{
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"
    }
   },
   "cell_type": "markdown",
   "id": "1c7524e2-ddd2-4c44-aacb-c2a49ea0a385",
   "metadata": {},
   "source": [
    "# Handling missing data (Python version)\n",
    "![Screenshot 2024-10-30 at 1.39.37 AM.png](attachment:7f720156-61c1-4c1d-a29b-20d7b0b12c0d.png)\n",
    "### Is it better to replace missing values with averages or with regression values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d2a03c2-9053-4425-b367-5bc5f594c8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5761ce74-2e15-4f9b-8759-8bc6ab7fd066",
   "metadata": {},
   "source": [
    "## Original student and book weights (full dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "602b796a-5d5b-447d-b270-80d5728f7fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "student_weights = [48.50, 54.50, 61.25, 65.00, 69.00, 74.50, 85.00, 91.23, 89.00, 99.00, 105.00, 112.00, 123.00, 134.00, 142.00]\n",
    "book_weights    = [ 8.00,  9.44, 10.08, 11.07, 11.81, 12.28, 13.61, 14.00, 15.13, 15.47,  15.53, 17.36,  18.07,  20.29,  16.06 ]"
   ]
  },
  {
   "attachments": {
    "c3e69f96-bfc9-4bd7-9417-5a230e303d98.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "3b429830-8655-490f-af16-1ddf0d3f39a9",
   "metadata": {},
   "source": [
    "![Screenshot 2024-10-30 at 1.40.28 AM.png](attachment:c3e69f96-bfc9-4bd7-9417-5a230e303d98.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36770d84-da9f-4f39-b7c1-b82a5f244043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 0.11139024135906639\n",
      "b = 3.8327487497340242\n"
     ]
    }
   ],
   "source": [
    "m, b = np.polyfit(student_weights, book_weights, 1)\n",
    "\n",
    "print(f'{m = }')\n",
    "print(f'{b = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0655342e-2bc1-4bd9-9fe1-6ae28f7d69fd",
   "metadata": {},
   "source": [
    "## Calculations from the original full dataset\n",
    "#### New student weights 72, 108, and 150 which were not in the original full dataset. Use the regression equation of the full dataset to create the baseline estimated book weights for the new student weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9570095-a582-44fd-b630-e99a36d25e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_student_a =  72, baseline_est_a = 11.8528\n",
      "new_student_b = 108, baseline_est_b = 15.8629\n",
      "new_student_c = 150, baseline_est_c = 20.5413\n"
     ]
    }
   ],
   "source": [
    "new_student_a, new_student_b, new_student_c = 72, 108, 150\n",
    "\n",
    "baseline_est_a, baseline_est_b, baseline_est_c = \\\n",
    "    [m*x + b for x in (new_student_a, new_student_b, new_student_c)]\n",
    "\n",
    "print(f'{new_student_a = :3d}, {baseline_est_a = :7.4f}')\n",
    "print(f'{new_student_b = :3d}, {baseline_est_b = :7.4f}')\n",
    "print(f'{new_student_c = :3d}, {baseline_est_c = :7.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35ef75e-a0b6-442c-9c9a-637253bdabe5",
   "metadata": {},
   "source": [
    "## Student and book weights with missing values\n",
    "#### -1 is the placeholder for missing weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e85cba3b-a438-404c-ba33-f16e6e417840",
   "metadata": {},
   "outputs": [],
   "source": [
    "dirty_student = [48.50, 54.50, 61.25, 65.00, 69.00, 74.50, 85.00,    -1, 89.00, 99.00, 105.00, 112.00, 123.00, 134.00, 142.00]\n",
    "dirty_books   = [ 8.00,  9.44, 10.08,    -1, 11.81, 12.28, 13.61, 14.00, 15.13, 15.47,     -1, 17.36,  18.07,  20.29,  16.06 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf4b4b7-31a7-46c1-8ea5-8ac7f4d78f0e",
   "metadata": {},
   "source": [
    "## Calculations with only the good data\n",
    "#### Extract the good X and Y pairs from the dirty student and book weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59693749-d8d6-4c00-a1f7-7a49ad826be5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good_X = [48.5, 54.5, 61.25, 69.0, 74.5, 85.0, 89.0, 99.0, 112.0, 123.0, 134.0, 142.0]\n",
      "good_Y = [8.0, 9.44, 10.08, 11.81, 12.28, 13.61, 15.13, 15.47, 17.36, 18.07, 20.29, 16.06]\n"
     ]
    }
   ],
   "source": [
    "good_X = []\n",
    "good_Y = []\n",
    "i = 0\n",
    "\n",
    "while i < len(dirty_student):\n",
    "    if (dirty_student[i] > 0) and (dirty_books[i] > 0):\n",
    "        good_X.append(dirty_student[i])  # only good X values\n",
    "        good_Y.append(dirty_books[i])    # only good Y values\n",
    "    i += 1\n",
    "\n",
    "print(f'{good_X = }')\n",
    "print(f'{good_Y = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6e5a16-f867-40b0-b0bb-f9731a40bc9a",
   "metadata": {},
   "source": [
    "## Cleanup 1: Replace missing values with averages\n",
    "#### Compute the averages of the good X and good Y data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af44f3bd-cb3e-4cd4-8d92-129cafc7f70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_good_X = 90.97916666666667\n",
      "avg_good_Y = 13.966666666666667\n"
     ]
    }
   ],
   "source": [
    "avg_good_X = sum(good_X)/len(good_X)\n",
    "avg_good_Y = sum(good_Y)/len(good_Y)\n",
    "\n",
    "print(f'{avg_good_X = }')\n",
    "print(f'{avg_good_Y = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b837dd07-0134-4f0a-a098-c08c2ce2ed94",
   "metadata": {},
   "source": [
    "#### Replace each missing student weight with the average of the good X data to produce cleaned `X_1`. Replace each missing book weight with the average of the good Y data to produce cleaned `Y_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62a791d3-a6f9-40fb-abe0-20d1ce0716d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65.0, -1) => (65.0, 13.966666666666667)\n",
      "(-1, 14.0) => (90.97916666666667, 14.0)\n",
      "(105.0, -1) => (105.0, 13.966666666666667)\n"
     ]
    }
   ],
   "source": [
    "X_cleaned_avg = dirty_student.copy()\n",
    "Y_cleaned_avg = dirty_books.copy()\n",
    "i = 0\n",
    "\n",
    "while i < len(dirty_student):\n",
    "    if X_cleaned_avg[i] < 0: \n",
    "        X_cleaned_avg[i] = avg_good_X  # replace a missing X value\n",
    "        print(f'({dirty_student[i]}, {dirty_books[i]}) => ({X_cleaned_avg[i]}, {Y_cleaned_avg[i]})')\n",
    "    if Y_cleaned_avg[i] < 0:\n",
    "        Y_cleaned_avg[i] = avg_good_Y  # replace a missing Y value\n",
    "        print(f'({dirty_student[i]}, {dirty_books[i]}) => ({X_cleaned_avg[i]}, {Y_cleaned_avg[i]})')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e91fad2-0272-4fb6-957e-ba5feef3b62c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>X cleaned with averages</th>\n",
       "      <th>Y cleaned with averages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.500000</td>\n",
       "      <td>9.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.250000</td>\n",
       "      <td>10.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>13.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>11.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.500000</td>\n",
       "      <td>12.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>13.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90.979167</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>15.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>15.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>13.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>17.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>18.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>134.000000</td>\n",
       "      <td>20.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>16.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X cleaned with averages  Y cleaned with averages\n",
       "0                 48.500000                 8.000000\n",
       "1                 54.500000                 9.440000\n",
       "2                 61.250000                10.080000\n",
       "3                 65.000000                13.966667\n",
       "4                 69.000000                11.810000\n",
       "5                 74.500000                12.280000\n",
       "6                 85.000000                13.610000\n",
       "7                 90.979167                14.000000\n",
       "8                 89.000000                15.130000\n",
       "9                 99.000000                15.470000\n",
       "10               105.000000                13.966667\n",
       "11               112.000000                17.360000\n",
       "12               123.000000                18.070000\n",
       "13               134.000000                20.290000\n",
       "14               142.000000                16.060000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(zip(X_cleaned_avg, Y_cleaned_avg),\n",
    "          columns=['X cleaned with averages',\n",
    "                   'Y cleaned with averages'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a4a4df-dbfa-42bc-b68d-b743860aea31",
   "metadata": {},
   "source": [
    "#### Compute regression coefficients `m_cleaned_avg` and `b_cleaned_avg` from the data cleaned with averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e7fd700-1a93-46b1-a64f-bb739bac89f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m_cleaned_avg = 0.10314994248558672\n",
      "b_cleaned_avg = 4.666626506206067\n"
     ]
    }
   ],
   "source": [
    "m_cleaned_avg, b_cleaned_avg = np.polyfit(X_cleaned_avg, Y_cleaned_avg, 1)\n",
    "\n",
    "print(f'{m_cleaned_avg = }')\n",
    "print(f'{b_cleaned_avg = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c7490c-a82e-4476-9abc-52d41ceef836",
   "metadata": {},
   "source": [
    "#### Make estimated book weights from the regression equation based on `m_cleaned_avg` and `b_cleaned_avg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5637ac3d-e74e-4d74-8795-9d8588668743",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_avg_est_a, cleaned_avg_est_b, cleaned_avg_est_c = \\\n",
    "    [m_cleaned_avg*student + b_cleaned_avg for student in (new_student_a, new_student_b, new_student_c)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c02eed1-295e-4e1b-88b5-6cf91353f3cd",
   "metadata": {},
   "source": [
    "#### How do these estimates compare to the baseline estimates? Compute relative differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f6aee40-9031-4a0e-b07b-dfcd18a62d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>student</th>\n",
       "      <th>est. from averages</th>\n",
       "      <th>baseline est.</th>\n",
       "      <th>rel. diff.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>12.093422</td>\n",
       "      <td>11.852846</td>\n",
       "      <td>2.030%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>15.806820</td>\n",
       "      <td>15.862895</td>\n",
       "      <td>0.353%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>20.139118</td>\n",
       "      <td>20.541285</td>\n",
       "      <td>1.958%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  est. from averages  baseline est. rel. diff.\n",
       "0       72           12.093422      11.852846     2.030%\n",
       "1      108           15.806820      15.862895     0.353%\n",
       "2      150           20.139118      20.541285     1.958%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average relative difference: 1.447%\n"
     ]
    }
   ],
   "source": [
    "rel_diff_a = abs(cleaned_avg_est_a - baseline_est_a)/baseline_est_a\n",
    "rel_diff_b = abs(cleaned_avg_est_b - baseline_est_b)/baseline_est_b\n",
    "rel_diff_c = abs(cleaned_avg_est_c - baseline_est_c)/baseline_est_c\n",
    "\n",
    "avg_diff_cleaned = (rel_diff_a + rel_diff_b + rel_diff_c)/3\n",
    "\n",
    "df = DataFrame([[new_student_a, cleaned_avg_est_a, baseline_est_a, f'{rel_diff_a:6.3%}'],\n",
    "               [new_student_b, cleaned_avg_est_b, baseline_est_b, f'{rel_diff_b:6.3%}'],\n",
    "               [new_student_c, cleaned_avg_est_c, baseline_est_c, f'{rel_diff_c:6.3%}']], \n",
    "               columns=['student', 'est. from averages', \n",
    "                        'baseline est.', 'rel. diff.'])\n",
    "\n",
    "display(df)\n",
    "print()\n",
    "print(f'Average relative difference: {avg_diff_cleaned:6.3%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb2bfae-7393-4b44-82c6-3ac34d7a264c",
   "metadata": {},
   "source": [
    "## Cleanup 2: Replace missing values with regression estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7dc9af2-54db-4f39-91e3-5672a394f74a",
   "metadata": {},
   "source": [
    "#### Compute regression coefficients `good_m` and `good_b` from only the good data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ed12238-da5d-433e-adc0-39dce21c3db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good_m = 0.11138097330386121\n",
      "good_b = 3.8333185329591326\n"
     ]
    }
   ],
   "source": [
    "good_m, good_b = np.polyfit(good_X, good_Y, 1)\n",
    "\n",
    "print(f'{good_m = }')\n",
    "print(f'{good_b = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ad428e-5e17-45e9-bef0-7854efe40f1d",
   "metadata": {},
   "source": [
    "#### Use the regression equation based on `good_m` and `good_b` to compute a replacement value for each missing student and book weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d37bdab-10b0-4356-871a-bee9b8e9917f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65.0, -1) => (65.0, 11.073081797710111)\n",
      "(-1, 14.0) => (91.27843980412067, 14.0)\n",
      "(105.0, -1) => (105.0, 15.52832072986456)\n"
     ]
    }
   ],
   "source": [
    "X_cleaned_reg = dirty_student.copy()\n",
    "Y_cleaned_reg = dirty_books.copy()\n",
    "i = 0\n",
    "\n",
    "while i < len(dirty_student):\n",
    "    if X_cleaned_reg[i] < 0: \n",
    "        X_cleaned_reg[i] = (Y_cleaned_reg[i] - good_b)/good_m  # replace a missing X value\n",
    "        print(f'({dirty_student[i]}, {dirty_books[i]}) '\n",
    "              f'=> ({X_cleaned_reg[i]}, {Y_cleaned_reg[i]})')\n",
    "    if Y_cleaned_reg[i] < 0:\n",
    "        Y_cleaned_reg[i] = good_m*X_cleaned_reg[i] + good_b    # replace a missing Y value\n",
    "        print(f'({dirty_student[i]}, {dirty_books[i]}) '\n",
    "              f'=> ({X_cleaned_reg[i]}, {Y_cleaned_reg[i]})')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca80cdb3-c6ef-4d8a-a285-e6a68cdc349c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>X cleaned with regression</th>\n",
       "      <th>Y cleaned with regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.50000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.50000</td>\n",
       "      <td>9.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.25000</td>\n",
       "      <td>10.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.00000</td>\n",
       "      <td>11.073082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.00000</td>\n",
       "      <td>11.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.50000</td>\n",
       "      <td>12.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85.00000</td>\n",
       "      <td>13.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>91.27844</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>89.00000</td>\n",
       "      <td>15.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.00000</td>\n",
       "      <td>15.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>105.00000</td>\n",
       "      <td>15.528321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.00000</td>\n",
       "      <td>17.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>123.00000</td>\n",
       "      <td>18.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>134.00000</td>\n",
       "      <td>20.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>142.00000</td>\n",
       "      <td>16.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X cleaned with regression  Y cleaned with regression\n",
       "0                    48.50000                   8.000000\n",
       "1                    54.50000                   9.440000\n",
       "2                    61.25000                  10.080000\n",
       "3                    65.00000                  11.073082\n",
       "4                    69.00000                  11.810000\n",
       "5                    74.50000                  12.280000\n",
       "6                    85.00000                  13.610000\n",
       "7                    91.27844                  14.000000\n",
       "8                    89.00000                  15.130000\n",
       "9                    99.00000                  15.470000\n",
       "10                  105.00000                  15.528321\n",
       "11                  112.00000                  17.360000\n",
       "12                  123.00000                  18.070000\n",
       "13                  134.00000                  20.290000\n",
       "14                  142.00000                  16.060000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(zip(X_cleaned_reg, Y_cleaned_reg),\n",
    "          columns=['X cleaned with regression',\n",
    "                   'Y cleaned with regression'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28df4ba6-5d52-4a6e-a8d9-93494cb9a235",
   "metadata": {},
   "source": [
    "#### Compute regression coefficients `m_cleaned_reg` and `b_cleaned_reg` from the data cleaned with regression estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "21e81ef1-0c1f-4c5a-99ea-a2373e912378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m_cleaned_reg = 0.11138097330386126\n",
      "b_cleaned_reg = 3.833318532959122\n"
     ]
    }
   ],
   "source": [
    "m_cleaned_reg, b_cleaned_reg = np.polyfit(X_cleaned_reg, Y_cleaned_reg, 1)\n",
    "\n",
    "print(f'{m_cleaned_reg = }')\n",
    "print(f'{b_cleaned_reg = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e775986-ba3b-4173-be5b-e697428bcc81",
   "metadata": {},
   "source": [
    "#### Make estimated book weights from the regression equation based on `m_cleaned_reg` and `b_cleaned_reg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "202eee7a-e2ac-40c3-b590-7aa405990b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_reg_est_a, cleaned_reg_est_b, cleaned_reg_est_c = \\\n",
    "    [m_cleaned_reg*student + b_cleaned_reg for student in (new_student_a, new_student_b, new_student_c)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ff61f7-82c0-428c-9a77-479cfae3189c",
   "metadata": {},
   "source": [
    "#### How do these estimates compare to the baseline estimates? Compute relative differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b973fb6-bdeb-4a4a-9082-d3b2a8613d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>student</th>\n",
       "      <th>est. from regression</th>\n",
       "      <th>baseline est.</th>\n",
       "      <th>rel. diff.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>11.852749</td>\n",
       "      <td>11.852846</td>\n",
       "      <td>0.001%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>15.862464</td>\n",
       "      <td>15.862895</td>\n",
       "      <td>0.003%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>20.540465</td>\n",
       "      <td>20.541285</td>\n",
       "      <td>0.004%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  est. from regression  baseline est. rel. diff.\n",
       "0       72             11.852749      11.852846     0.001%\n",
       "1      108             15.862464      15.862895     0.003%\n",
       "2      150             20.540465      20.541285     0.004%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average relative difference: 0.003%\n"
     ]
    }
   ],
   "source": [
    "rel_diff_a = abs(cleaned_reg_est_a - baseline_est_a)/baseline_est_a\n",
    "rel_diff_b = abs(cleaned_reg_est_b - baseline_est_b)/baseline_est_b\n",
    "rel_diff_c = abs(cleaned_reg_est_c - baseline_est_c)/baseline_est_c\n",
    "\n",
    "avg_diff_cleaned = (rel_diff_a + rel_diff_b + rel_diff_c)/3\n",
    "\n",
    "df = DataFrame([[new_student_a, cleaned_reg_est_a, baseline_est_a, f'{rel_diff_a:6.3%}'],\n",
    "               [new_student_b, cleaned_reg_est_b, baseline_est_b, f'{rel_diff_b:6.3%}'],\n",
    "               [new_student_c, cleaned_reg_est_c, baseline_est_c, f'{rel_diff_c:6.3%}']], \n",
    "               columns=['student', 'est. from regression', \n",
    "                        'baseline est.', 'rel. diff.'])\n",
    "\n",
    "display(df)\n",
    "print()\n",
    "print(f'Average relative difference: {avg_diff_cleaned:6.3%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd0ac30-ad78-49c2-8263-0df880a9747a",
   "metadata": {},
   "source": [
    "## Replacing missing values with regression estimates is much better!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf856a8d-3243-4e14-a0fc-56e18961aa8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x_offset = 10\n",
    "y_offset = 2\n",
    "\n",
    "# For a regression line, we only need \n",
    "# the end points (x1,y1) and (x2,y2)\n",
    "# End point 1: (min(X), m*min(X) + b)\n",
    "# End point 2: (max(X), m*max(X) + b)\n",
    "\n",
    "# Adjust the left end of the regression line\n",
    "# to make it cross the Y axis.\n",
    "max_x = max(student_weights)\n",
    "max_y = max(book_weights)\n",
    "x1 = -x_offset\n",
    "x2 = max_x + x_offset\n",
    "\n",
    "# Full dataset regression line using m and b: red.\n",
    "y1 = m*x1 + b\n",
    "y2 = m*x2 + b\n",
    "plt.plot([x1, x2], [y1, y2], color='red',\n",
    "         label='original full dataset')\n",
    "\n",
    "# Regression line using m_cleaned_avg and b_cleaned_avg: gray.\n",
    "y1 = m_cleaned_avg*x1 + b_cleaned_avg\n",
    "y2 = m_cleaned_avg*x2 + b_cleaned_avg\n",
    "plt.plot([x1, x2], [y1, y2], color='gray', linestyle='--', \n",
    "         label='average value replacement')\n",
    "\n",
    "# Regression line using m_cleaned_reg and b_cleaned_reg: blue.\n",
    "y1 = m_cleaned_reg*x1 + b\n",
    "y2 = m_cleaned_reg*x2 + b\n",
    "plt.plot([x1, x2], [y1, y2], color='black', linestyle=':',\n",
    "         label='regression value replacement')\n",
    "\n",
    "# Set the limits of the x-axis and the y-axis.\n",
    "ax.set_xlim([x1, x2])\n",
    "ax.set_ylim([-y_offset, max_y + y_offset])\n",
    "    \n",
    "# Set the ticks of the x-axis and the y-axis.\n",
    "plt.xticks(range(0, int(max_x + x_offset), x_offset))\n",
    "plt.yticks(range(0, int(max_y + y_offset), y_offset))\n",
    "\n",
    "# Position the x-axis and the y-axis to the origin.\n",
    "ax.spines.left.set_position('zero')\n",
    "ax.spines.bottom.set_position('zero')\n",
    "\n",
    "# Remove the top and right spines.\n",
    "ax.spines.top.set_color('none')\n",
    "ax.spines.right.set_color('none')\n",
    "\n",
    "# Title and axis labels.\n",
    "ax.set_title('Student weights vs. book weights')\n",
    "ax.set_xlabel('Student weights')\n",
    "ax.set_ylabel('Book weights')\n",
    "\n",
    "# Display the graph.\n",
    "print()\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.0, 0.4))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562924ff-4b4a-46a8-b7a2-324a3d365c6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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