdAmBzkAI9R3deHZvLf50pDGq/41OlrB4SjZWzXNhfKZ1JELvJ8WoQ7rVOKoSmDMtLM7EIytm9PWR6Txd5zQlz84+MkSjEBOZUSqiqPCNovlGAOA0G2GQe2piTProZCakCEAVeHZvLfbXtOPs3bQUgw43zszDrXMLkWU3jVDU/2DUy0i3GmEx8r/twuJMXD4hg519iYiJzGjS2+vFG4ggMEqSlzMV51jhyrDixClvvxoZIXqOkTd5goioAns/ae/3dWkWA26ZU4CbZ+XDbjaMeNy9p5C0eO54JssSj1gTEROZZKf2NapTEq7XS6zJkoQ18114bNcxtHhDsJn0CEVUtPvDCA2wpZafasbqeS5cOzUHJg0KafWyDKfFAIc5+Y5RExHFChOZJJQMvV6Gy+wxabjvqmL8YvcJ1Lb7o7aPAGBijg2lJWPw2UsyNWkkp5MlpKYY4UhhAkNE9GmYyCQJIQS6Tx+XTvReL8PF0x3Gi4fqsXN/HTq6w1HrJePSsLrEhdmuVE0SCJ0swZligMNsYK0HEdEgMZFJcN2nhzP6Q8nR62U4NHsCeHZfLf5Y2YBAuP8WkiwBV07KRmmJC8XZNk3ik6WeBMaZwgSGiGiomMgkmIiiojusoDvUU/PC5OXcTrb4sL3Cjdc/bI76Ppn0MpZOz8Vt8wqR50zRJL7eBMaRkjyzkIiIRhoTmQQQCPfUu/hDSlL3eYkFIQQq6zpRVuHG30+0Ra07zHosn12AFZcWwGnR5hQQt5CIiGKHiUycYvIyNKoQeKeqFWUVNfigoStqPcdhwm1zXVg6IxcpGrXy7y3itZv1TGCIiGKEiUwcCUXUvtlGTF4GJxRR8ZejTdhe4Ya7vTtqfUKmFaXzXbhyYhb0IzzEsZcsSXCkGJDKGhgiophjIqOx3g67XcFwVCt8OjdfMIKXDjfg+f21aPWGotYvdTlRWjIGJePSNDvCLEkS7GY90ixG1sAQEQ0TJjIaUFUBb6jnykt3aPR12L0Yrd4gnt9fh5cO1cN31vdOAvCZSzJRWuLClDxtpx/bTicwBo2uAhERjRZMZEZIb5M67+m6FzapGxp3mx/b97qx64MmhJX+3zuDTsJ103Kxal4hCtMsGkXYw2rqSWCMeiYwREQjgYnMMOvt8+ILRtik7gIcbfCgrMKNt4634OzvntWkw7JZ+bhlTiHSrUZN4uuVYtQhzWKEWaNCYiKi0YqJzDDoPXHkCyqIqKx7GSohBN472YbtFW4cqu2MWs+wGXHrnELcODMPVpO2/4SNehkZVhNSjExgiIi0wEQmRnhc+uJFFBVvfHQKz1S4caLFF7U+Jt2C1SUuXD05W/OtG4NORprVCJvGiRQR0WjH38IXIRBW4AmE0R1ih92L0R1S8Mr7DXh2by2au4JR69PyHSgtcWFBUQZkjYcociI1EVF8YSJzEXzBCLyBiNZhJKwOfwgvHKjHCwfr4Bng+7hgQgZun+/C9AKnBtH1J0sSUi0985CYwBARxQ8mMjQgVQhUNfnQGQjBaTaiOMcas6sh9R3deHZfLV59vxHBs3rn6GQJi6dkY9U8F8ZnWmPyfBdDkiQ4zHqkshcMEVFcYiJDUQ7UtGPbHjfcrT6EVQGDLMGVYcWa+S7MHpN2wY97vKkLZRVulB87hbN34lIMOtw4Mw+3zi1Elt10ka8gNuxmA9IsBs06AhMR0adjIkP9HKhpx2O7jsEfUuAwG+DQSQgrAidOefHYrmPYcM3EISUzQgjsr+lAWYUb+z5pj1pPsxiwYnYBbp6VD0eKNkMcz8ZeMEREiYOJDPVRhcC2PW74QwoybUZI6NlKMeklZNqMaPGGsG2PG7NcqZ+6zaSoAn87fgplFW4ca/JGreenmrF6ngvXTs2BKU56r5gMOmRY2QuGiCiRMJGhPlVNPrhbfXCYDX1JTC8JEuxmA9ytPlQ1+TAx1zbgYwTDCl490oRn9rrR0BmIWp+YY0NpyRh89pLMuKk5MehkpFuNmvekISKioeNvburTGQghrAo4dAMnGEadhC4h0BmIHtLYFQjjhYP12Lm/Dh3d4aj1knFpWF3iwmxXatyc+pEkCakpBqRaeBKJiChRMZGhPk6zEQa5pybGpI9+Yw8pAgZJgtP8j3EAzZ4Anttfi5cPNyAQ7n8CSZaAKydlo7TEheLsga/gaCXFqEOG1cQ6GCKiBMdEhvoU51jhyrDixClvvxoZABAQ6AqEMSHLhuIcK062+LC9wo3XP2yOagZo0stYOj0Xt80rRJ4zZaRfxnnpZAnpViPs5vgoLCYioovDRIb6yJKENfNdeGzXMbR4QzDpZUgSIAQQjKhIMci4fHw6/v2F9/H3E21RX+8w67F8dgFWXFoApyW+EgVJkuBMMSA1xQA5TmpziIjo4jGRoX5mj0nDTTPz8fs9Nej0BPsmTpsMMox6GT/fXR31NTkOE26b68LSGblIicMTP+wHQ0SUvJjIUD8Hatrx0uF66CQgy25EMKKiK6ggEFYRCPefgzQhy4rSEheunJgVl0mCxahHupX9YIiIkhkTGerT20fGF4xAL0to8YUHHIZ5qcuJ0pIxKBmXFpenfQw6GRk2IyxG/vMmIkp2/E1PffZ93IGj9Z3ojqgQAwzzTjHoYNZL+Ornis/ZR0ZLsiQhzWKEI4WTqYmIRgsmMoSaNj+e2evGa0eaoq7ASOgp4k2zGKHXSWj1hwbsI6M1u9mAdCsHOxIRjTZMZEaxow0ePL3HjberWnD2BRhZApwpBqSl/KNINhBRo/rIaI1jBYiIRjcmMqOMEAJ7Pm5D2R43DtV2Rq0bdBL0soQ8pxl6+R9Fsmf3kdGaTpaQZjXCwX4wRESjGhOZUSKiqPjrR6ewfa8bJ075otbHpFuwusSFTKsBT7xRhXZ/GHazAUadhJDSk8RYjDqsme/61IGRw81m0iPDZuI2EhERMZFJdt1hBa9UNuDZvbVo7gpGrU/Nc+D2+S4sKMroS1A2XCNj2x433K0+dImesQQTsmxYM9+F2WPSRvol9OFpJCIiOhvfEZJUhz+EnQfq8OLBengCkaj1yyek4/aSMZhR6Ixamz0mDbNcqahq8qEzEILTbERxjlWzKzGyJMGR0tPUjqeRiIjoTExkkkx9Rzee3VeLV99vRDDSf4ijTpaweEo2Vs1zYXzm+etcZEnS/Ii1JElwmPVItfA0EhERDYyJTJI43tSFsgo3yo+dwtk97FIMOtw4Mw8r5xQg22HWJsAhkCQJdrMeqSnDN1ZAVQWO1HvQ5g8h3WLEtHwHZzARESUgJjIJTAiBAzUdeLrCjX2ftEetp1kMuGVOAW6elZ8w055tJj3SrEYYhnHkwTtVLdhcXo3qZi/CioBBJ6Eo24Z1i4qwsDhz2J6XiIhij4lMAlJUgb8dP4WyCjeONXmj1vNTzVg9z4Vrp+bAlCD9VcwGHdJHoB/MO1Ut2LizEt5gBGkWI4w6GSFFxdGGLmzcWYlHVsxgMkNElECYyCSQYFjBax804Zm9btR3BKLWJ+bYUFoyBp+9JDNhakqMehlpFiOspuH/p6iqApvLq+ENRpDrMPcVDptlHXIdMho9QWwur8blEzK4zURElCCYyCSArkAYLx6sx84DdWj3h6PW541NQ+l8F2a7UhPmVI9BJyPNaoRtBBKYXkfqPahu9iLNYoz6PkmShFSLAdXNXhyp9wx4mouIiOIPE5k41uwJ4Ln9tXj5cAMC4f4nkGQJWDQxC6UlLlySY9cowqHTyzLSrAZNanba/CGEFQHjOepvTDoZnapAmz/+ZkkREdHAmMjEoZMtPjyz142/HG2OGuJo0stYOj0Xt80rRJ4zRaMIh673KHWaxajZtk26xQiDTkJIUWGWo2txgooKgywh3RI/s6SIiOj8mMjEkcraTjxdUYO/n2iLWnOY9Vh+aQGWz85HaoK90ZoNOmTYjDDptS08npbvQFG2DUcbupDrkPttLwkh0OEPY0qeHdPyHRpGSUREQ8FERmOqEHi3uhVlFW4cqfdErWfbTVg1rxBLZ+QhJUFOIPWKt8GOsixh3aIibNxZiUZPEKkWA0w6GUFFRYc/DJtJh3WLiljoS0SUQJjIaCQUUfH60SZs31uLmjZ/1PqETCtK57tw5cSsYWsKN5zidbDjwuJMPLJiRl8fmU5VwCBLmJJnZx8ZIqIExERmhPmCEbx8uAHP7a9Fqze6qPRSlxOlJWNQMi4tYU4gnSkRBjsuLM7E5RMy2NmXiCgJxO+7TZJp9Qax40Ad/nCoHr6g0m9NAvCZSzJRWuLClLzErM/oLeZNt0YfbY5HsizxiDURURJgIjPM3G1+PLO3Fn/+oBFhpf8JJINOwrVTc7FqXiFc6RaNIrx48VLMS0REo4+mxRdvvvkmbrrpJuTn50OSJLzwwgv91oUQ+M53voO8vDykpKRg8eLFOH78uDbBDtHRBg/+4w9HcNeWCvyxsqFfEmM16nD7fBe2ffky/Mu1ExM2idHJEjLtJuSnpjCJISIiTWh6Rcbn82HWrFlYu3Ytbrnllqj1Rx99FE888QR++9vfYvz48XjwwQdx3XXX4YMPPoDZHH9TnIUQ2PNxG7ZXuHHQ3Rm1nmEz4tY5hbhxZt6ItOQfLr3TqdMsxrgr5iUiotFF03fTpUuXYunSpQOuCSHw05/+FP/+7/+OZcuWAQCeeuop5OTk4IUXXkBpaelIhnpeEUXF7mM9QxxPnPJFrbvSUlBa4sLVU3Jg1CfeCaQzWU09CUyivw4iIkoOcXtZ4OTJk2hsbMTixYv7bnM6nbjsssvw7rvvnjORCQaDCAaDfZ97PNG9WWKlO6Rgx/5aPLuvFk2eYNT61DwHbp/vwoKiDMgJUAB7PiaDDhkjMJ2aiIhoKOI2kWlsbAQA5OTk9Ls9Jyenb20gmzZtwsMPPzyssbX5Qtj6zsf47Tsfo7M7eojj5RPScXvJGEwvcCTECZ7z0WK4IxER0WAl3bvTAw88gA0bNvR97vF44HK5Yvocr1Q24InX+xcd62QJi6dkY9U8F8ZnWmP6fFrQyRJSU4xwpOgTPhkjIqLkFbeJTG5uLgCgqakJeXl5fbc3NTXh0ksvPefXmUwmmEymYY3t1rmF+OlfjqHFG0KKQYcbZ+Zh5ZwCZDvirwB5qOJhuCMREdFgxW0iM378eOTm5uL111/vS1w8Hg/ee+89rFu3TtPYzAYd/vnqS9DUGcC103Jgj5NZQhfLZtIjzWqEQSdDVQUqazvZ+ZaIiOKapomM1+tFVVVV3+cnT57EwYMHkZ6ejjFjxuD+++/H9773PVxyySV9x6/z8/OxfPly7YI+7UsLxqHVGxywRibRmA06pJ9RyPtOVUvfLKKwImDQSSjKtnEWERERxR1NE5m9e/fi85//fN/nvbUtd955J7Zu3Yp//dd/hc/nwz/90z+ho6MDn/nMZ/Dqq6/GZQ+ZRDTQXKR3qlqwcWclvMFIzzFrnYyQouJoQxc27qzEIytmMJkhIqK4IQkhxKffLXF5PB44nU50dnbC4YjtHKNEvSKjkyWkWY2wm/oX8qqqwJ1b9uBogwe5DnO/NSEEGj1BTMmz47f/Zz63mYiIaFgN9v2bXc1GEUmS4EwxwJVmgcNsiDqNdKTeg+pmL9Is0YMfJUlCqsWA6mYvjtQPX28eIiKioYjbYl+KLbNBh0yb6bwdedv8IYQVAaNu4PuYdDI6VYE2f2i4wiQiIhoSJjJJTidLSLcaB3WyKt1ihEEnIaSoMMvRHXyDigqDLCHdYhyOUImIiIaMW0tJqmcryAhXmmXQx8On5TtQlG1Duz+Ms0unhBDo8IdRlG3DtPzY1hoRERFdKCYySaZnMrUBrrQUpFuH1tROliWsW1QEm0mHRk8Q3WEFqirQHVbQ6AnCZtJh3aIiFvoSEVHcYCKTRKwmPQpSU5BlN0F/jjqXT7OwOBOPrJiBKXl2+IMRNHuD8AcjmJJn59FrIiKKO6yRSQJGvYwMqwkpxthMpl5YnInLJ2TgSL2HnX2JiCiuMZFJYL39YBzDMCJBliXMKHTG/HGJiIhiiYlMgnKmGDjYkYiIRj0mMgnGZNAh02aESR+bbSQiIqJExkQmQQznNhIREVGiYiKTAGwmPTJsJui4jURERNQPE5k4ppdlZNr7T6cmIiKif+A7ZJxypBiQzmJeIiKi82IiE2cMOhlZdhPMBhbzEhERfRomMnFCkqTTR6oNkCRehSEiIhoMJjJxgEeqiYiILgwTGQ1JkoR0ixFOC49UExERXQgmMhpJMeqQaTPBcIHDHYmIiIiJzIjTyRLSrUbY2diOiIjoojGRuQCqKnCk3oOPW30wyDKKc6yQB1Ggy8Z2REREscVEZojeqWrB5vJqVDd7EYyo0EmAK8OKNfNdmD0mbcCvYWM7IiKi4cECjSF4p6oFG3dW4miDB1aTHplWI1KMepw45cVju47hQE17v/v3HqkuTEthEkNERDQMmMgMkqoKbC6vhjcYQa7DDLNBB1mWYNLLyLQZ4Q8p2LbHDVUIAD3FvPmpZmTYTOzOS0RENEyYyAzSkXoPqpu9SLMYoxrWSZBgNxvgbvXh5Ck/sh1m5DlT2BeGiIhomDGRGaQ2fwhhRcB4juPSRp0ERQB6vQSbidtIREREI4GJzCClW4ww6CSEFDVqTZYlCKBnm8lqGvngiIiIRikmMoM0Ld+Bomwb2v1hiNN1MJIkwaCToZcldHZHUJRtw7R8h8aREhERjR5MZAZJliWsW1QEm0mHRk8Q3WEFEoCgoqLRE4TNpMO6RUUs7CUiIhpBTGSGYGFxJh5ZMQNT8uzwByNo9gbhD0YwJc+OR1bMwMLiTK1DJCIiGlVYlTpEC4szcfmEDByp96DNH0K6xYhp+Q5eiSEiItIAE5kLIMsSZhQ6tQ6DiIho1OPWEhERESUsJjJERESUsJjIEBERUcJiIkNEREQJi4kMERERJSwmMkRERJSwmMgQERFRwmIiQ0RERAmLiQwRERElLCYyRERElLCYyBAREVHCYiJDRERECSvph0YKIQAAHo9H40iIiIhosHrft3vfx88l6ROZrq4uAIDL5dI4EiIiIhqqrq4uOJ3Oc65L4tNSnQSnqirq6+tht9shSVLMHtfj8cDlcsHtdsPhcMTsceNJsr/GZH99QPK/Rr6+xJfsr5Gv78IJIdDV1YX8/HzI8rkrYZL+iowsyygsLBy2x3c4HEn5j/NMyf4ak/31Acn/Gvn6El+yv0a+vgtzvisxvVjsS0RERAmLiQwRERElLCYyF8hkMuGhhx6CyWTSOpRhk+yvMdlfH5D8r5GvL/El+2vk6xt+SV/sS0RERMmLV2SIiIgoYTGRISIiooTFRIaIiIgSFhMZIiIiSlhMZIZo06ZNKCkpgd1uR3Z2NpYvX46PPvpI67BiZvPmzZg5c2Zfc6MFCxbgT3/6k9ZhDZsf/OAHkCQJ999/v9ahxMx//Md/QJKkfh+TJ0/WOqyYqqurwxe+8AVkZGQgJSUFM2bMwN69e7UOK2bGjRsX9TOUJAnr16/XOrSYUBQFDz74IMaPH4+UlBQUFRXhu9/97qfO1EkkXV1duP/++zF27FikpKRg4cKFqKio0DqsC/bmm2/ipptuQn5+PiRJwgsvvNBvXQiB73znO8jLy0NKSgoWL16M48ePj0hsTGSGqLy8HOvXr8ff//537Nq1C+FwGNdeey18Pp/WocVEYWEhfvCDH2Dfvn3Yu3cvrrrqKixbtgxHjhzROrSYq6iowK9+9SvMnDlT61Bibtq0aWhoaOj7eOutt7QOKWba29txxRVXwGAw4E9/+hM++OAD/PjHP0ZaWprWocVMRUVFv5/frl27AAC33XabxpHFxg9/+ENs3rwZ//3f/42jR4/ihz/8IR599FH87Gc/0zq0mPnyl7+MXbt24Xe/+x0qKytx7bXXYvHixairq9M6tAvi8/kwa9Ys/PznPx9w/dFHH8UTTzyBX/7yl3jvvfdgtVpx3XXXIRAIDH9wgi5Kc3OzACDKy8u1DmXYpKWliV//+tdahxFTXV1d4pJLLhG7du0SixYtEl/72te0DilmHnroITFr1iytwxg23/rWt8RnPvMZrcMYUV/72tdEUVGRUFVV61Bi4oYbbhBr167td9stt9wi7rjjDo0iii2/3y90Op14+eWX+90+Z84c8W//9m8aRRU7AMTOnTv7PldVVeTm5oof/ehHfbd1dHQIk8kknn766WGPh1dkLlJnZycAID09XeNIYk9RFJSVlcHn82HBggVahxNT69evxw033IDFixdrHcqwOH78OPLz8zFhwgTccccdqKmp0TqkmPnDH/6AefPm4bbbbkN2djZmz56N//mf/9E6rGETCoXw//7f/8PatWtjOvhWSwsXLsTrr7+OY8eOAQAOHTqEt956C0uXLtU4stiIRCJQFAVms7nf7SkpKUl1dbTXyZMn0djY2O/3qdPpxGWXXYZ333132J8/6YdGDidVVXH//ffjiiuuwPTp07UOJ2YqKyuxYMECBAIB2Gw27Ny5E1OnTtU6rJgpKyvD/v37E3q/+nwuu+wybN26FZMmTUJDQwMefvhhfPazn8X7778Pu92udXgX7cSJE9i8eTM2bNiAjRs3oqKiAv/8z/8Mo9GIO++8U+vwYu6FF15AR0cH7rrrLq1DiZlvf/vb8Hg8mDx5MnQ6HRRFwfe//33ccccdWocWE3a7HQsWLMB3v/tdTJkyBTk5OXj66afx7rvvori4WOvwYq6xsREAkJOT0+/2nJycvrXhxETmIqxfvx7vv/9+0mXYkyZNwsGDB9HZ2YnnnnsOd955J8rLy5MimXG73fja176GXbt2Rf21lCzO/Kt25syZuOyyyzB27Fg888wzuPvuuzWMLDZUVcW8efPwyCOPAABmz56N999/H7/85S+TMpH53//9XyxduhT5+flahxIzzzzzDH7/+99j27ZtmDZtGg4ePIj7778f+fn5SfMz/N3vfoe1a9eioKAAOp0Oc+bMwe233459+/ZpHVrS4dbSBbr33nvx8ssv469//SsKCwu1DiemjEYjiouLMXfuXGzatAmzZs3C448/rnVYMbFv3z40Nzdjzpw50Ov10Ov1KC8vxxNPPAG9Xg9FUbQOMeZSU1MxceJEVFVVaR1KTOTl5UUl1VOmTEmq7bNen3zyCf7yl7/gy1/+stahxNQ3v/lNfPvb30ZpaSlmzJiBL37xi/j617+OTZs2aR1azBQVFaG8vBxerxdutxt79uxBOBzGhAkTtA4t5nJzcwEATU1N/W5vamrqWxtOTGSGSAiBe++9Fzt37sQbb7yB8ePHax3SsFNVFcFgUOswYuLqq69GZWUlDh482Pcxb9483HHHHTh48CB0Op3WIcac1+tFdXU18vLytA4lJq644oqolgfHjh3D2LFjNYpo+GzZsgXZ2dm44YYbtA4lpvx+P2S5/9uPTqeDqqoaRTR8rFYr8vLy0N7ejtdeew3Lli3TOqSYGz9+PHJzc/H666/33ebxePDee++NSH0lt5aGaP369di2bRtefPFF2O32vv0/p9OJlJQUjaO7eA888ACWLl2KMWPGoKurC9u2bcPu3bvx2muvaR1aTNjt9qh6JqvVioyMjKSpc/rGN76Bm266CWPHjkV9fT0eeugh6HQ63H777VqHFhNf//rXsXDhQjzyyCNYtWoV9uzZgyeffBJPPvmk1qHFlKqq2LJlC+68807o9cn1q/qmm27C97//fYwZMwbTpk3DgQMH8Nhjj2Ht2rVahxYzr732GoQQmDRpEqqqqvDNb34TkydPxv/5P/9H69AuiNfr7XdV9+TJkzh48CDS09MxZswY3H///fje976HSy65BOPHj8eDDz6I/Px8LF++fPiDG/ZzUUkGwIAfW7Zs0Tq0mFi7dq0YO3asMBqNIisrS1x99dXiz3/+s9ZhDatkO369evVqkZeXJ4xGoygoKBCrV68WVVVVWocVUy+99JKYPn26MJlMYvLkyeLJJ5/UOqSYe+211wQA8dFHH2kdSsx5PB7xta99TYwZM0aYzWYxYcIE8W//9m8iGAxqHVrMbN++XUyYMEEYjUaRm5sr1q9fLzo6OrQO64L99a9/HfC978477xRC9BzBfvDBB0VOTo4wmUzi6quvHrF/u5IQSdRKkYiIiEYV1sgQERFRwmIiQ0RERAmLiQwRERElLCYyRERElLCYyBAREVHCYiJDRERECYuJDBERESUsJjJEpLndu3dDkiR0dHQwDiIaEiYyRBTl1KlTWLduHcaMGQOTyYTc3Fxcd911ePvtty/6sa+88krcf//9/W5buHAhGhoa4HQ6L/rxz+Wuu+6CJEnn/Bg3btyIxEFEsZVcAzyIKCZWrlyJUCiE3/72t5gwYQKamprw+uuvo7W19YIfMxQKwWg0DrhmNBqHfUru448/jh/84Ad9n+fl5WHLli1YsmQJgJ6hhSMRBxHF2IgMQiCihNHe3i4AiN27d5/3fp988om4+eabhdVqFXa7Xdx2222isbGxb/2hhx4Ss2bNEv/zP/8jxo0bJyRJEnfeeWfUrJaTJ0/2zXFpb28XQgixZcsW4XQ6xauvviomT54srFaruO6660R9fX3f44fDYXHfffcJp9Mp0tPTxb/+67+KL33pS2LZsmWDep0AxM6dO/vddq44XnrpJTFx4kSRkpIiVq5cKXw+n9i6dasYO3asSE1NFffdd5+IRCJ9jxMIBMS//Mu/iPz8fGGxWMT8+fPFX//610HFRURDw60lIurHZrPBZrPhhRdeQDAYHPA+qqpi2bJlaGtrQ3l5OXbt2oUTJ05g9erV/e5XVVWF559/Hjt27MDBgwfx+OOPY8GCBbjnnnvQ0NCAhoYGuFyuAZ/D7/fjv/7rv/C73/0Ob775JmpqavCNb3yjb/2HP/whfv/732PLli14++234fF48MILL8Ts+3BmHE888QTKysrw6quvYvfu3VixYgVeeeUVvPLKK/jd736HX/3qV3juuef6vubee+/Fu+++i7KyMhw+fBi33XYblixZguPHj8c8PqJRT+tMiojiz3PPPSfS0tKE2WwWCxcuFA888IA4dOhQ3/qf//xnodPpRE1NTd9tR44cEQDEnj17hBA9V2QMBoNobm7u99gDTRsf6EoIgH5Tu3/+85+LnJycvs9zcnLEj370o77PI5GIGDNmTMyvyJwdx1e+8hVhsVhEV1dX323XXXed+MpXviKE6LlSpdPpRF1dXb/Hvvrqq8UDDzwwqNiIaPB4RYaIoqxcuRL19fX4wx/+gCVLlmD37t2YM2cOtm7dCgA4evQoXC5Xv6spU6dORWpqKo4ePdp329ixY5GVlXVBMVgsFhQVFfV9npeXh+bmZgBAZ2cnmpqaMH/+/L51nU6HuXPnXtBzDSWOnJwcjBs3Djabrd9tvbFVVlZCURRMnDix7+qWzWZDeXk5qqurYx4f0WjHYl8iGpDZbMY111yDa665Bg8++CC+/OUv46GHHsJdd9016MewWq0X/PwGg6Hf55IkQQhxwY8XyzgGuk1VVQCA1+uFTqfDvn37oNPp+t3vzOSHiGKDV2SIaFCmTp0Kn88HAJgyZQrcbjfcbnff+gcffICOjg5MnTr1vI9jNBqhKMpFxeJ0OpGTk4OKioq+2xRFwf79+y/qcWNh9uzZUBQFzc3NKC4u7vfBE1FEsccrMkTUT2trK2677TasXbsWM2fOhN1ux969e/Hoo49i2bJlAIDFixdjxowZuOOOO/DTn/4UkUgE//f//l8sWrQI8+bNO+/jjxs3Du+99x4+/vhj2Gw2pKenX1Cc9913HzZt2oTi4mJMnjwZP/vZz9De3g5Jki7o8WJl4sSJuOOOO/ClL30JP/7xjzF79mycOnUKr7/+OmbOnIkbbrhB0/iIkg0TGSLqx2az4bLLLsNPfvITVFdXIxwOw+Vy4Z577sHGjRsB9GylvPjii7jvvvvwuc99DrIsY8mSJfjZz372qY//jW98A3feeSemTp2K7u5unDx58oLi/Na3voXGxkZ86Utfgk6nwz/90z/huuuui9rO0cKWLVvwve99D//yL/+Curo6ZGZm4vLLL8eNN96odWhESUcSWmw6ExHFmKqqmDJlClatWoXvfve7WodDRCOEV2SIKCF98skn+POf/4xFixYhGAziv//7v3Hy5EmsWbNG69CIaASx2JeIEpIsy9i6dStKSkpwxRVXoLKyEn/5y18wZcoUrUMjohHErSUiIiJKWLwiQ0RERAmLiQwRERElLCYyRERElLCYyBAREVHCYiJDRERECYuJDBERESUsJjJERESUsJjIEBERUcJiIkNEREQJ6/8Dn2JWNx7KLwcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.regplot(x=\"Sorting Time\", y=\"Delivery Time\", data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "05ef6e1d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05ef6e1d",
        "outputId": "8f115c96-ad62-4bd4-8100-6a6ff7ebacf9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    6.582734\n",
              "Sorting      1.649020\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "model.params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "0556358f",
      "metadata": {
        "id": "0556358f"
      },
      "outputs": [],
      "source": [
        "#prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "3729f189",
      "metadata": {
        "id": "3729f189"
      },
      "outputs": [],
      "source": [
        "newdata = pd.Series([15,20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "055b3136",
      "metadata": {
        "id": "055b3136"
      },
      "outputs": [],
      "source": [
        "predData=pd.DataFrame(newdata,columns=['Sorting'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "9c0f5652",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9c0f5652",
        "outputId": "9da6f135-0c07-4c70-e498-f29ea89af77a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    31.318032\n",
              "1    39.563132\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "model.predict(predData)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "0f88f558",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0f88f558",
        "outputId": "76741bca-24a6-4592-fd1a-c14c5ce1af1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6822714748417231"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "model.rsquared"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "48804097",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48804097",
        "outputId": "cb3be551-4ed8-43d5-b71e-ad44fcdf0375"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    3.823349\n",
              "Sorting      6.387447\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "model.tvalues"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "c81f37af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c81f37af",
        "outputId": "90bc8a10-2f9c-4444-e80d-7bfdb00674bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    0.001147\n",
              "Sorting      0.000004\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "model.pvalues"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "jtO2ipttpL6R",
        "outputId": "0c67882c-c94e-4a5d-d8e1-df941edc13fa"
      },
      "id": "jtO2ipttpL6R",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Delivery Time  Sorting Time\n",
              "0          21.00            10\n",
              "1          13.50             4\n",
              "2          19.75             6\n",
              "3          24.00             9\n",
              "4          29.00            10"
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "161e171e",
      "metadata": {
        "id": "161e171e"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "X = data['Sorting Time']\n",
        "y = data['Delivery Time']\n",
        "\n",
        "results = pd.DataFrame(columns=['Transformation', 'R-squared', 't-value', 'p-value'])\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the models with different transformations\n",
        "for transformation in ['Original', 'Log', 'Sqrt', 'Square']:\n",
        "    if transformation == 'Original':\n",
        "        X_transformed = X\n",
        "    elif transformation == 'Log':\n",
        "        X_transformed = np.log(X)\n",
        "    elif transformation == 'Sqrt':\n",
        "        X_transformed = np.sqrt(X)\n",
        "    elif transformation == 'Square':\n",
        "        X_transformed = X ** 2\n",
        "    # Fit the model\n",
        "    model = smf.ols(f\"y ~ X_transformed\", data=data).fit()\n",
        "\n",
        "    # Store the results\n",
        "    results = results.append({\n",
        "        'Transformation': transformation,\n",
        "        'R-squared': model.rsquared,\n",
        "        't-value': model.tvalues[1],\n",
        "        'p-value': model.pvalues[1]\n",
        "    }, ignore_index=True)\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Ncb1u6Tpvhf",
        "outputId": "39ee996f-1237-4bce-ef9b-c0a1befed4d6"
      },
      "id": "9Ncb1u6Tpvhf",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-df789d9b0b61>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-38-df789d9b0b61>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-38-df789d9b0b61>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-38-df789d9b0b61>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UcWE4IY9p3Cl",
        "outputId": "95629eb2-7343-43fb-983c-68b7d2072250"
      },
      "id": "UcWE4IY9p3Cl",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Transformation  R-squared   t-value   p-value\n",
            "0       Original   0.682271  6.387447  0.000004\n",
            "1            Log   0.695443  6.586789  0.000003\n",
            "2           Sqrt   0.695806  6.592434  0.000003\n",
            "3         Square   0.630287  5.691330  0.000017\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UkKnzbSXqTCJ"
      },
      "id": "UkKnzbSXqTCJ",
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame(columns=['Transformation', 'R-squared', 't-value', 'p-value'])\n",
        "\n"
      ],
      "metadata": {
        "id": "65JOLWEOqjhf"
      },
      "id": "65JOLWEOqjhf",
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transformations = {\n",
        "    'Original': lambda x: x,\n",
        "    'Log': np.log,\n",
        "    'Sqrt': np.sqrt,\n",
        "    'Square': lambda x: x**2\n",
        "}"
      ],
      "metadata": {
        "id": "qtZvAbt3q7sP"
      },
      "id": "qtZvAbt3q7sP",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
        "for (transformation, func), ax in zip(transformations.items(), axes.flatten()):\n",
        "    X_transformed = func(X)\n",
        "    model = smf.ols(f\"y ~ X_transformed\", data=data).fit()\n",
        "\n",
        "    # Store the results\n",
        "    results = results.append({\n",
        "        'Transformation': transformation,\n",
        "        'R-squared': model.rsquared,\n",
        "        't-value': model.tvalues[1],\n",
        "        'p-value': model.pvalues[1]\n",
        "    }, ignore_index=True)\n",
        "\n",
        "    # Scatter plot with regression line\n",
        "    ax.scatter(X_transformed, y, color='blue')\n",
        "    ax.plot(X_transformed, model.predict(), color='red')\n",
        "    ax.set_title(f'Scatter Plot ({transformation})')\n",
        "    ax.set_xlabel('Transformed Sorting Time')\n",
        "    ax.set_ylabel('Delivery Time')\n",
        "    ax.text(0.1, 0.9, f\"R-squared: {model.rsquared:.2f}\", transform=ax.transAxes)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q5lozB4gr5_7",
        "outputId": "c30f36ed-3d38-4351-b76b-9e2f79f4245f"
      },
      "id": "Q5lozB4gr5_7",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-14b01bf0b188>:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-47-14b01bf0b188>:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-47-14b01bf0b188>:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n",
            "<ipython-input-47-14b01bf0b188>:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  results = results.append({\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rphlamCasHmx"
      },
      "id": "rphlamCasHmx",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}