diff --git a/correlation.ipynb b/correlation.ipynb
index 51c1fea..3bbd5df 100644
--- a/correlation.ipynb
+++ b/correlation.ipynb
@@ -79,12 +79,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "brainFrame = pd.read_csv(?, delimiter='\\t')"
+    "brainFrame = pd.read_csv('brainsize.txt',delimiter='\\t')"
    ]
   },
   {
@@ -98,11 +98,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "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>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "0  Female   133  132  124   118.0    64.5     816932\n",
+       "1    Male   140  150  124     NaN    72.5    1001121\n",
+       "2    Male   139  123  150   143.0    73.3    1038437\n",
+       "3    Male   133  129  128   172.0    68.8     965353\n",
+       "4  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "brainFrame.?()"
+    "brainFrame.head()"
    ]
   },
   {
@@ -114,11 +213,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "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>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>99</td>\n",
+       "      <td>90</td>\n",
+       "      <td>110</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>928799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>138</td>\n",
+       "      <td>136</td>\n",
+       "      <td>131</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>991305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>92</td>\n",
+       "      <td>90</td>\n",
+       "      <td>98</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>854258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>89</td>\n",
+       "      <td>93</td>\n",
+       "      <td>84</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>66.3</td>\n",
+       "      <td>904858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>114</td>\n",
+       "      <td>147</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>955466</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "0  Female   133  132  124   118.0    64.5     816932\n",
+       "1    Male   140  150  124     NaN    72.5    1001121\n",
+       "2    Male   139  123  150   143.0    73.3    1038437\n",
+       "3    Male   133  129  128   172.0    68.8     965353\n",
+       "4  Female   137  132  134   147.0    65.0     951545\n",
+       "5  Female    99   90  110   146.0    69.0     928799\n",
+       "6  Female   138  136  131   138.0    64.5     991305\n",
+       "7  Female    92   90   98   175.0    66.0     854258\n",
+       "8    Male    89   93   84   134.0    66.3     904858\n",
+       "9    Male   133  114  147   172.0    68.8     955466"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "brainFrame.head(?)"
+    "brainFrame.head(10)"
    ]
   },
   {
@@ -130,11 +383,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "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>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>103</td>\n",
+       "      <td>96</td>\n",
+       "      <td>110</td>\n",
+       "      <td>192.0</td>\n",
+       "      <td>75.5</td>\n",
+       "      <td>997925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>90</td>\n",
+       "      <td>96</td>\n",
+       "      <td>86</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>879987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>83</td>\n",
+       "      <td>90</td>\n",
+       "      <td>81</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>834344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>948066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>70.5</td>\n",
+       "      <td>949395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>88</td>\n",
+       "      <td>86</td>\n",
+       "      <td>94</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>893983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>81</td>\n",
+       "      <td>90</td>\n",
+       "      <td>74</td>\n",
+       "      <td>148.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>930016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>89</td>\n",
+       "      <td>91</td>\n",
+       "      <td>89</td>\n",
+       "      <td>179.0</td>\n",
+       "      <td>75.5</td>\n",
+       "      <td>935863</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "32    Male   103   96  110   192.0    75.5     997925\n",
+       "33    Male    90   96   86   181.0    69.0     879987\n",
+       "34  Female    83   90   81   143.0    66.5     834344\n",
+       "35  Female   133  129  128   153.0    66.5     948066\n",
+       "36    Male   140  150  124   144.0    70.5     949395\n",
+       "37  Female    88   86   94   139.0    64.5     893983\n",
+       "38    Male    81   90   74   148.0    74.0     930016\n",
+       "39    Male    89   91   89   179.0    75.5     935863"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "?"
+    "brainFrame.tail(8)"
    ]
   },
   {
@@ -155,11 +540,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "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>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>4.000000e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>113.450000</td>\n",
+       "      <td>112.350000</td>\n",
+       "      <td>111.02500</td>\n",
+       "      <td>151.052632</td>\n",
+       "      <td>68.525641</td>\n",
+       "      <td>9.087550e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>24.082071</td>\n",
+       "      <td>23.616107</td>\n",
+       "      <td>22.47105</td>\n",
+       "      <td>23.478509</td>\n",
+       "      <td>3.994649</td>\n",
+       "      <td>7.228205e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>72.00000</td>\n",
+       "      <td>106.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>7.906190e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>89.750000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>88.25000</td>\n",
+       "      <td>135.250000</td>\n",
+       "      <td>66.000000</td>\n",
+       "      <td>8.559185e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>116.500000</td>\n",
+       "      <td>113.000000</td>\n",
+       "      <td>115.00000</td>\n",
+       "      <td>146.500000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>9.053990e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>135.500000</td>\n",
+       "      <td>129.750000</td>\n",
+       "      <td>128.00000</td>\n",
+       "      <td>172.000000</td>\n",
+       "      <td>70.500000</td>\n",
+       "      <td>9.500780e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>144.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.00000</td>\n",
+       "      <td>192.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>1.079549e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             FSIQ         VIQ        PIQ      Weight     Height     MRI_Count\n",
+       "count   40.000000   40.000000   40.00000   38.000000  39.000000  4.000000e+01\n",
+       "mean   113.450000  112.350000  111.02500  151.052632  68.525641  9.087550e+05\n",
+       "std     24.082071   23.616107   22.47105   23.478509   3.994649  7.228205e+04\n",
+       "min     77.000000   71.000000   72.00000  106.000000  62.000000  7.906190e+05\n",
+       "25%     89.750000   90.000000   88.25000  135.250000  66.000000  8.559185e+05\n",
+       "50%    116.500000  113.000000  115.00000  146.500000  68.000000  9.053990e+05\n",
+       "75%    135.500000  129.750000  128.00000  172.000000  70.500000  9.500780e+05\n",
+       "max    144.000000  150.000000  150.00000  192.000000  77.000000  1.079549e+06"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "brainFrame.?()"
+    "brainFrame.describe()"
    ]
   },
   {
@@ -176,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,12 +703,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "menDf = brainFrame[?]\n",
-    "womenDf = brainFrame[?]"
+    "menDf = brainFrame[brainFrame['Gender']=='Male']\n",
+    "womenDf = brainFrame[brainFrame['Gender']=='Female']"
    ]
   },
   {
@@ -216,9 +724,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Ячейка для кода № 6\n",
     "menMeanSmarts = menDf[[\"PIQ\", \"FSIQ\", \"VIQ\"]].mean(axis=1)\n",
@@ -236,17 +755,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAGsCAYAAAAllFaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/YUlEQVR4nO3df1RVZaL/8c8BhUMhxwGVA4aCzJQxKiYGUeZtJgqzxTTJNI5WGqUuvegquHNLEiTqFrfujNGUWnddsylzxlyZLazhpjTZciJpQK/jVRl/XXGQH/4YD4bxQ87+/uHXY2cA4ZBwYPN+rbXXdPZ59t7P3kOdz36e/TzbYhiGIQAAgH7Ox9sVAAAAuBoINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQGZKj5/PPPlZKSovDwcFksFm3evNnjfRiGoV/96le6/vrr5e/vr5EjR+r555+/+pUFAABdMsjbFfCGhoYGxcbG6tFHH9WMGTO6tY/HH39cn3zyiX71q19p/PjxOnPmjM6cOXOVawoAALrKMtBfaGmxWPTBBx/opz/9qWtdU1OTli1bpt/97nc6e/asxo0bpxdffFF33HGHJGn//v2aMGGC9u7dqxtuuME7FQcAAG4GZPdTZxYvXqySkhL9/ve/1549e/TAAw9o2rRpOnjwoCSpsLBQY8aM0ZYtWxQVFaXIyEjNmzePlhoAALyIUPMPKisrtXbtWm3cuFG33367oqOj9ctf/lJTpkzR2rVrJUlHjhzRsWPHtHHjRr399tt66623VFZWpp/97Gderj0AAAPXgHym5kr+8pe/qLW1Vddff73b+qamJoWEhEiSnE6nmpqa9Pbbb7vKrVmzRnFxcaqoqKBLCgAALyDU/IOvv/5avr6+Kisrk6+vr9t3gYGBkqSwsDANGjTILfjceOONki629BBqAADofYSaf3DTTTeptbVVdXV1uv3229stc9ttt+nChQs6fPiwoqOjJUl//etfJUmjR4/utboCAIDLBuTop6+//lqHDh2SdDHErFixQj/60Y8UHBysUaNG6aGHHtKf/vQn/frXv9ZNN92kkydPqri4WBMmTNC9994rp9Opm2++WYGBgSooKJDT6VR6erqCgoL0ySefePnsAAAYmAZkqPnss8/0ox/9qM36uXPn6q233lJLS4v+7d/+TW+//baqqqo0bNgw3XLLLcrLy9P48eMlSSdOnNCSJUv0ySef6Nprr9U999yjX//61woODu7t0wEAABqgoQYAAJgPQ7oBAIApeBxquvPepM8++0yTJk2Sv7+/vv/97+utt95qU2blypWKjIyU1WpVQkKCSktL3b5vbGxUenq6QkJCFBgYqNTUVNXW1npafQAAYFIej37y9L1JR48e1b333quFCxfq3XffVXFxsebNm6ewsDAlJydLkjZs2KDMzEy9/vrrSkhIUEFBgZKTk1VRUaERI0ZIkjIyMvTRRx9p48aNstlsWrx4sWbMmKE//elPXaq30+nUiRMnNGTIEFksFk9PGwAAeIFhGDp37pzCw8Pl49NJW4zxHUgyPvjggyuWefLJJ40f/vCHbutmzpxpJCcnuz7Hx8cb6enprs+tra1GeHi4kZ+fbxiGYZw9e9YYPHiwsXHjRleZ/fv3G5KMkpKSLtX1+PHjhiQWFhYWFhaWfrgcP36809/6Hp+npqSkRElJSW7rkpOT9cQTT0iSmpubVVZWpqysLNf3Pj4+SkpKUklJiSSprKxMLS0tbvsZO3asRo0apZKSEt1yyy1tjtvU1KSmpibXZ+P/Pw99/PhxBQUFXbXzAwAAPae+vl4REREaMmRIp2V7PNTU1NQoNDTUbV1oaKjq6+v1zTff6O9//7taW1vbLXPgwAHXPvz8/DR06NA2ZWpqato9bn5+vvLy8tqsDwoKItQAANDPdOXREdOOfsrKypLD4XAtx48f93aVAABAD+rxlhq73d5mlFJtba2CgoIUEBAgX19f+fr6tlvGbre79tHc3KyzZ8+6tdZ8u8w/8vf3l7+//9U9GQAA0Gf1eEtNYmKiiouL3dZt3bpViYmJkiQ/Pz/FxcW5lXE6nSouLnaViYuL0+DBg93KVFRUqLKy0lUGAAAMbB631Hz7vUnSxSHbu3fvdr03KSsrS1VVVXr77bclSQsXLtRrr72mJ598Uo8++qg+/fRTvffee/roo49c+8jMzNTcuXM1efJkxcfHq6CgQA0NDUpLS5Mk2Ww2PfbYY8rMzFRwcLCCgoK0ZMkSJSYmtvuQMAAAGHg8DjV//vOf3d6blJmZKenye5Oqq6tVWVnp+j4qKkofffSRMjIy9Morr+i6667Tf/3Xf7nmqJGkmTNn6uTJk1q+fLlqamo0ceJEFRUVuT08/PLLL8vHx0epqalqampScnKyVq1a1a2TBgAA5jNg3v1UX18vm80mh8PB6CcAAPoJT36/TTv6CQAADCyEGgAAYAo9PqQbAACYR6vTUOnRM6o716gRQ6yKjwqWr0/feKcioQYAAHRJ0d5q5RXuU7Wj0bUuzGZVbkqMpo0L82LNLqL7CQAAdKpob7UWrSt3CzSSVONo1KJ15SraW+2lml1GqAEAAFfU6jSUV7hP7Q2XvrQur3CfWp3eHVBNqAEAAFdUevRMmxaabzMkVTsaVXr0TO9Vqh2EGgAAcEV15zoONN0p11MINQAA4IpGDLFe1XI9hVADAACuKD4qWGE2qzoauG3RxVFQ8VHBvVmtNgg1AADginx9LMpNiZGkNsHm0ufclBivz1dDqAEAAJ2aNi5Mqx+aJLvNvYvJbrNq9UOT+sQ8NUy+BwAAumTauDDdFWNnRmEAAND/+fpYlBgd4u1qtIvuJwAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYAqEGgAAYArdCjUrV65UZGSkrFarEhISVFpa2mHZlpYWPfvss4qOjpbValVsbKyKiorcykRGRspisbRZ0tPTXWXuuOOONt8vXLiwO9UHAAAm5HGo2bBhgzIzM5Wbm6vy8nLFxsYqOTlZdXV17ZbPzs7WG2+8oVdffVX79u3TwoULdf/992vXrl2uMl999ZWqq6tdy9atWyVJDzzwgNu+5s+f71bupZde8rT6AADApCyGYRiebJCQkKCbb75Zr732miTJ6XQqIiJCS5Ys0dKlS9uUDw8P17Jly9xaXVJTUxUQEKB169a1e4wnnnhCW7Zs0cGDB2WxWCRdbKmZOHGiCgoKPKmuS319vWw2mxwOh4KCgrq1DwAA0Ls8+f32qKWmublZZWVlSkpKurwDHx8lJSWppKSk3W2amppktVrd1gUEBGjHjh0dHmPdunV69NFHXYHmknfffVfDhg3TuHHjlJWVpfPnz3dY16amJtXX17stAADAvAZ5UvjUqVNqbW1VaGio2/rQ0FAdOHCg3W2Sk5O1YsUKTZ06VdHR0SouLtamTZvU2trabvnNmzfr7NmzeuSRR9zWz549W6NHj1Z4eLj27Nmjp556ShUVFdq0aVO7+8nPz1deXp4npwcAAPoxj0JNd7zyyiuaP3++xo4dK4vFoujoaKWlpenNN99st/yaNWt0zz33KDw83G39ggULXP88fvx4hYWF6c4779Thw4cVHR3dZj9ZWVnKzMx0fa6vr1dERMRVOisAANDXeNT9NGzYMPn6+qq2ttZtfW1trex2e7vbDB8+XJs3b1ZDQ4OOHTumAwcOKDAwUGPGjGlT9tixY9q2bZvmzZvXaV0SEhIkSYcOHWr3e39/fwUFBbktAADAvDwKNX5+foqLi1NxcbFrndPpVHFxsRITE6+4rdVq1ciRI3XhwgW9//77uu+++9qUWbt2rUaMGKF7772307rs3r1bkhQWFubJKQAAAJPyuPspMzNTc+fO1eTJkxUfH6+CggI1NDQoLS1NkjRnzhyNHDlS+fn5kqSdO3eqqqpKEydOVFVVlZ555hk5nU49+eSTbvt1Op1au3at5s6dq0GD3Kt1+PBhrV+/XtOnT1dISIj27NmjjIwMTZ06VRMmTOjuuQMAABPxONTMnDlTJ0+e1PLly1VTU6OJEyeqqKjI9fBwZWWlfHwuNwA1NjYqOztbR44cUWBgoKZPn6533nlHQ4cOddvvtm3bVFlZqUcffbTNMf38/LRt2zZXgIqIiFBqaqqys7M9rT4AADApj+ep6a+YpwYAgP6nx+apAQAA6KsINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQGebsCAACg72l1Gio9ekZ15xo1YohV8VHB8vWxeLtaV0SoAQAAbor2ViuvcJ+qHY2udWE2q3JTYjRtXJgXa3ZldD8BAACXor3VWrSu3C3QSFKNo1GL1pWraG+1l2rWOUINAACQdLHLKa9wn4x2vru0Lq9wn1qd7ZXwvm6FmpUrVyoyMlJWq1UJCQkqLS3tsGxLS4ueffZZRUdHy2q1KjY2VkVFRW5lnnnmGVksFrdl7NixbmUaGxuVnp6ukJAQBQYGKjU1VbW1td2pPgAAaEfp0TNtWmi+zZBU7WhU6dEzvVcpD3gcajZs2KDMzEzl5uaqvLxcsbGxSk5OVl1dXbvls7Oz9cYbb+jVV1/Vvn37tHDhQt1///3atWuXW7kf/vCHqq6udi07duxw+z4jI0OFhYXauHGjtm/frhMnTmjGjBmeVh8AAHSg7lzHgaY75Xqbx6FmxYoVmj9/vtLS0hQTE6PXX39d11xzjd588812y7/zzjt6+umnNX36dI0ZM0aLFi3S9OnT9etf/9qt3KBBg2S3213LsGHDXN85HA6tWbNGK1as0I9//GPFxcVp7dq1+uKLL/Tll196egoAAPRprU5DJYdP68PdVSo5fLrXuntGDLFe1XK9zaPRT83NzSorK1NWVpZrnY+Pj5KSklRSUtLuNk1NTbJa3U8+ICCgTUvMwYMHFR4eLqvVqsTEROXn52vUqFGSpLKyMrW0tCgpKclVfuzYsRo1apRKSkp0yy23tHvcpqYm1+f6+npPThUAAK/w5sij+KhghdmsqnE0tvtcjUWS3XZxeHdf5FFLzalTp9Ta2qrQ0FC39aGhoaqpqWl3m+TkZK1YsUIHDx6U0+nU1q1btWnTJlVXX356OiEhQW+99ZaKioq0evVqHT16VLfffrvOnTsnSaqpqZGfn5+GDh3a5ePm5+fLZrO5loiICE9OFQCAXuftkUe+PhblpsRIuhhgvu3S59yUmD47X02Pj3565ZVX9IMf/EBjx46Vn5+fFi9erLS0NPn4XD70PffcowceeEATJkxQcnKyPv74Y509e1bvvfdet4+blZUlh8PhWo4fP341TgcAgB7RV0YeTRsXptUPTZLd5t7LYrdZtfqhSX16nhqPup+GDRsmX1/fNqOOamtrZbfb291m+PDh2rx5sxobG3X69GmFh4dr6dKlGjNmTIfHGTp0qK6//nodOnRIkmS329Xc3KyzZ8+6tdZc6bj+/v7y9/f35PQAAPAaT0YeJUaH9Ghdpo0L010x9n43o7BHLTV+fn6Ki4tTcXGxa53T6VRxcbESExOvuK3VatXIkSN14cIFvf/++7rvvvs6LPv111/r8OHDCgu7mAbj4uI0ePBgt+NWVFSosrKy0+MCANAf9LWRR74+FiVGh+i+iSOVGB3S5wON1I3XJGRmZmru3LmaPHmy4uPjVVBQoIaGBqWlpUmS5syZo5EjRyo/P1+StHPnTlVVVWnixImqqqrSM888I6fTqSeffNK1z1/+8pdKSUnR6NGjdeLECeXm5srX11ezZs2SJNlsNj322GPKzMxUcHCwgoKCtGTJEiUmJrb7kDAAAP1Nfx951Bd4HGpmzpypkydPavny5aqpqdHEiRNVVFTkeni4srLS7XmZxsZGZWdn68iRIwoMDNT06dP1zjvvuHUj/e1vf9OsWbN0+vRpDR8+XFOmTNGXX36p4cOHu8q8/PLL8vHxUWpqqpqampScnKxVq1Z9h1MHAKDv6O8jj/oCi2EYfXOu46usvr5eNptNDodDQUFB3q4OAABtXBr9JMkt2Fzq+OnrD+r2BE9+v3n3EwAAfUR/HnnUF3jc/QQAAHpOfx151BcQagAA6GMujTyCZ+h+AgAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApjDI2xUA0HtanYZKj55R3blGjRhiVXxUsHx9LN6uFgBcFYQaYIAo2lutvMJ9qnY0utaF2azKTYnRtHFhXqwZAFwddD8BA0DR3motWlfuFmgkqcbRqEXrylW0t9pLNQOAq4dQA5hcq9NQXuE+Ge18d2ldXuE+tTrbKwEA/QehBjC50qNn2rTQfJshqdrRqNKjZ3qvUgDQAwg1gMnVnes40HSnHAD0VYQawORGDLFe1XIA0Fd1K9SsXLlSkZGRslqtSkhIUGlpaYdlW1pa9Oyzzyo6OlpWq1WxsbEqKipyK5Ofn6+bb75ZQ4YM0YgRI/TTn/5UFRUVbmXuuOMOWSwWt2XhwoXdqT4woMRHBSvMZlVHA7ctujgKKj4quDerBQBXncehZsOGDcrMzFRubq7Ky8sVGxur5ORk1dXVtVs+Oztbb7zxhl599VXt27dPCxcu1P33369du3a5ymzfvl3p6en68ssvtXXrVrW0tOjuu+9WQ0OD277mz5+v6upq1/LSSy95Wn1gwPH1sSg3JUaS2gSbS59zU2KYrwZAv2cxDMOjIQ8JCQm6+eab9dprr0mSnE6nIiIitGTJEi1durRN+fDwcC1btkzp6emudampqQoICNC6devaPcbJkyc1YsQIbd++XVOnTpV0saVm4sSJKigo8KS6LvX19bLZbHI4HAoKCurWPoD+jHlqAPRHnvx+ezT5XnNzs8rKypSVleVa5+Pjo6SkJJWUlLS7TVNTk6xW9776gIAA7dixo8PjOBwOSVJwsHtz+Lvvvqt169bJbrcrJSVFOTk5uuaaazo8blNTk+tzfX39lU8OMLlp48J0V4ydGYUBmJZHoebUqVNqbW1VaGio2/rQ0FAdOHCg3W2Sk5O1YsUKTZ06VdHR0SouLtamTZvU2trabnmn06knnnhCt912m8aNG+daP3v2bI0ePVrh4eHas2ePnnrqKVVUVGjTpk3t7ic/P195eXmenB5ger4+FiVGh3i7GgDQI3r8NQmvvPKK5s+fr7Fjx8pisSg6OlppaWl688032y2fnp6uvXv3tmnJWbBggeufx48fr7CwMN155506fPiwoqOj2+wnKytLmZmZrs/19fWKiIi4SmcFAAD6Go8eFB42bJh8fX1VW1vrtr62tlZ2u73dbYYPH67NmzeroaFBx44d04EDBxQYGKgxY8a0Kbt48WJt2bJFf/zjH3XdddddsS4JCQmSpEOHDrX7vb+/v4KCgtwWAABgXh6FGj8/P8XFxam4uNi1zul0qri4WImJiVfc1mq1auTIkbpw4YLef/993Xfffa7vDMPQ4sWL9cEHH+jTTz9VVFRUp3XZvXu3JCksjAccAQBAN7qfMjMzNXfuXE2ePFnx8fEqKChQQ0OD0tLSJElz5szRyJEjlZ+fL0nauXOnqqqqNHHiRFVVVemZZ56R0+nUk08+6dpnenq61q9frw8//FBDhgxRTU2NJMlmsykgIECHDx/W+vXrNX36dIWEhGjPnj3KyMjQ1KlTNWHChKtxHQAAQD/ncaiZOXOmTp48qeXLl6umpkYTJ05UUVGR6+HhyspK+fhcbgBqbGxUdna2jhw5osDAQE2fPl3vvPOOhg4d6iqzevVqSReHbX/b2rVr9cgjj8jPz0/btm1zBaiIiAilpqYqOzu7G6cMAADMyON5avor5qkBAKD/8eT3m3c/AQAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAU+hWqFm5cqUiIyNltVqVkJCg0tLSDsu2tLTo2WefVXR0tKxWq2JjY1VUVOTxPhsbG5Wenq6QkBAFBgYqNTVVtbW13ak+AAAwIY9DzYYNG5SZmanc3FyVl5crNjZWycnJqqura7d8dna23njjDb366qvat2+fFi5cqPvvv1+7du3yaJ8ZGRkqLCzUxo0btX37dp04cUIzZszoxikDAAAzshiGYXiyQUJCgm6++Wa99tprkiSn06mIiAgtWbJES5cubVM+PDxcy5YtU3p6umtdamqqAgICtG7dui7t0+FwaPjw4Vq/fr1+9rOfSZIOHDigG2+8USUlJbrllls6rXd9fb1sNpscDoeCgoI8OWUAAOAlnvx+e9RS09zcrLKyMiUlJV3egY+PkpKSVFJS0u42TU1NslqtbusCAgK0Y8eOLu+zrKxMLS0tbmXGjh2rUaNGXfG49fX1bgsAADAvj0LNqVOn1NraqtDQULf1oaGhqqmpaXeb5ORkrVixQgcPHpTT6dTWrVu1adMmVVdXd3mfNTU18vPz09ChQ7t83Pz8fNlsNtcSERHhyakCAIB+psdHP73yyiv6wQ9+oLFjx8rPz0+LFy9WWlqafHx69tBZWVlyOByu5fjx4z16PAAA4F0eJYthw4bJ19e3zaij2tpa2e32drcZPny4Nm/erIaGBh07dkwHDhxQYGCgxowZ0+V92u12NTc36+zZs10+rr+/v4KCgtwWAABgXh6FGj8/P8XFxam4uNi1zul0qri4WImJiVfc1mq1auTIkbpw4YLef/993XfffV3eZ1xcnAYPHuxWpqKiQpWVlZ0eFwAADAyDPN0gMzNTc+fO1eTJkxUfH6+CggI1NDQoLS1NkjRnzhyNHDlS+fn5kqSdO3eqqqpKEydOVFVVlZ555hk5nU49+eSTXd6nzWbTY489pszMTAUHBysoKEhLlixRYmJil0Y+AQAA8/M41MycOVMnT57U8uXLVVNTo4kTJ6qoqMj1oG9lZaXb8zKNjY3Kzs7WkSNHFBgYqOnTp+udd95xe+i3s31K0ssvvywfHx+lpqaqqalJycnJWrVq1Xc4dfRXrU5DpUfPqO5co0YMsSo+Kli+PhZvVwsA4GUez1PTXzFPjTkU7a1WXuE+VTsaXevCbFblpsRo2rgwL9YMANATemyeGsCbivZWa9G6crdAI0k1jkYtWleuor3VXqoZAKAvINSgX2h1Gsor3Kf2mhUvrcsr3KdW54BoeAQAtINQg36h9OiZNi0032ZIqnY0qvTomd6rFACgTyHUoF+oO9dxoOlOOQCA+RBq0C+MGGLtvJAH5QAA5kOoQb8QHxWsMJtVHQ3ctujiKKj4qODerBYAoA8h1KBf8PWxKDclRpLaBJtLn3NTYpivBgAGMEIN+o1p48K0+qFJstvcu5jsNqtWPzSJeWoAYIDzeEZhwJumjQvTXTF2ZhQGALRBqEG/4+tjUWJ0iLerAQDoY+h+AgAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApkCoAQAApjDI2xUABopWp6HSo2dUd65RI4ZYFR8VLF8fi7erBQCmQagBekHR3mrlFe5TtaPRtS7MZlVuSoymjQvzYs0AwDzofgJ6WNHeai1aV+4WaCSpxtGoRevKVbS32ks1AwBzIdQAPajVaSivcJ+Mdr67tC6vcJ9ane2VAAB4glAD9KDSo2fatNB8myGp2tGo0qNneq9SAGBShBqgB9Wd6zjQdKccAKBjhBqgB40YYr2q5QAAHetWqFm5cqUiIyNltVqVkJCg0tLSK5YvKCjQDTfcoICAAEVERCgjI0ONjZfvTCMjI2WxWNos6enprjJ33HFHm+8XLlzYneoDvSY+KlhhNqs6Grht0cVRUPFRwb1ZLQAwJY9DzYYNG5SZmanc3FyVl5crNjZWycnJqqura7f8+vXrtXTpUuXm5mr//v1as2aNNmzYoKefftpV5quvvlJ1dbVr2bp1qyTpgQcecNvX/Pnz3cq99NJLnlYf6FW+PhblpsRIUptgc+lzbkoM89UAwFXgcahZsWKF5s+fr7S0NMXExOj111/XNddcozfffLPd8l988YVuu+02zZ49W5GRkbr77rs1a9Yst9ad4cOHy263u5YtW7YoOjpa//RP/+S2r2uuucatXFBQkKfVB3rdtHFhWv3QJNlt7l1MdptVqx+axDw1AHCVeDT5XnNzs8rKypSVleVa5+Pjo6SkJJWUlLS7za233qp169aptLRU8fHxOnLkiD7++GM9/PDDHR5j3bp1yszMlMXifvf67rvvat26dbLb7UpJSVFOTo6uueaadvfT1NSkpqYm1+f6+npPThW4qqaNC9NdMXZmFAaAHuRRqDl16pRaW1sVGhrqtj40NFQHDhxod5vZs2fr1KlTmjJligzD0IULF7Rw4UK37qdv27x5s86ePatHHnmkzX5Gjx6t8PBw7dmzR0899ZQqKiq0adOmdveTn5+vvLw8T04P6FG+PhYlRod4uxoAYFo9/pqEzz77TC+88IJWrVqlhIQEHTp0SI8//riee+455eTktCm/Zs0a3XPPPQoPD3dbv2DBAtc/jx8/XmFhYbrzzjt1+PBhRUdHt9lPVlaWMjMzXZ/r6+sVERFxFc8MAAD0JR6FmmHDhsnX11e1tbVu62tra2W329vdJicnRw8//LDmzZsn6WIgaWho0IIFC7Rs2TL5+Fx+rOfYsWPatm1bh60v35aQkCBJOnToULuhxt/fX/7+/l0+NwAA0L959KCwn5+f4uLiVFxc7FrndDpVXFysxMTEdrc5f/68W3CRJF9fX0mSYbhPDb927VqNGDFC9957b6d12b17tyQpLIyHLAEAQDe6nzIzMzV37lxNnjxZ8fHxKigoUENDg9LS0iRJc+bM0ciRI5Wfny9JSklJ0YoVK3TTTTe5up9ycnKUkpLiCjfSxXC0du1azZ07V4MGuVfr8OHDWr9+vaZPn66QkBDt2bNHGRkZmjp1qiZMmPBdzh8AAJiEx6Fm5syZOnnypJYvX66amhpNnDhRRUVFroeHKysr3VpmsrOzZbFYlJ2draqqKg0fPlwpKSl6/vnn3fa7bds2VVZW6tFHH21zTD8/P23bts0VoCIiIpSamqrs7GxPqw8AAEzKYvxjH5BJ1dfXy2azyeFwML8NAAD9hCe/37z7CQAAmAKhBgAAmEKPz1MDAIBZtDoNZgbvwwg1AAB0QdHeauUV7lO1o9G1LsxmVW5KDO9w6yPofhqgWp2GSg6f1oe7q1Ry+LRanQPieXEA6JaivdVatK7cLdBIUo2jUYvWlatob7WXaoZvo6VmAOJuAwC6rtVpKK9wn9q79TMkWSTlFe7TXTF2uqK8jJaaAYa7DQDwTOnRM23+m/lthqRqR6NKj57pvUqhXYSaAaSzuw3p4t0GXVEAcFnduY4DTXfKoecQagYQ7jYAwHMjhlivajn0HELNAMLdBgB4Lj4qWGE2qzp6Wsaii88lxkcF92a10A5CzQDC3QYAeM7Xx6LclBhJahNsLn3OTYnhIeE+gFAzgHC3AQDdM21cmFY/NEl2m/tNn91m1eqHJjFytI9gSPcAculuY9G6clkktweGudsAgCubNi5Md8XYmVG4D+Mt3QMQ89QAAPoLT36/aakZgLjbAACYEaFmgPL1sSgxOsTb1QAA4KrhQWEAAGAKtNQAANBHtToNHhXwAKEGAIA+iEEdnqP7CQCAPqa/vXy41Wmo5PBpfbi7SiWHT3vtHYK01AAA0Id09vJhiy6+fPiuGHuf6IrqSy1KtNQAANCH9KeXD/e1FiVCDQAAfUh/eflwZy1K0sUWpd7siiLUAF3UV/qMAZhbf3n5cF9sUeKZGqAL+lKfMQBzu/Ty4RpHY7utIBZdfJGmt18+3BdblGipATrR1/qMAZjbpZcPS5dfNnxJX3r5cF9sUSLUAFfQF/uMAfR937W7etq4MK1+aJLsNvdAYLdZtfqhSX2ihfhSi1JH0cqiiy3avdmiRPcTcAWe9BnzLi0A0tXrru7rLx++1KK0aF25LJLbzZ+3WpRoqQGuoC/2GQPou652d/Wllw/fN3GkEqND+kyguaSvtSjRUgNcQV/sMwbQN/W3SfOulr7UokSoAa6gv4xCAOB9A7m7+lKLkrd1q/tp5cqVioyMlNVqVUJCgkpLS69YvqCgQDfccIMCAgIUERGhjIwMNTZe/j/+mWeekcVicVvGjh3rto/Gxkalp6crJCREgYGBSk1NVW1tbXeqD3RZfxmFAMD76K72Po9DzYYNG5SZmanc3FyVl5crNjZWycnJqqura7f8+vXrtXTpUuXm5mr//v1as2aNNmzYoKefftqt3A9/+ENVV1e7lh07drh9n5GRocLCQm3cuFHbt2/XiRMnNGPGDE+rD3isr/UZA+ib6K72Po+7n1asWKH58+crLS1NkvT666/ro48+0ptvvqmlS5e2Kf/FF1/otttu0+zZsyVJkZGRmjVrlnbu3OlekUGDZLfb2z2mw+HQmjVrtH79ev34xz+WJK1du1Y33nijvvzyS91yyy2engbgkb7UZwygb6K72vs8aqlpbm5WWVmZkpKSLu/Ax0dJSUkqKSlpd5tbb71VZWVlri6qI0eO6OOPP9b06dPdyh08eFDh4eEaM2aMHnzwQVVWVrq+KysrU0tLi9txx44dq1GjRnV43KamJtXX17stwHfR10chAPAuuqu9z6NQc+rUKbW2tio0NNRtfWhoqGpqatrdZvbs2Xr22Wc1ZcoUDR48WNHR0brjjjvcup8SEhL01ltvqaioSKtXr9bRo0d1++2369y5c5Kkmpoa+fn5aejQoV0+bn5+vmw2m2uJiIjw5FQBAPAY3dXe1eOjnz777DO98MILWrVqlRISEnTo0CE9/vjjeu6555STkyNJuueee1zlJ0yYoISEBI0ePVrvvfeeHnvssW4dNysrS5mZma7P9fX1BBsAQI+ju9p7PAo1w4YNk6+vb5tRR7W1tR0+D5OTk6OHH35Y8+bNkySNHz9eDQ0NWrBggZYtWyYfn7aNRUOHDtX111+vQ4cOSZLsdruam5t19uxZt9aaKx3X399f/v7+npweAABXRV8Z4jzQeNT95Ofnp7i4OBUXF7vWOZ1OFRcXKzExsd1tzp8/3ya4+Pr6SpIMo/13YXz99dc6fPiwwsIuNtPFxcVp8ODBbsetqKhQZWVlh8cFAAADi8fdT5mZmZo7d64mT56s+Ph4FRQUqKGhwTUaas6cORo5cqTy8/MlSSkpKVqxYoVuuukmV/dTTk6OUlJSXOHml7/8pVJSUjR69GidOHFCubm58vX11axZsyRJNptNjz32mDIzMxUcHKygoCAtWbJEiYmJjHwCAACSuhFqZs6cqZMnT2r58uWqqanRxIkTVVRU5Hp4uLKy0q1lJjs7WxaLRdnZ2aqqqtLw4cOVkpKi559/3lXmb3/7m2bNmqXTp09r+PDhmjJlir788ksNHz7cVebll1+Wj4+PUlNT1dTUpOTkZK1ateq7nDsAADARi9FRH5DJ1NfXy2azyeFwKCgoyNvVAQAAXeDJ7zdv6QYAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKZAqAEAAKYwyNsVQN/W6jRUevSM6s41asQQq+KjguXrY/F2tQAAaINQgw4V7a1WXuE+VTsaXevCbFblpsRo2rgwL9YMAPoPbg57D6EG7SraW61F68pl/MP6GkejFq0r1+qHJhFsAKAT3Bz2Lp6pQRutTkN5hfvaBBpJrnV5hfvU6myvBABAunxz+O1AI12+OSzaW+2lmpkXoQZtlB490+Zfwm8zJFU7GlV69EzvVQoA+hFuDr2DUIM26s51HGi6U66/a3UaKjl8Wh/urlLJ4dP8RwhAp7g59A6eqUEbI4ZYr2q5/oz+cADdwc2hd9BSgzbio4IVZrOqo2fzLbr4wx4fFdyb1ep19IcD6C5uDr2DUIM2fH0syk2JkaQ2webS59yUGFMPSaQ/HMB3wc2hdxBq0K5p48K0+qFJstvc7yLsNuuAGM5NfziA74KbQ+/gmRp0aNq4MN0VYx+Qk0bRHw7gu7p0c/iPz+XZeS6vxxBqcEW+PhYlRod4uxq9jv5wAFfDQL459AZCDdCOS/3hNY7Gdp+rseji3Rb94QA6M1BvDr2BZ2qAdtAfDgD9D6EG6MBAf1gaAPobup+AK6A/HAD6D0IN0An6wwGgf+hW99PKlSsVGRkpq9WqhIQElZaWXrF8QUGBbrjhBgUEBCgiIkIZGRlqbLw8vC0/P18333yzhgwZohEjRuinP/2pKioq3PZxxx13yGKxuC0LFy7sTvUBAIAJeRxqNmzYoMzMTOXm5qq8vFyxsbFKTk5WXV1du+XXr1+vpUuXKjc3V/v379eaNWu0YcMGPf30064y27dvV3p6ur788ktt3bpVLS0tuvvuu9XQ0OC2r/nz56u6utq1vPTSS55WHwAAmJTH3U8rVqzQ/PnzlZaWJkl6/fXX9dFHH+nNN9/U0qVL25T/4osvdNttt2n27NmSpMjISM2aNUs7d+50lSkqKnLb5q233tKIESNUVlamqVOnutZfc801stvtnlYZAAAMAB611DQ3N6usrExJSUmXd+Djo6SkJJWUlLS7za233qqysjJXF9WRI0f08ccfa/r06R0ex+FwSJKCg93nAHn33Xc1bNgwjRs3TllZWTp//nyH+2hqalJ9fb3bAgAAzMujlppTp06ptbVVoaGhbutDQ0N14MCBdreZPXu2Tp06pSlTpsgwDF24cEELFy506376NqfTqSeeeEK33Xabxo0b57af0aNHKzw8XHv27NFTTz2liooKbdq0qd395OfnKy8vz5PTAwAA/ViPj3767LPP9MILL2jVqlVKSEjQoUOH9Pjjj+u5555TTk5Om/Lp6enau3evduzY4bZ+wYIFrn8eP368wsLCdOedd+rw4cOKjo5us5+srCxlZma6PtfX1ysiIuIqnhkAAOhLPAo1w4YNk6+vr2pra93W19bWdvisS05Ojh5++GHNmzdP0sVA0tDQoAULFmjZsmXy8bncA7Z48WJt2bJFn3/+ua677ror1iUhIUGSdOjQoXZDjb+/v/z9/T05PQAA0I959EyNn5+f4uLiVFxc7FrndDpVXFysxMTEdrc5f/68W3CRJF9fX0mSYRiu/128eLE++OADffrpp4qKiuq0Lrt375YkhYUxqysAAOhG91NmZqbmzp2ryZMnKz4+XgUFBWpoaHCNhpozZ45Gjhyp/Px8SVJKSopWrFihm266ydX9lJOTo5SUFFe4SU9P1/r16/Xhhx9qyJAhqqmpkSTZbDYFBATo8OHDWr9+vaZPn66QkBDt2bNHGRkZmjp1qiZMmHC1rgUAAOjHPA41M2fO1MmTJ7V8+XLV1NRo4sSJKioqcj08XFlZ6dYyk52dLYvFouzsbFVVVWn48OFKSUnR888/7yqzevVqSRcn2Pu2tWvX6pFHHpGfn5+2bdvmClARERFKTU1VdnZ2d84ZAACYkMW41AdkcvX19bLZbHI4HAoKCvJ2dQAAQBd48vvNW7oBAIAp8EJLAAD6gFanodKjZ1R3rlEjhlgVHxUsXx+Lt6vVrxBqAADwsqK91cor3Kdqx+WXPYfZrMpNidG0cYzy7Sq6nwAA8KKivdVatK7cLdBIUo2jUYvWlatob7WXatb/EGoAAPCSVqehvMJ9am/EzqV1eYX71OocEGN6vjNCDQAAXlJ69EybFppvMyRVOxpVevRM71WqHyPUAADgJXXnOg403Sk30BFqAADwkhFDrFe13EBHqAEAwEvio4IVZrOqo4HbFl0cBRUfFdyb1eq3CDUAAHiJr49FuSkxktQm2Fz6nJsSw3w1XUSoAQDAi6aNC9PqhybJbnPvYrLbrFr90CTmqfEAk+8BAOBl08aF6a4YOzMKf0eEGgAA+gBfH4sSo0O8XY1+je4nAABgCoQaAABgCoQaAABgCjxTA5hUq9PgoUMAAwqhBjChor3Vyivc5/ZOmTCbVbkpMQwPBWBadD8BJlO0t1qL1pW3eUlejaNRi9aVq2hvtZdqBgA9i1ADmEir01Be4T4Z7Xx3aV1e4T61OtsrAQD9G6EGMJHSo2fatNB8myGp2tGo0qNneq9SANBLCDWAidSd6zjQdKccAPQnhBrAREYMsXZeyINyANCfEGoAE4mPClaYzdrmbb+XWHRxFFR8VHBvVgsAegWhBjARXx+LclNiJKlNsLn0OTclhvlqAJgSoQYwmWnjwrT6oUmy29y7mOw2q1Y/NIl5agCYFpPvASY0bVyY7oqxM6MwgAGFUAOYlK+PRYnRId6uBgD0GrqfAACAKRBqAACAKRBqAACAKXQr1KxcuVKRkZGyWq1KSEhQaWnpFcsXFBTohhtuUEBAgCIiIpSRkaHGRvcZTTvbZ2Njo9LT0xUSEqLAwEClpqaqtra2O9UHAAAm5HGo2bBhgzIzM5Wbm6vy8nLFxsYqOTlZdXV17ZZfv369li5dqtzcXO3fv19r1qzRhg0b9PTTT3u0z4yMDBUWFmrjxo3avn27Tpw4oRkzZnTjlNHTWp2GSg6f1oe7q1Ry+DQvTwQA9AqLYRge/eIkJCTo5ptv1muvvSZJcjqdioiI0JIlS7R06dI25RcvXqz9+/eruLjYte5f/uVftHPnTu3YsaNL+3Q4HBo+fLjWr1+vn/3sZ5KkAwcO6MYbb1RJSYluueWWTutdX18vm80mh8OhoKAgT04ZHijaW628wn1uL1UMs1mVmxLD/CgAAI958vvtUUtNc3OzysrKlJSUdHkHPj5KSkpSSUlJu9vceuutKisrc3UnHTlyRB9//LGmT5/e5X2WlZWppaXFrczYsWM1atSoDo/b1NSk+vp6twU9q2hvtRatK2/zlugaR6MWrStX0d5qL9UMADAQeBRqTp06pdbWVoWGhrqtDw0NVU1NTbvbzJ49W88++6ymTJmiwYMHKzo6WnfccYer+6kr+6ypqZGfn5+GDh3a5ePm5+fLZrO5loiICE9OFR5qdRrKK9yn9pr9Lq3LK9xHVxQAoMf0+Oinzz77TC+88IJWrVql8vJybdq0SR999JGee+65Hj1uVlaWHA6Hazl+/HiPHm+gKz16pk0LzbcZkqodjSo9eqb3KgUAGFA8mlF42LBh8vX1bTPqqLa2Vna7vd1tcnJy9PDDD2vevHmSpPHjx6uhoUELFizQsmXLurRPu92u5uZmnT171q215krH9ff3l7+/vyenh++g7lzHgaY75QAA8JRHLTV+fn6Ki4tze+jX6XSquLhYiYmJ7W5z/vx5+fi4H8bX11eSZBhGl/YZFxenwYMHu5WpqKhQZWVlh8dF7xoxxNp5IQ/KAQDgKY/f/ZSZmam5c+dq8uTJio+PV0FBgRoaGpSWliZJmjNnjkaOHKn8/HxJUkpKilasWKGbbrpJCQkJOnTokHJycpSSkuIKN53t02az6bHHHlNmZqaCg4MVFBSkJUuWKDExsUsjn9Dz4qOCFWazqsbR2O5zNRZdfEt0fFRwb1cNADBAeBxqZs6cqZMnT2r58uWqqanRxIkTVVRU5HrQt7Ky0q1lJjs7WxaLRdnZ2aqqqtLw4cOVkpKi559/vsv7lKSXX35ZPj4+Sk1NVVNTk5KTk7Vq1arvcu64inx9LMpNidGideWySG7B5tJ7oXNTYnhLNACgx3g8T01/1VPz1LQ6DZUePaO6c40aMeRiS8RA/uFmnhoAwNXkye+3xy01uIwf8LamjQvTXTF2gh4AoNfRUtNNlyaa+8eLd+mne/VDkwZssAEA4GrpsRmFcRETzQEA0PcQarqBieYAAP3JQHnRMM/UdAMTzQEA+ouB9PwnLTXdwERzAID+YKC9aJhQ0w2XJprraDyPRRdTMBPNAQC8ZSA+/0mo6YZLE81JahNsmGgOANAXDMTnPwk13TRtXJhWPzRJdpt7F5PdZmU4NwDA6wbi8588KPwdMNEcAKCvGojPfxJqviNfH4sSo0O8XQ0AANwMxBcN0/0EAIAJDcTnPwk1AACY1EB7/pPuJwAATGwgPf9JqAEAwOQGyvOfdD8BAABTINQAAABTINQAAABTINQAAABTINQAAABTINQAAABTINQAAABTINQAAABTINQAAABTGDAzChvGxXeU1tfXe7kmAACgqy79bl/6Hb+SARNqzp07J0mKiIjwck0AAICnzp07J5vNdsUyFqMr0ccEnE6nTpw4oSFDhshi6dsv8aqvr1dERISOHz+uoKAgb1en3+C6eY5r5jmuWfdw3TzHNbvIMAydO3dO4eHh8vG58lMzA6alxsfHR9ddd523q+GRoKCgAf2H3F1cN89xzTzHNeserpvnuGbqtIXmEh4UBgAApkCoAQAApkCo6YP8/f2Vm5srf39/b1elX+G6eY5r5jmuWfdw3TzHNfPcgHlQGAAAmBstNQAAwBQINQAAwBQINQAAwBQINQAAwBQINV7U2tqqnJwcRUVFKSAgQNHR0Xruuefc3m9hGIaWL1+usLAwBQQEKCkpSQcPHvRirb3v3LlzeuKJJzR69GgFBATo1ltv1VdffeX6fqBfs88//1wpKSkKDw+XxWLR5s2b3b7vyvU5c+aMHnzwQQUFBWno0KF67LHH9PXXX/fiWfS+zq7bpk2bdPfddyskJEQWi0W7d+9us4/Gxkalp6crJCREgYGBSk1NVW1tbe+cgBdc6Zq1tLToqaee0vjx43XttdcqPDxcc+bM0YkTJ9z2MdD+1jr7O3vmmWc0duxYXXvttfre976npKQk7dy5063MQLtmniDUeNGLL76o1atX67XXXtP+/fv14osv6qWXXtKrr77qKvPSSy/pN7/5jV5//XXt3LlT1157rZKTk9XY2OjFmnvXvHnztHXrVr3zzjv6y1/+orvvvltJSUmqqqqSxDVraGhQbGysVq5c2e73Xbk+Dz74oP73f/9XW7du1ZYtW/T5559rwYIFvXUKXtHZdWtoaNCUKVP04osvdriPjIwMFRYWauPGjdq+fbtOnDihGTNm9FSVve5K1+z8+fMqLy9XTk6OysvLtWnTJlVUVOgnP/mJW7mB9rfW2d/Z9ddfr9dee01/+ctftGPHDkVGRuruu+/WyZMnXWUG2jXziAGvuffee41HH33Ubd2MGTOMBx980DAMw3A6nYbdbjf+4z/+w/X92bNnDX9/f+N3v/tdr9a1rzh//rzh6+trbNmyxW39pEmTjGXLlnHN/oEk44MPPnB97sr12bdvnyHJ+Oqrr1xl/vCHPxgWi8Woqqrqtbp70z9et287evSoIcnYtWuX2/qzZ88agwcPNjZu3Ohat3//fkOSUVJS0oO17RuudM0uKS0tNSQZx44dMwyDv7WuXDOHw2FIMrZt22YYBtesM7TUeNGtt96q4uJi/fWvf5Uk/c///I927Nihe+65R5J09OhR1dTUKCkpybWNzWZTQkKCSkpKvFJnb7tw4YJaW1tltVrd1gcEBGjHjh1cs0505fqUlJRo6NChmjx5sqtMUlKSfHx82jSD47KysjK1tLS4XduxY8dq1KhR/O39fw6HQxaLRUOHDpXE31pnmpub9Z//+Z+y2WyKjY2VxDXrzIB5oWVftHTpUtXX12vs2LHy9fVVa2urnn/+eT344IOSpJqaGklSaGio23ahoaGu7waaIUOGKDExUc8995xuvPFGhYaG6ne/+51KSkr0/e9/n2vWia5cn5qaGo0YMcLt+0GDBik4OJhreAU1NTXy8/Nz/WBfwt/eRY2NjXrqqac0a9Ys18sZ+Vtr35YtW/SLX/xC58+fV1hYmLZu3aphw4ZJ4pp1hpYaL3rvvff07rvvav369SovL9dvf/tb/epXv9Jvf/tbb1etT3vnnXdkGIZGjhwpf39//eY3v9GsWbM6fSU9AO9oaWnRz3/+cxmGodWrV3u7On3ej370I+3evVtffPGFpk2bpp///Oeqq6vzdrX6BX4FvOhf//VftXTpUv3iF7/Q+PHj9fDDDysjI0P5+fmSJLvdLkltRk/U1ta6vhuIoqOjtX37dn399dc6fvy4SktL1dLSojFjxnDNOtGV62O329v8B/TChQs6c+YM1/AK7Ha7mpubdfbsWbf1A/1v71KgOXbsmLZu3epqpZH4W+vItddeq+9///u65ZZbtGbNGg0aNEhr1qyRxDXrDKHGi86fP9+mdcHX11dOp1OSFBUVJbvdruLiYtf39fX12rlzpxITE3u1rn3Rtddeq7CwMP3973/Xf//3f+u+++7jmnWiK9cnMTFRZ8+eVVlZmavMp59+KqfTqYSEhF6vc38RFxenwYMHu13biooKVVZWDti/vUuB5uDBg9q2bZtCQkLcvudvrWucTqeampokcc065e0nlQeyuXPnGiNHjjS2bNliHD161Ni0aZMxbNgw48knn3SV+fd//3dj6NChxocffmjs2bPHuO+++4yoqCjjm2++8WLNvauoqMj4wx/+YBw5csT45JNPjNjYWCMhIcFobm42DINrdu7cOWPXrl3Grl27DEnGihUrjF27drlGnHTl+kybNs246aabjJ07dxo7duwwfvCDHxizZs3y1in1is6u2+nTp41du3YZH330kSHJ+P3vf2/s2rXLqK6udu1j4cKFxqhRo4xPP/3U+POf/2wkJiYaiYmJ3jqlHnela9bc3Gz85Cc/Ma677jpj9+7dRnV1tWtpampy7WOg/a1d6Zp9/fXXRlZWllFSUmL83//9n/HnP//ZSEtLM/z9/Y29e/e69jHQrpknCDVeVF9fbzz++OPGqFGjDKvVaowZM8ZYtmyZ27/wTqfTyMnJMUJDQw1/f3/jzjvvNCoqKrxYa+/bsGGDMWbMGMPPz8+w2+1Genq6cfbsWdf3A/2a/fGPfzQktVnmzp1rGEbXrs/p06eNWbNmGYGBgUZQUJCRlpZmnDt3zgtn03s6u25r165t9/vc3FzXPr755hvjn//5n43vfe97xjXXXGPcf//9bqHHbK50zS4NfW9v+eMf/+jax0D7W7vSNfvmm2+M+++/3wgPDzf8/PyMsLAw4yc/+YlRWlrqto+Bds08YTGMb01fCwAA0E/xTA0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADCF/wdmgGgDWP5ZSwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Ячейка для кода № 7\n",
     "# Постройка графика диаграммы рассеяния для кадра данных с женскими записями\n",
-    "womenMeanSmarts = ???\n",
-    "plt.scatter(???)\n",
-    "\n",
+    "womenMeanSmarts = womenDf[['PIQ','FSIQ','VIQ']].mean(axis = 1)\n",
+    "plt.scatter(womenMeanSmarts,womenDf['MRI_Count'])\n",
+    "plt.show()\n",
+    "%matplotlib inline\n",
     "#\n",
     "#"
    ]
@@ -268,11 +799,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "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>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FSIQ</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.946639</td>\n",
+       "      <td>0.934125</td>\n",
+       "      <td>-0.051483</td>\n",
+       "      <td>-0.086002</td>\n",
+       "      <td>0.357641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VIQ</th>\n",
+       "      <td>0.946639</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.778135</td>\n",
+       "      <td>-0.076088</td>\n",
+       "      <td>-0.071068</td>\n",
+       "      <td>0.337478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIQ</th>\n",
+       "      <td>0.934125</td>\n",
+       "      <td>0.778135</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.002512</td>\n",
+       "      <td>-0.076723</td>\n",
+       "      <td>0.386817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Weight</th>\n",
+       "      <td>-0.051483</td>\n",
+       "      <td>-0.076088</td>\n",
+       "      <td>0.002512</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.699614</td>\n",
+       "      <td>0.513378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Height</th>\n",
+       "      <td>-0.086002</td>\n",
+       "      <td>-0.071068</td>\n",
+       "      <td>-0.076723</td>\n",
+       "      <td>0.699614</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.601712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MRI_Count</th>\n",
+       "      <td>0.357641</td>\n",
+       "      <td>0.337478</td>\n",
+       "      <td>0.386817</td>\n",
+       "      <td>0.513378</td>\n",
+       "      <td>0.601712</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\n",
+       "FSIQ       1.000000  0.946639  0.934125 -0.051483 -0.086002   0.357641\n",
+       "VIQ        0.946639  1.000000  0.778135 -0.076088 -0.071068   0.337478\n",
+       "PIQ        0.934125  0.778135  1.000000  0.002512 -0.076723   0.386817\n",
+       "Weight    -0.051483 -0.076088  0.002512  1.000000  0.699614   0.513378\n",
+       "Height    -0.086002 -0.071068 -0.076723  0.699614  1.000000   0.601712\n",
+       "MRI_Count  0.357641  0.337478  0.386817  0.513378  0.601712   1.000000"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "brainFrame.?(method='pearson')"
+    "brainFrame[['FSIQ','VIQ','PIQ','Weight','Height','MRI_Count']].corr(method='pearson')"
    ]
   },
   {
@@ -286,7 +920,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(ответ)"
+    "(ответ)корреляция для столбцов \"FSIQ\"и \"FSIQ\", \"VIQ\"и \"VIQ\" т.д. по сути одинаковых столбцов равна единице"
    ]
   },
   {
@@ -300,7 +934,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(ответ)"
+    "(ответ)корреляция между условным столбцом M и N равна корреляции N и M, порядок столбцов не имеет значения"
    ]
   },
   {
@@ -312,11 +946,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "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>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FSIQ</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.955717</td>\n",
+       "      <td>0.939382</td>\n",
+       "      <td>0.038192</td>\n",
+       "      <td>-0.059011</td>\n",
+       "      <td>0.325697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VIQ</th>\n",
+       "      <td>0.955717</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.802652</td>\n",
+       "      <td>-0.021889</td>\n",
+       "      <td>-0.146453</td>\n",
+       "      <td>0.254933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIQ</th>\n",
+       "      <td>0.939382</td>\n",
+       "      <td>0.802652</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.113901</td>\n",
+       "      <td>-0.001242</td>\n",
+       "      <td>0.396157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Weight</th>\n",
+       "      <td>0.038192</td>\n",
+       "      <td>-0.021889</td>\n",
+       "      <td>0.113901</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.552357</td>\n",
+       "      <td>0.446271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Height</th>\n",
+       "      <td>-0.059011</td>\n",
+       "      <td>-0.146453</td>\n",
+       "      <td>-0.001242</td>\n",
+       "      <td>0.552357</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.174541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MRI_Count</th>\n",
+       "      <td>0.325697</td>\n",
+       "      <td>0.254933</td>\n",
+       "      <td>0.396157</td>\n",
+       "      <td>0.446271</td>\n",
+       "      <td>0.174541</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\n",
+       "FSIQ       1.000000  0.955717  0.939382  0.038192 -0.059011   0.325697\n",
+       "VIQ        0.955717  1.000000  0.802652 -0.021889 -0.146453   0.254933\n",
+       "PIQ        0.939382  0.802652  1.000000  0.113901 -0.001242   0.396157\n",
+       "Weight     0.038192 -0.021889  0.113901  1.000000  0.552357   0.446271\n",
+       "Height    -0.059011 -0.146453 -0.001242  0.552357  1.000000   0.174541\n",
+       "MRI_Count  0.325697  0.254933  0.396157  0.446271  0.174541   1.000000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "womenDf.?(method='pearson')"
+    "womenDf[['FSIQ','VIQ','PIQ','Weight','Height','MRI_Count']].corr(method='pearson')"
    ]
   },
   {
@@ -328,11 +1065,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "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>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FSIQ</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.944400</td>\n",
+       "      <td>0.930694</td>\n",
+       "      <td>-0.278140</td>\n",
+       "      <td>-0.356110</td>\n",
+       "      <td>0.498369</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VIQ</th>\n",
+       "      <td>0.944400</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.766021</td>\n",
+       "      <td>-0.350453</td>\n",
+       "      <td>-0.355588</td>\n",
+       "      <td>0.413105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIQ</th>\n",
+       "      <td>0.930694</td>\n",
+       "      <td>0.766021</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.156863</td>\n",
+       "      <td>-0.287676</td>\n",
+       "      <td>0.568237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Weight</th>\n",
+       "      <td>-0.278140</td>\n",
+       "      <td>-0.350453</td>\n",
+       "      <td>-0.156863</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.406542</td>\n",
+       "      <td>-0.076875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Height</th>\n",
+       "      <td>-0.356110</td>\n",
+       "      <td>-0.355588</td>\n",
+       "      <td>-0.287676</td>\n",
+       "      <td>0.406542</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.301543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MRI_Count</th>\n",
+       "      <td>0.498369</td>\n",
+       "      <td>0.413105</td>\n",
+       "      <td>0.568237</td>\n",
+       "      <td>-0.076875</td>\n",
+       "      <td>0.301543</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               FSIQ       VIQ       PIQ    Weight    Height  MRI_Count\n",
+       "FSIQ       1.000000  0.944400  0.930694 -0.278140 -0.356110   0.498369\n",
+       "VIQ        0.944400  1.000000  0.766021 -0.350453 -0.355588   0.413105\n",
+       "PIQ        0.930694  0.766021  1.000000 -0.156863 -0.287676   0.568237\n",
+       "Weight    -0.278140 -0.350453 -0.156863  1.000000  0.406542  -0.076875\n",
+       "Height    -0.356110 -0.355588 -0.287676  0.406542  1.000000   0.301543\n",
+       "MRI_Count  0.498369  0.413105  0.568237 -0.076875  0.301543   1.000000"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Используйте corr() для расчёта критерия корреляции Пирсона для кадра данных с мужчинами\n"
+    "# Используйте corr() для расчёта критерия корреляции Пирсона для кадра данных с мужчинами\n",
+    "menDf[['FSIQ','VIQ','PIQ','Weight','Height','MRI_Count']].corr(method='pearson')"
    ]
   },
   {
@@ -356,9 +1197,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting seaborn\n",
+      "  Obtaining dependency information for seaborn from https://files.pythonhosted.org/packages/7b/e5/83fcd7e9db036c179e0352bfcd20f81d728197a16f883e7b90307a88e65e/seaborn-0.13.0-py3-none-any.whl.metadata\n",
+      "  Downloading seaborn-0.13.0-py3-none-any.whl.metadata (5.3 kB)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (1.26.1)\n",
+      "Requirement already satisfied: pandas>=1.2 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (2.1.4)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (3.8.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.45.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.2)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.1.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.2->seaborn) (2023.3)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\kosty\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n",
+      "Downloading seaborn-0.13.0-py3-none-any.whl (294 kB)\n",
+      "   ---------------------------------------- 0.0/294.6 kB ? eta -:--:--\n",
+      "   - -------------------------------------- 10.2/294.6 kB ? eta -:--:--\n",
+      "   ---- ---------------------------------- 30.7/294.6 kB 435.7 kB/s eta 0:00:01\n",
+      "   --------- ----------------------------- 71.7/294.6 kB 653.6 kB/s eta 0:00:01\n",
+      "   -------------------------- ------------- 194.6/294.6 kB 1.3 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 294.6/294.6 kB 1.5 MB/s eta 0:00:00\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.13.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.2.1 -> 23.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
    "source": [
     "# Ячейка для кода № 11\n",
     "!pip install seaborn"
@@ -379,14 +1262,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Ячейка для кода № 12\n",
     "import seaborn as sns\n",
     "\n",
-    "wcorr = womenDf.corr()\n",
+    "wcorr = womenDf[['FSIQ','VIQ','PIQ','Weight','Height','MRI_Count']].corr()\n",
     "sns.heatmap(wcorr)\n",
     "#plt.savefig('attribute_correlations.png', tight_layout=True)"
    ]
@@ -400,12 +1304,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAGiCAYAAABgTyUPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABEn0lEQVR4nO3deXxM9/4/8NckMpOFhBRJaIgtlNquJYSKEI3WpW4XoZYIRVxBRbTi2ou0tVbRKCW0lBZVe5umidq1fIPatzS3SFAlEkySmc/vDz9zzWRy5EzOZMZ4PT3O42E+5zOfeZ8jknc+21EJIQSIiIiISsjJ1gEQERHR04XJAxEREcnC5IGIiIhkYfJAREREsjB5ICIiIlmYPBAREZEsTB6IiIhIFiYPREREJAuTByIiIpKFyQMRERHJwuSBiIjITvzyyy/o3r07qlWrBpVKhc2bNz/xPWlpafjHP/4BjUaDunXrIikpyepxMnkgIiKyE3l5eWjatCkWL15covqXL19Gt27dEBoaivT0dLz77rt455138MMPP1g1ThUfjEVERGR/VCoVvvvuO/Ts2bPYOu+//z62b9+O33//3VDWu3dv3L59G7t27bJabOx5ICIisiKtVoucnByjQ6vVKtL2gQMHEBYWZlQWHh6OAwcOKNJ+ccpZtXUZCm5esnUIdkF/57qtQ7APBcr8x3ra1QgaZusQ7MJf9+/aOgS7cP2VurYOwW54f7/bqu0r+TMpYdFqTJs2zahsypQpmDp1aqnbzsrKgo+Pj1GZj48PcnJycP/+fbi5uZX6M8yxm+SBiIjIbuh1ijUVHx+P2NhYozKNRqNY+7bA5IGIiMiKNBqN1ZIFX19fZGdnG5VlZ2fD09PTar0OAJMHIiKiooTe1hGUSNu2bbFjxw6jsuTkZLRt29aqn8sJk0RERKb0euUOGXJzc5Geno709HQAD5dipqenIzMzE8DDIZABAwYY6kdHR+PSpUt47733cObMGSxZsgTffPMNxowZo9itMIc9D0RERCaEjXoefvvtN4SGhhpeP5orERkZiaSkJFy7ds2QSABArVq1sH37dowZMwaffPIJnn/+eSxfvhzh4eFWjdNu9nngaouHuNri/+NqCwBcbfEIV1s8xNUW/2Pt1Rb5V08q1pa6WiPF2rIX7HkgIiIyJXO44VnD5IGIiMjUUzJh0lY4YZKIiIhkYc8DERGRKQU3iXJETB6IiIhMcdhCEoctiIiISBb2PBAREZniagtJTB6IiIhM2GqTqKcFhy2IiIhIFvY8EBERmeKwhSQmD0RERKY4bCGJyQMREZEp7vMgyeLk4cqVK9i4cSPOnTsHtVqN+vXro1evXqhUqZKS8REREZGdsSh5WLJkCWJjY5Gfnw9PT08AQE5ODmJjY7F8+XL06dMHQgikp6ejefPmigZMRERkdRy2kCR7tcX27dsxatQoxMTE4MqVK7h9+zZu376NK1euYNiwYYiMjMTevXvRt29fbN261RoxExERWZder9zhgGT3PMyePRvjx4/HjBkzjMr9/Pwwb948uLu7o0uXLvD19UVCQoJigRIREZF9kN3zcPToUfTv37/Y8/3794dWq8Xu3btRs2bNUgVHRERkE0Kv3OGAZPc86HQ6uLi4FHvexcUFbm5uqFGjRqkCIyIishkHHW5Qiuyeh0aNGuH7778v9vzmzZvRqFGjUgVFRERE9kt2z8OIESMwfPhwaDQaDB06FOXKPWyisLAQS5cuxcSJE7FkyRLFAyUiIiorQnCfBymyk4fIyEicOHECMTExiI+PR506dSCEwKVLl5Cbm4tRo0Zh4MCBVgiViIiojDjoXAWlWLTPw5w5c/Dmm2/i66+/xvnz5wEAHTp0QJ8+fdCmTRtFAyQiIiL7YvEOk23atLE4UdBqtdBqtUZlTlotNBqNpeEQEREphxMmJclOHo4fP16iek2aNCn2XEJCAqZNm2ZUNnHcKEx+b7TccIiIiJTHYQtJspOHZs2aQaVSQQhRbB2VSgWdrvjJJvHx8YiNjTUqc7p7RW4oRERE1sEHY0mSnTxcvny51B+q0WiKDFEU5N8sdbtERERkfbKTB+4aSUREDo/DFpJkbxJ18+ZN/PHHH0ZlJ0+eRFRUFHr16oW1a9cqFhwREZFN8MFYkmQnDyNHjsTChQsNr69fv46XXnoJv/76K7RaLQYOHIgvv/xS0SCJiIjIfshOHg4ePIgePXoYXq9evRre3t5IT0/H999/j1mzZmHx4sWKBklERFSm+GAsSbKTh6ysLAQEBBhe//zzz3j99dcN21T36NHDsHEUERHRU4nDFpJkJw+enp64ffu24fXhw4cRFBRkeK1SqYpsAEVERESOQ3by0KZNGyxcuBB6vR4bNmzA3bt30alTJ8P5c+fOwd/fX9EgiYiIyhR7HiTJXqo5ffp0hIWF4auvvkJhYSHi4+NRqVIlw/l169YhJCRE0SCJiIjKEp+qKU128lChQgWcPHkSBw4cgK+vr9GQBQD07t0bDRs2VCxAIiIisi+yhy3q1asHAHjttdcQFBSEiIgIZGdnG85369YNtWrVUi5CIiKissZhC0mykwfTZ1rs2LEDeXl5igVERERkc1yqKcniR3ITERE5LAftMVCK7J4HlUoFlUpVpIyIiIieDbJ7HoQQGDhwoOGpmA8ePEB0dDQ8PDyM6m3atEmZCImIiMqagw43KEV28hAZGWn0ul+/fooFQ0REZBc4bCFJdvKwcuVKa8RBRERETwlOmCQiIjLFYQtJTB6IiIhMcdhCkuzVFkRERGQ9ixcvRkBAAFxdXREUFITDhw9L1l+wYAHq168PNzc3+Pv7Y8yYMXjw4IFVY2TPAxERkSkb9TysX78esbGxSExMRFBQEBYsWIDw8HCcPXsWVatWLVJ/7dq1GD9+PFasWIHg4GCcO3cOAwcOhEqlwrx586wWJ3seiIiITCm4w6RWq0VOTo7RodVqzX7svHnzMGTIEERFRaFhw4ZITEyEu7s7VqxYYbb+/v370a5dO7z99tsICAjAyy+/jD59+jyxt6K0mDwQERFZUUJCAry8vIyOhISEIvXy8/Nx5MgRhIWFGcqcnJwQFhaGAwcOmG07ODgYR44cMSQLly5dwo4dO/Dqq69a52L+Pw5bEBERmVJw2CI+Ph6xsbFGZY82WnzczZs3odPp4OPjY1Tu4+ODM2fOmG377bffxs2bN9G+fXsIIVBYWIjo6GhMmDBBsfjNYc8DERGRKQWHLTQaDTw9PY0Oc8mDJdLS0jBr1iwsWbIER48exaZNm7B9+3Z88MEHirRfHPY8EBERmbLBhMnKlSvD2dkZ2dnZRuXZ2dnw9fU1+55Jkyahf//+eOeddwAAjRs3Rl5eHoYOHYr//Oc/cHKyTh8Bex6IiIjsgFqtRosWLZCSkmIo0+v1SElJQdu2bc2+5969e0USBGdnZwAPn0VlLex5ICIiMmWjHSZjY2MRGRmJli1bonXr1liwYAHy8vIQFRUFABgwYACqV69umHDZvXt3zJs3D82bN0dQUBAuXLiASZMmoXv37oYkwhqYPBAREZmy0T4PERERuHHjBiZPnoysrCw0a9YMu3btMkyizMzMNOppmDhxIlQqFSZOnIgrV66gSpUq6N69O2bOnGnVOFXCmv0aMmgvHrR1CHbByavoJiDPosKUL20dgl3w7P+5rUOwC3bxTcoOXHzxBVuHYDdqHv3Jqu3f3zBDsbbc3pyoWFv2gj0PREREpvhsC0lMHoiIiEzZR6e83eJqCyIiIpKFPQ9ERESmOGwhickDERGRKSYPkjhsQURERLKw54GIiMiUjTaJeloweSAiIjLFYQtJTB6IiIhMcammJM55ICIiIlnY80BERGSKwxaSmDwQERGZYvIgicMWREREJAt7HoiIiExxqaYkJg9EREQmhJ6rLaRw2IKIiIhkYc8DERGRKU6YlMTkgYiIyBTnPEjisAURERHJwp4HIiIiU5wwKYnJAxERkSnOeZBkUfJw5coVbNy4EefOnYNarUb9+vXRq1cvVKpUSen4iIiIyh6TB0myk4clS5YgNjYW+fn58PT0BADk5OQgNjYWy5cvR58+fSCEQHp6Opo3b654wERERGRbsiZMbt++HaNGjUJMTAyuXLmC27dv4/bt27hy5QqGDRuGyMhI7N27F3379sXWrVutFTMREZF1CaHc4YBk9TzMnj0b48ePx4wZM4zK/fz8MG/ePLi7u6NLly7w9fVFQkKCooESERGVGQ5bSJLV83D06FH079+/2PP9+/eHVqvF7t27UbNmzVIHR0RERPZHVs+DTqeDi4tLseddXFzg5uaGGjVqlDowIiIim+FSTUmyeh4aNWqE77//vtjzmzdvRqNGjUodFBERkU0JvXKHA5LV8zBixAgMHz4cGo0GQ4cORblyD99eWFiIpUuXYuLEiViyZIlVAiUiIiL7ICt5iIyMxIkTJxATE4P4+HjUqVMHQghcunQJubm5GDVqFAYOHPjEdrRaLbRarUlhPjQatZxwiIiIrIPDFpJkP9tizpw52L9/PwYOHAhfX1/4+flh4MCB2LdvH+bPn1+iNhISEuDl5WV0fJy4WnbwRERE1iD0esUOR2TRDpNt2rRBmzZtLP7Q+Ph4xMbGGhf+mW5xe0RERFR2ZCUPx48fL1G9Jk2aSJ7XaDTQaDRGZVoOWRARkb3gsIUkWclDs2bNnlhHpVJBp9NZGg8REZHtOegqCaXISh6OHTtmeJ4FERGRw2LPgyRZyUPTpk3RunVrDB48GL1790aFChWsFRcRERHZKVmrLXbv3o2GDRti7Nix8PPzQ2RkJPbs2WOt2IiIiGxDr1fucECykoeXXnoJK1aswLVr1/Dpp58iIyMDISEhCAwMxEcffYSsrCxrxUlERFR29EK5wwHJ3ucBADw8PBAVFYXdu3fj3LlzeOutt7B48WLUqFEDPXr0UDpGIiIisiMW7fPwuLp162LChAmoWbMm4uPjsX37diXiIiIish2utpBUquThl19+wYoVK7Bx40Y4OTmhV69eGDx4sFKxERER2YaDDjcoRXbycPXqVSQlJSEpKQkXLlxAcHAwFi5ciF69esHDw8MaMRIREZEdkZU8vPLKK/jpp59QuXJlDBgwAIMGDUL9+vWtFRsREZFNOOozKZQia8Kki4sLNmzYgD///BMfffQREwciInJMNlxtsXjxYgQEBMDV1RVBQUE4fPiwZP3bt29jxIgR8PPzg0ajQWBgIHbs2GHplZeIrJ6HLVu2WCsOIiKiZ9769esRGxuLxMREBAUFYcGCBQgPD8fZs2dRtWrVIvXz8/PRpUsXVK1aFRs2bED16tXxxx9/oGLFilaNs9SrLYiIiByOjSZMzps3D0OGDEFUVBQAIDExEdu3b8eKFSswfvz4IvVXrFiBW7duYf/+/XBxcQEABAQEWD1Oi/Z5ICIicmhCr9ih1WqRk5NjdGi12iIfmZ+fjyNHjiAsLMxQ5uTkhLCwMBw4cMBsmFu2bEHbtm0xYsQI+Pj44MUXX8SsWbOs/oBKJg9ERESmFJzzkJCQAC8vL6MjISGhyEfevHkTOp0OPj4+RuU+Pj7F7uB86dIlbNiwATqdDjt27MCkSZMwd+5czJgxwyq35REOWxAREVlRfHw8YmNjjco0Go0ibev1elStWhWff/45nJ2d0aJFC1y5cgWzZ8/GlClTFPkMc5g8EBERmRAKznnQaDQlShYqV64MZ2dnZGdnG5VnZ2fD19fX7Hv8/Pzg4uICZ2dnQ9kLL7yArKws5OfnQ61Wly74YnDYgoiIyJQNlmqq1Wq0aNECKSkp/wtDr0dKSgratm1r9j3t2rXDhQsXoH9sX4pz587Bz8/PaokDwOSBiIjIbsTGxmLZsmVYtWoVTp8+jeHDhyMvL8+w+mLAgAGIj4831B8+fDhu3bqF0aNH49y5c9i+fTtmzZqFESNGWDVODlsQERGZstEOkxEREbhx4wYmT56MrKwsNGvWDLt27TJMoszMzIST0/9+7/f398cPP/yAMWPGoEmTJqhevTpGjx6N999/36pxqoQQdvH0D+3Fg7YOwS44eRXdBORZVJjypa1DsAue/T+3dQh2wS6+SdmBiy++YOsQ7EbNoz9Ztf27/35FsbYqLNmpWFv2gsMWREREJAuHLYiIiEzxkdySmDwQERGZsJMRfbvFYQsiIiKShT0PREREpjhsIYnJAxERkSkmD5KYPBAREZlQcntqR2Q/yUNB0ceTPou4v8FD5Tr3t3UIdqFF5VRbh2AXLuRetXUIdsG7tf18y6ZnG78SiYiITLHnQRKTByIiIlO22Z36qcGlmkRERCQLex6IiIhMcMKkNCYPREREppg8SOKwBREREcnCngciIiJTnDApickDERGRCc55kMZhCyIiIpKFPQ9ERESmOGwhickDERGRCQ5bSGPyQEREZIo9D5I454GIiIhkYc8DERGRCcGeB0lMHoiIiEwxeZDEYQsiIiKShT0PREREJjhsIY3JAxERkSkmD5I4bEFERESysOeBiIjIBIctpDF5ICIiMsHkQRqTByIiIhNMHqRZnDzcv38fycnJOHfuHNRqNQIDA9GlSxc4OzsrGR8RERHZGYuShy1btuCdd97BzZs3jcqrV6+ONWvWoEOHDgCAy5cvo1atWqWPkoiIqCwJla0jsGuyV1vs378fb775Jjp06IB9+/bh1q1buHXrFvbu3YvWrVsjPDwcZ86cwfvvv48vv/zSGjETERFZldArdzgi2T0PM2bMQFRUFJYuXWpUHhwcjODgYAwbNgwvvfQShBBISUlRLFAiIiKyD7J7Hg4ePIiYmJhiz48YMQJ//fUXfvrpJzRt2rRUwREREdmC0KsUOxyR7J6H+/fvw9PTs9jzXl5e0Gg0aNasWWniIiIishlHHW5Qiuyeh3r16uHnn38u9nxKSgrq1atXqqCIiIjIfsnueYiKikJcXBx8fHzw6quvGp3bvn073nvvPUyYMEGyDa1WC61Wa1yYnw+NWi03HCIiIsUJrraQJLvnYfTo0ejUqRP++c9/4oUXXsDrr7+Of/3rX2jQoAF69OiBDh064N1335VsIyEhAV5eXkbHx5+vsfQaiIiIFMXVFtJkJw9OTk749ttv8fXXX6N+/fo4c+YMzp49i/r162PNmjXYtGkTnJykm42Pj8edO3eMjveG9rX4IoiIiKjsWLzDZEREBCIiIix6r0ajgUajMSrTcsiCiIjshKOuklCK7OQhJyenRPWkVmQQERHZMyFsHYF9k508VKxYESpV8RmZEAIqlQo6na5UgREREdkKex6kyU4eUlNTrREHERERPSVkT5h86aWXcPDgQUyYMAFxcXHYuXMnWrdujZCQEKODiIjoaWXLHSYXL16MgIAAuLq6IigoCIcPHy7R+9atWweVSoWePXvK/ky5ZCcPM2fOxIQJE1C+fHlUr14dn3zyCUaMGGGN2IiIiGxCCOUOOdavX4/Y2FhMmTIFR48eRdOmTREeHo7r169Lvi8jIwNxcXF46aWXSnHVJSc7eVi9ejWWLFmCH374AZs3b8bWrVuxZs0a6PUOupiViIioFLRaLXJycoyOIhsl/n/z5s3DkCFDEBUVhYYNGyIxMRHu7u5YsWJFse3rdDr07dsX06ZNQ+3ata11GUZkJw+ZmZlGO0uGhYVBpVLh6tWrigZGRERkK0oOW5jbGDEhIaHIZ+bn5+PIkSMICwszlDk5OSEsLAwHDhwoNtbp06ejatWqGDx4sFXuhTmyJ0wWFhbC1dXVqMzFxQUFBQWKBUVERGRLSm5PHR8fj9jYWKMy072OAODmzZvQ6XTw8fExKvfx8cGZM2fMtr1371588cUXSE9PVyzekpCdPAghMHDgQKMLf/DgAaKjo+Hh4WEo27RpkzIREhERPcXMbYyohLt376J///5YtmwZKleurHj7UmQnD5GRkUXK+vXrp0gwRERE9sAWz6SoXLkynJ2dkZ2dbVSenZ0NX1/fIvUvXryIjIwMdO/e3VD2aP5huXLlcPbsWdSpU8cqscpOHlauXGmNOIiIiOyG3gZP1VSr1WjRogVSUlIMyy31ej1SUlIQExNTpH6DBg1w4sQJo7KJEyfi7t27+OSTT+Dv72+1WC1+tgUREREpKzY2FpGRkWjZsiVat26NBQsWIC8vD1FRUQCAAQMGoHr16khISICrqytefPFFo/dXrFgRAIqUK43JAxERkQklJ0zKERERgRs3bmDy5MnIyspCs2bNsGvXLsMkyszMzCc+ubosMHkgIiIyYctnW8TExJgdpgCAtLQ0yfcmJSUpH5AZTB6IiIhM8Kma0mzf90FERERPFfY8EBERmeAjuaUxeSAiIjJhi6WaTxMOWxAREZEs7HkgIiIyYaulmk8LJg9EREQmuNpCGoctiIiISBb2PBAREZnghElpTB6IiIhMcM6DNA5bEBERkSzseSAiIjLBCZPSmDwQERGZ4JwHaXaTPNQIGmbrEOzCrft3bR2CXWhROdXWIdiFPcdX2DoEu/BnZ35/AIB+O+zmW7bNfW/l9jnnQRrnPBAREZEsTGOJiIhMcNhCGpMHIiIiE5wvKY3DFkRERCQLex6IiIhMcNhCGpMHIiIiE1xtIY3DFkRERCQLex6IiIhM6G0dgJ2T3fOQmZkJYWbfTiEEMjMzFQmKiIjIlgRUih2OSHbyUKtWLdy4caNI+a1bt1CrVi1FgiIiIiL7JXvYQggBlapoJpWbmwtXV1dFgiIiIrIlPTd6kFTi5CE2NhYAoFKpMGnSJLi7uxvO6XQ6HDp0CM2aNVM8QCIiorKmd9DhBqWUOHn4v//7PwAPex5OnDgBtVptOKdWq9G0aVPExcUpHyEREVEZc9S5CkopcfKQmvrwKYdRUVH45JNP4OnpabWgiIiIyH7JnvOwcuVKa8RBRERkN7hUU5rs5CEvLw8ffvghUlJScP36dej1xrf40qVLigVHRERkCxy2kCY7eXjnnXewe/du9O/fH35+fmZXXhAREZHjkp087Ny5E9u3b0e7du2sEQ8REZHNcdhCmuzkoVKlSvD29rZGLERERHaByYM02TtMfvDBB5g8eTLu3btnjXiIiIjIzpWo56F58+ZGcxsuXLgAHx8fBAQEwMXFxaju0aNHlY2QiIiojHHCpLQSJQ89e/a0chhERET2Q8/cQVKJkocpU6ZYOw4iIiJ6SsieMElEROTo+GwLaRattjC3t4NKpYKrqyvq1q2LgQMHIioqSpEAiYiIyhofqilNdvIwefJkzJw5E6+88gpat24NADh8+DB27dqFESNG4PLlyxg+fDgKCwsxZMgQxQMmIiKyNi7VlCY7edi7dy9mzJiB6Ohoo/KlS5fixx9/xMaNG9GkSRMsXLiQyQMREZEDkr3Pww8//ICwsLAi5Z07d8YPP/wAAHj11Vf5jAsiInpq6VUqxQ5HJDt58Pb2xtatW4uUb9261bDzZF5eHipUqFD66IiIiGxAKHg4ItnDFpMmTcLw4cORmppqmPPw66+/YseOHUhMTAQAJCcnIyQkpNg2tFottFqtUZkQeqhUsnMZIiIih7J48WLMnj0bWVlZaNq0KT799FPDz1tTy5Ytw+rVq/H7778DAFq0aIFZs2YVW18psn9aDxkyBLt374aHhwc2bdqETZs2wd3dHbt378bgwYMBAGPHjsX69euLbSMhIQFeXl5GR572luVXQUREpCC9gocc69evR2xsLKZMmYKjR4+iadOmCA8Px/Xr183WT0tLQ58+fZCamooDBw7A398fL7/8Mq5cuSL3kmVRCSHKvFfFXM9D3edbsucBwK37d20dgl1oUbmerUOwC3uOr7B1CHbhz87DbB2CXXj3b42tQ7Ab32dus2r7X1frq1hbfa6uKXHdoKAgtGrVCosWLQIA6PV6+Pv7Y+TIkRg/fvwT36/T6VCpUiUsWrQIAwYMsDjmJynRsEVOTg48PT0Nf5fyqJ4UjUYDjcb4PwETByIickTmfmE293MwPz8fR44cQXx8vKHMyckJYWFhOHDgQIk+6969eygoKLD6069L9BO7UqVKhi6TihUrolKlSkWOR+VERERPOz1Uih3mhuoTEhKKfObNmzeh0+ng4+NjVO7j44OsrKwSxf3++++jWrVqZldFKqlEPQ8///yzIYtJTU21akBERES2puR4fnx8PGJjY43KTHsdlPDhhx9i3bp1SEtLg6urq+LtP65EycPjKyekVlEQERGRMXNDFOZUrlwZzs7OyM7ONirPzs6Gr6+v5HvnzJmDDz/8ED/99BOaNGlSqnhLwqKJBnv27EG/fv0QHBxsmNH55ZdfYu/evYoGR0REZAt6lXJHSanVarRo0QIpKSn/i0OvR0pKCtq2bVvs+z7++GN88MEH2LVrF1q2bFmayy4x2cnDxo0bER4eDjc3Nxw9etQwCeTOnTuYNWuW4gESERGVNVst1YyNjcWyZcuwatUqnD59GsOHD0deXp7hYZMDBgwwmlD50UcfYdKkSVixYgUCAgKQlZWFrKws5ObmWnztJSE7eZgxYwYSExOxbNkyuLi4GMrbtWuHo0ePKhocERGRLdhqh8mIiAjMmTMHkydPRrNmzZCeno5du3YZJlFmZmbi2rVrhvqfffYZ8vPz8eabb8LPz89wzJkzx+JrLwnZO0yePXsWHTp0KFLu5eWF27dvKxETERHRMysmJgYxMTFmz6WlpRm9zsjIsH5AZsjuefD19cWFCxeKlO/duxe1a9dWJCgiIiJbssWch6eJRdtTjx49GocOHYJKpcLVq1exZs0axMXFYfjw4daIkYiIqEzZas7D06LEwxaXL19GrVq1MH78eOj1enTu3Bn37t1Dhw4doNFoEBcXh5EjR1ozViIiIrIDJU4e6tSpg5o1ayI0NBShoaE4ffo07t69i9zcXDRs2BDly5e3ZpxERERlxlF7DJRS4uTh559/RlpaGtLS0vD1118jPz8ftWvXRqdOndCpUyd07NixyJaaRERETyPhoHMVlFLi5KFjx47o2LEjAODBgwfYv3+/IZlYtWoVCgoK0KBBA5w8edJasRIREZEdkL1UEwBcXV3RqVMntG/fHqGhodi5cyeWLl2KM2fOKB0fERFRmeOwhTRZyUN+fj4OHjyI1NRUpKWl4dChQ/D390eHDh2waNEiPveCiIgcApMHaSVOHjp16oRDhw6hVq1aCAkJwbBhw7B27Vr4+flZMz4iIiKyMyVOHvbs2QM/Pz/D5MiQkBA899xz1oyNiIjIJpR8JLcjKvEmUbdv38bnn38Od3d3fPTRR6hWrRoaN26MmJgYbNiwATdu3LBmnERERGWGO0xKK3HPg4eHB7p27YquXbsCAO7evYu9e/ciNTUVH3/8Mfr27Yt69erh999/t1qwREREZYFzHqTJ3p76EQ8PD3h7e8Pb2xuVKlVCuXLlcPr0aSVjIyIiIjtU4p4HvV6P3377DWlpaUhNTcW+ffuQl5eH6tWrIzQ0FIsXL0ZoaKg1YyUiIioT7HmQVuLkoWLFisjLy4Ovry9CQ0Mxf/58dOzYEXXq1LFmfERERGWOEyallTh5mD17NkJDQxEYGGjNeIiIiMjOlTh5GDZsmDXjICIishuOukpCKRZtT01EROTIOOdBmsWrLYiIiOjZxJ4HIiIiE5wwKY3JAxERkQk90wdJdpM8/HX/rq1DIDtyIfeqrUOwC3925kRlAHg+ZamtQ7AL7zceZ+sQiADYUfJARERkLzhhUhqTByIiIhMctJDG5IGIiMgEex6kcakmERERycKeByIiIhPcYVIakwciIiITXKopjcMWREREJAt7HoiIiEyw30EakwciIiITXG0hjcMWREREJAt7HoiIiExwwqQ0i3oepk+fjnv37hUpv3//PqZPn17qoIiIiGxJKHg4IouSh2nTpiE3N7dI+b179zBt2rRSB0VERET2y6JhCyEEVKqiO2gcO3YM3t7epQ6KiIjIljhhUpqs5KFSpUpQqVRQqVQIDAw0SiB0Oh1yc3MRHR2teJBERERliXMepMlKHhYsWAAhBAYNGoRp06bBy8vLcE6tViMgIABt27ZVPEgiIqKyxNRBmqzkITIyEgBQq1YtBAcHw8XFxSpBERERkf2yaM5DSEgI9Ho9zp07h+vXr0OvNx4d6tChgyLBERER2QLnPEizKHk4ePAg3n77bfzxxx8QwrhzR6VSQafTKRIcERGRLQgOXEiyKHmIjo5Gy5YtsX37dvj5+ZldeUFERESOyaLk4fz589iwYQPq1q2rdDxEREQ2x2ELaRZtEhUUFIQLFy4oHQsREZFd0EModjiiEicPx48fNxwjR47E2LFjkZSUhCNHjhidO378uDXjJSIicmiLFy9GQEAAXF1dERQUhMOHD0vW//bbb9GgQQO4urqicePG2LFjh9VjLPGwRbNmzaBSqYwmSA4aNMjw90fnOGGSiIiedrbqL1i/fj1iY2ORmJiIoKAgLFiwAOHh4Th79iyqVq1apP7+/fvRp08fJCQk4J///CfWrl2Lnj174ujRo3jxxRetFqdKmC6XKMYff/xR4kZr1qwpO5By6uqy30OOq6Krh61DsAu/1pL/f8kRPZ+y1NYh2IVfG4+zdQh2I/jaRqu2PyzgLcXaWnj2K2i1WqMyjUYDjUZTpG5QUBBatWqFRYsWAQD0ej38/f0xcuRIjB8/vkj9iIgI5OXlYdu2bYayNm3aoFmzZkhMTFTsGkyVeNiiZs2aJT6IiIjooYSEBHh5eRkdCQkJRerl5+fjyJEjCAsLM5Q5OTkhLCwMBw4cMNv2gQMHjOoDQHh4eLH1lWLRaostW7aYLVepVHB1dUXdunVRq1atUgVGRERkK0qutoiPj0dsbKxRmbleh5s3b0Kn08HHx8eo3MfHB2fOnDHbdlZWltn6WVlZpYxamkXJQ8+ePYvMfwCM5z20b98emzdvRqVKlRQJlIiIqKwouUlUcUMUTzOLlmomJyejVatWSE5Oxp07d3Dnzh0kJycjKCgI27Ztwy+//IK//voLcXFxSsdLRERkdXoFj5KqXLkynJ2dkZ2dbVSenZ0NX19fs+/x9fWVVV8pFiUPo0ePxrx589C5c2dUqFABFSpUQOfOnTF79myMGzcO7dq1w4IFC5CcnGz2/VqtFjk5OUZHCedtEhEROSS1Wo0WLVogJSXFUKbX65GSklLsE6vbtm1rVB94+Au+tZ9wbVHycPHiRXh6ehYp9/T0xKVLlwAA9erVw82bN82+39zkEaG/a0koREREihMK/pEjNjYWy5Ytw6pVq3D69GkMHz4ceXl5iIqKAgAMGDAA8fHxhvqjR4/Grl27MHfuXJw5cwZTp07Fb7/9hpiYGEXvhymLkocWLVpg3LhxuHHjhqHsxo0beO+999CqVSsAD7ew9vf3N/v++Ph4w3DHo0PlVMGSUIiIiBRni2EL4OHSyzlz5mDy5Mlo1qwZ0tPTsWvXLsOkyMzMTFy7ds1QPzg4GGvXrsXnn3+Opk2bYsOGDdi8ebNV93gAZOzz8LizZ8/itddew+XLlw0Jwn//+1/Url0b33//PQIDA7F582bcvXsX/fv3L1Gb3OeBHsd9Hh7iPg8PcZ+Hh7jPw/9Ye5+HyIA3FGtrVYZ1Y7UFi1Zb1K9fH6dOncKPP/6Ic+fOGcq6dOkCJ6eHnRk9e/ZULEgiIqKypOc8PEkWJQ/Aw40runbtiq5duyoZDxERkc0xdZBW4uRh4cKFGDp0KFxdXbFw4ULJuqNGjSp1YERERGSfSpw8zJ8/H3379oWrqyvmz59fbD2VSsXkgYiInmqO+ihtpZQ4ebh8+bLZvxMRETkaJXeYdEQWLdV8JD8/H2fPnkVhYaFS8RAREZGdsyh5uHfvHgYPHgx3d3c0atQImZmZAICRI0fiww8/VDRAIiKismarfR6eFhYlD/Hx8Th27BjS0tLg6upqKA8LC8P69esVC46IiMgW9BCKHY7IoqWamzdvxvr169GmTRuoVCpDeaNGjXDx4kXFgiMiIrIFznmQZlHPw40bN1C1atUi5Xl5eUbJBBERETkei5KHli1bYvv27YbXjxKG5cuXW/1JXkRERNbGOQ/SLBq2mDVrFl555RWcOnUKhYWF+OSTT3Dq1Cns378fu3fvVjpGIiKiMmXBY5+eKRb1PLRv3x7p6ekoLCxE48aN8eOPP6Jq1ao4cOAAWrRooXSMREREZEdk9Tzk5OQY/l6lShXMnTvXbB1PT8/SR0ZERGQjjrpKQimykoeKFStKTogUQkClUkGn05U6MCIiIltx1LkKSpGVPKSmphr+LoTAq6++iuXLl6N69eqKB0ZERET2SVbyEBISYvTa2dkZbdq0Qe3atRUNioiIyJa4z4M0i1ZbEBEROTLOeZBWqgdjERER0bOn1D0P3FGSiIgcDfd5kCYreXj99deNXj948ADR0dHw8PAwKt+0aVPpIyMiIrIRrraQJit58PLyMnrdr18/RYMhIiKyB5wwKU1W8rBy5UprxUFERERPCa62ICIiMsHVFtKYPBAREZnghElpFi3VnD59Ou7du1ek/P79+5g+fXqpgyIiIiL7ZVHyMG3aNOTm5hYpv3fvHqZNm1bqoIiIiGxJD6HY4YgsGrZ49AAsU8eOHYO3t3epgyIiIrIlrraQJit5qFSpElQqFVQqFQIDA40SCJ1Oh9zcXERHR1sUyPVX6lr0Pkdz978utg7BLni35nQcAOi3g/cBAN5vPM7WIdiFVidm2zoEIgAyk4cFCxZACIFBgwZh2rRpRvs+qNVqBAQEoG3btooHSUREVJb0nDApSVbyEBkZCQCoVasWgoOD4eLC35KJiMjxMHWQZlGfaEhICPR6Pc6dO4fr169DrzfeyLNDhw6KBEdERET2x6Lk4eDBg3j77bfxxx9/FFkLq1KpoNPpFAmOiIjIFhx1lYRSLEoeoqOj0bJlS2zfvh1+fn58siYRETkUJg/SLEoezp8/jw0bNqBuXa6QICIix8MdJqVZtElUUFAQLly4oHQsRERE9BSwqOdh5MiRGDt2LLKystC4ceMiqy6aNGmiSHBERES2wGELaRYlD2+88QYAYNCgQYYylUpl2HmSEyaJiOhpxh0mpVmUPFy+fFnpOIiIiOgpYVHyULNmTaXjICIishucMCnNouRh9erVkucHDBhgUTBERET2gHMepFmUPIwePdrodUFBAe7duwe1Wg13d3cmD0RERA7MouTh77//LlJ2/vx5DB8+HOPG8el3RET0dOOwhTSL9nkwp169evjwww+L9EoQERE9bfQQih3WcuvWLfTt2xeenp6oWLEiBg8ejNzcXMn6I0eORP369eHm5oYaNWpg1KhRuHPnjuzPVix5AIBy5crh6tWrSjZJREREZvTt2xcnT55EcnIytm3bhl9++QVDhw4ttv7Vq1dx9epVzJkzB7///juSkpKwa9cuDB48WPZnWzRssWXLFqPXQghcu3YNixYtQrt27SxpkoiIyG7Y+z4Pp0+fxq5du/Drr7+iZcuWAIBPP/0Ur776KubMmYNq1aoVec+LL76IjRs3Gl7XqVMHM2fORL9+/VBYWIhy5UqeEliUPPTs2dPotUqlQpUqVdCpUyfMnTvXkiaJiIjshl7BOQ9arRZardaoTKPRQKPRWNzmgQMHULFiRUPiAABhYWFwcnLCoUOH8K9//atE7dy5cweenp6yEgfAwmELvV5vdOh0OmRlZWHt2rXw8/OzpEkiIiK7IRT8k5CQAC8vL6MjISGhVPFlZWWhatWqRmXlypWDt7c3srKyStTGzZs38cEHH0gOdRSn1HMehBCclUpERFSM+Ph43Llzx+iIj483W3f8+PFQqVSSx5kzZ0odU05ODrp164aGDRti6tSpst9v0bAF8HCjqNmzZ+P8+fMAgMDAQIwbNw79+/e3tEkiIiK7oOSwhZwhirFjx2LgwIGSdWrXrg1fX19cv37dqLywsBC3bt2Cr6+v5Pvv3r2Lrl27okKFCvjuu++KPNyyJCxKHubNm4dJkyYhJibGMEFy7969iI6Oxs2bNzFmzBhLmiUiIrILtpowWaVKFVSpUuWJ9dq2bYvbt2/jyJEjaNGiBQDg559/hl6vR1BQULHvy8nJQXh4ODQaDbZs2QJXV1eL4rQoefj000/x2WefGe0k2aNHDzRq1AhTp05l8kBERGRFL7zwArp27YohQ4YgMTERBQUFiImJQe/evQ0rLa5cuYLOnTtj9erVaN26NXJycvDyyy/j3r17+Oqrr5CTk4OcnBwAD5MWZ2fnEn++RcnDtWvXEBwcXKQ8ODgY165ds6RJIiIiu6HksIW1rFmzBjExMejcuTOcnJzwxhtvYOHChYbzBQUFOHv2LO7duwcAOHr0KA4dOgQAqFu3rlFbly9fRkBAQIk/26LkoW7duvjmm28wYcIEo/L169ejXr16ljRJRERkN+x9nwcA8Pb2xtq1a4s9HxAQYLSgoWPHjootcLAoeZg2bRoiIiLwyy+/GOY87Nu3DykpKfjmm28UCYyIiIjsk0XJwxtvvIFDhw5h/vz52Lx5M4CH4y+HDx9G8+bNlYyPiIiozD0Nwxa2ZPFSzRYtWuCrr75SMhYiIiK78DQMW9iSrE2irl69iri4OMPszMfduXMH48aNQ3Z29hPb0Wq1hlmejw6tTi8nFCIiIrIRWcnDvHnzkJOTA09PzyLnvLy8cPfuXcybN++J7ZjbqnPB+Uw5oRAREVmNEHrFDkckK3nYtWuX0d4OpgYMGIBt27Y9sR1zW3W+W6+GnFCIiIisRg+h2OGIZM15uHz5MmrUKP6H/PPPP4+MjIwntmNuq85C51I/ZoOIiEgRfGaTNFk/sd3c3CSTg4yMDLi5uZU2JiIiIrJjspKHoKAgfPnll8Wef7QFJhER0dOMwxbSZA1bxMXFoUuXLvDy8sK4cePg4+MDAMjOzsbHH3+MpKQk/Pjjj1YJlIiIqKxw2EKarOQhNDQUixcvxujRozF//nx4enpCpVLhzp07cHFxwaeffopOnTpZK1YiIiKyA7I3iRo2bBj++c9/4ptvvsGFCxcghEBgYCDefPNNPP/889aIkYiIqExxh0lpFu0wWb169RI9drtbt25Yvnw5/Pz8LPkYIiIim+AOk9Ksuj7yl19+wf379635EURERFTGLH62BRERkaPihElpTB6IiIhMOOoSS6VwW0ciIiKShT0PREREJjhsIY3JAxERkQku1ZRm1eRhwoQJ8Pb2tuZHEBERKY49D9JkJQ9btmwpUb0ePXoAePjobSIiInIsspKHnj17PrGOSqWCTqezNB4iIiKb42oLabKSB71eb604iIiI7AaHLaQpvlSTO0oSERE5NsWSB61Wi7lz56JWrVpKNUlERGQTeiEUOxyRrORBq9UiPj4eLVu2RHBwMDZv3gwAWLlyJWrVqoUFCxaU6IFZRERE9kwo+McRyZrzMHnyZCxduhRhYWHYv38/3nrrLURFReHgwYOYN28e3nrrLTg7O1srViIiIrIDspKHb7/9FqtXr0aPHj3w+++/o0mTJigsLMSxY8egUqmsFSMREVGZctThBqXISh7+/PNPtGjRAgDw4osvQqPRYMyYMUwciIjIoXC1hTRZcx50Oh3UarXhdbly5VC+fHnFgyIiIiL7JavnQQiBgQMHQqPRAAAePHiA6OhoeHh4GNXbtGmTchESERGVMUed6KgUWcnDgAEDjIYo+vXrp3hAREREtsZhC2mykoekpCQrhUFERGQ/mDxIk5U8DBo06Il1VCoVvvjiC4sDIiIiIvsmu+ehZs2aaN68ObMyIiJyWPwJ9wRChn//+9+iUqVKolmzZuKTTz4Rf/31l5y327UHDx6IKVOmiAcPHtg6FJvifXiI9+Eh3oeHeB8e4n2gR1RCyOtC0Gq12LRpE1asWIH9+/ejW7duGDx4MF5++eWner+HnJwceHl54c6dO/D09LR1ODbD+/AQ78NDvA8P8T48xPtAj8h+MJZGo0GfPn2QnJyMU6dOoVGjRvj3v/+NgIAA5ObmWiNGIiIisiOleqqmk5MTVCoVhBDQ6XRKxURERER2THbyoNVq8fXXX6NLly4IDAzEiRMnsGjRImRmZnK3SSIiomeArNUW//73v7Fu3Tr4+/tj0KBB+Prrr1G5cmVrxVamNBoNpkyZYtg981nF+/AQ78NDvA8P8T48xPtAj8iaMOnk5IQaNWqgefPmkpMjuT01ERGR4yrV9tRERET07JG9VJOIiIiebaVabUFERETPHiYPREREJAuTByIqIi0tDSqVCrdv3y7xe6ZOnYpmzZpZLSZbCAgIwIIFC0pcPyMjAyqVCunp6VaLicgeOFzyMHDgQKhUqiLHhQsXcOzYMfTo0QNVq1aFq6srAgICEBERgevXrwMo/j/+qlWr0KpVK7i7u6NChQoICQnBtm3bbHB1lunevTu6du1q9tyePXugUqlw/Phxh7x2cx7/GlGr1ahbty6mT5+OwsJCsz80dTod5s+fj8aNG8PV1RWVKlXCK6+8gn379tnuIh6TmJiIChUqoLCw0FCWm5sLFxcXdOzY0ajuo+u7ePGiZJvBwcG4du0avLy8FI21Y8eOePfddxVt05yBAweiZ8+eRcrlJkW//vorhg4dqmhsSUlJqFixoqz3PPqajY6OLnJuxIgRUKlUGDhwoFFdlUoFFxcX1KpVC++99x4ePHhg9D6VSoXNmzeXOIbU1FS8+uqreO655+Du7o6GDRti7NixuHLliqxrKS1L7h8pz+GSBwDo2rUrrl27ZnRUqFABnTt3hre3N3744QecPn0aK1euRLVq1ZCXl1dsW3FxcRg2bBgiIiJw/PhxHD58GO3bt8drr72GRYsWleFVWW7w4MFITk7Gn3/+WeTcypUr0bJlS7P71DvCtRfn0dfI+fPnMXbsWEydOhWzZ88uUk8Igd69e2P69OkYPXo0Tp8+jbS0NPj7+6Njx46yvvlaS2hoKHJzc/Hbb78Zyvbs2QNfX18cOnTI6IdGamoqatSogTp16ki2qVar4evr+8yvrqpSpQrc3d1tHQYAwN/fH+vWrcP9+/cNZQ8ePMDatWtRo0YNo7qPvr4vXbqE+fPnY+nSpZgyZYrFn7106VKEhYXB19cXGzduxKlTp5CYmIg7d+5g7ty5FrdLTzHbPZPLOiIjI8Vrr71WpPy7774T5cqVEwUFBcW+9/LlywKA+L//+z8hhBAHDhwQAMTChQuL1I2NjRUuLi4iMzNTqdCtpqCgQPj4+IgPPvjAqPzu3buifPny4rPPPnPYazfH3NdIly5dRJs2bURqaqoAIP7++28hhBDr1q0TAMSWLVuKtPP666+L5557TuTm5pZB1NL8/PxEQkKC4fV7770nRowYIV544QWRmppqKO/QoYOIjIwUOp1OzJo1SwQEBAhXV1fRpEkT8e233xrqmd4HIYT4/PPPxfPPPy/c3NxEz549xdy5c4WXl5fh/JQpU0TTpk3F6tWrRc2aNYWnp6eIiIgQOTk5QoiH9x0Pn3RsOC5fvmyV+1Hc9wHT69qzZ49o3769cHV1Fc8//7wYOXKk0b9nzZo1xfz58w2vT58+Ldq1ayc0Go144YUXRHJysgAgvvvuOyHE/76HbNy4UXTs2FG4ubmJJk2aiP379xt9/uPHlClTSnw9L774ovjqq68M5WvWrBFNmjQRr732moiMjCz22l9//XXRvHlzo7LH45by3//+V6jVavHuu++aPf/418iGDRtEw4YNhVqtFjVr1hRz5sx54md6eXmJlStXCiGsd/9IeQ7Z82COr68vCgsL8d1330GUcHXq119/jfLly2PYsGFFzo0dOxYFBQXYuHGj0qEqrly5chgwYACSkpKMrv3bb7+FTqdDnz59irzHUa69pNzc3JCfn1+kfO3atQgMDET37t2LnBs7diz++usvJCcnl0WIkkJDQ5Gammp4nZqaio4dOyIkJMRQfv/+fRw6dAihoaFISEjA6tWrkZiYiJMnT2LMmDHo168fdu/ebbb9ffv2ITo6GqNHj0Z6ejq6dOmCmTNnFql38eJFbN68Gdu2bcO2bduwe/dufPjhhwCATz75BG3btsWQIUMMPYL+/v5WuBslc/HiRXTt2hVvvPEGjh8/jvXr12Pv3r2IiYkxW1+n06Fnz55wd3fHoUOH8Pnnn+M///mP2br/+c9/EBcXh/T0dAQGBqJPnz4oLCxEcHAwFixYAE9PT8M9iIuLK3HMgwYNwsqVKw2vV6xYgaioKMn3/P7779i/fz/UanWJP+dx3377LfLz8/Hee++ZPf9oCOHIkSPo1asXevfujRMnTmDq1KmYNGkSkpKSZH+mte4fKcjW2YvSIiMjhbOzs/Dw8DAcb775phBCiAkTJohy5coJb29v0bVrV/Hxxx+LrKwsw3tNf/vu2rWraNq0abGf5enpKYYPH27Ny1HM6dOnBQCj30Jfeukl0a9fPyGEY1+7qcd/M9Pr9SI5OVloNBoRFxdX5DfTBg0amP0NVgghbt26JQCIjz76qGwCl7Bs2TLh4eEhCgoKRE5OjihXrpy4fv26WLt2rejQoYMQQoiUlBQBQGRkZAh3d3fDb3OPDB48WPTp00cIUfQ39IiICNGtWzej+n379i3S8+Du7m7oaRBCiHHjxomgoCDD65CQEDF69GgFr9w8c98HPDw8hKurq+G6Bg8eLIYOHWr0vj179ggnJydx//59IYRxz8POnTtFuXLlxLVr1wz1i+t5WL58uaHOyZMnBQBx+vRpIYQQK1euNLpvJb2e1157TVy/fl1oNBqRkZEhMjIyhKurq7hx40aRnodH167RaAQA4eTkJDZs2GDUJkrY8zB8+HDh6en5xHpvv/226NKli1HZuHHjRMOGDSU/01zPg9L3j5TnkD0PoaGhSE9PNxwLFy4EAMycORNZWVlITExEo0aNkJiYiAYNGuDEiRPFtiWe0EthaTZf1ho0aIDg4GCsWLECAHDhwgXs2bMHgwcPLvY9jnLt5mzbtg3ly5eHq6srXnnlFURERGDq1Klm6z4N96Fjx47Iy8vDr7/+ij179iAwMBBVqlRBSEiIYd5DWloaateujdzcXNy7dw9dunRB+fLlDcfq1auLnUh59uxZtG7d2qjM9DXwcHVChQoVDK/9/PwME5LLmun3gfT0dCxfvtxw/tixY0hKSjK6B+Hh4dDr9bh8+XKR9s6ePQt/f3/4+voayszdAwBo0qSJ4e9+fn4AoMh9qFKlCrp164akpCSsXLkS3bp1M/t8oUfXfujQIURGRiIqKgpvvPGGRZ8phCjR3JfTp0+jXbt2RmXt2rXD+fPnZT912Vr3j5Qja3vqp4WHhwfq1q1r9txzzz2Ht956C2+99RZmzZqF5s2bY86cOVi1alWRuvXq1cPevXuRn59f5AfE1atXkZOTg8DAQKtcgzUMHjwYI0eOxOLFi7Fy5UrUqVMHISEhZus62rWbCg0NxWeffQa1Wo1q1aqhXDnz/xXq1auH06dPmz33qNwe7kPdunXx/PPPIzU1FX///bfh37VatWrw9/fH/v37kZqaik6dOiE3NxcAsH37dlSvXt2ondI+8MjFxcXotUqlgl6vL1WbljL3feDxScO5ubkYNmwYRo0aVeS9phMQ5Xr8Pjz6wavUfRg0aJBhaGXx4sVm6zx+7StWrEDTpk3xxRdfSP6yUJzAwEDcuXMH165dM/wgt5RKpSqSjBcUFBSpZ837R8pwyJ6HklKr1ahTp06xqy369OmD3NxcLF26tMi5OXPmwNXVFREREdYOUzG9evWCk5MT1q5di9WrV2PQoEHF/kbhaNdu6tE31xo1ahSbOAAP78P58+exdevWIufmzp2LatWqoUuXLtYMtcRCQ0ORlpaGtLQ0oyWaHTp0wM6dO3H48GGEhoaiYcOG0Gg0yMzMRN26dY2O4uYg1K9fH7/++qtRmenrklCr1bJ/C7WWf/zjHzh16lSRe1C3bl2zvUn169fHf//7X2RnZxvKbHEPunbtivz8fBQUFCA8PPyJ9Z2cnDBhwgRMnDjRaKVGSb355ptQq9X4+OOPzZ5/tOz1hRdeKLJ8ed++fQgMDISzszOAhz0n165dM5w/f/487t27Jysee/oaepY5ZM+DOdu2bcO6devQu3dvBAYGQgiBrVu3YseOHUYTkB7Xtm1bjB49GuPGjUN+fj569uyJgoICfPXVV1i4cCGSkpLw3HPPlfGVWK58+fKIiIhAfHw8cnJyDOvCzXG0a7dU79698c033yAyMhKzZ89G586dkZOTg8WLF2Pbtm3YtWtXkd+2bSU0NBQjRoxAQUGBUY9SSEgIYmJikJ+fj9DQUFSoUAFxcXEYM2YM9Ho92rdvjzt37mDfvn3w9PREZGRkkbZHjhyJDh06YN68eejevTt+/vln7Ny5U/ZSzoCAABw6dAgZGRkoX748vL294eRkm99h3n//fbRp0wYxMTF455134OHhgVOnTiE5OdnsUuQuXbqgTp06iIyMxMcff4y7d+9i4sSJACDrPgQEBCA3NxcpKSlo2rQp3N3dZS0HdXZ2NvR6Pfqh/CRvvfUWxo0bh8WLF8ueYOjv74/58+cjJiYGOTk5GDBgAAICAvDnn39i9erVKF++PObOnYuxY8eiVatW+OCDDxAREYEDBw5g0aJFWLJkiaGtTp06YdGiRWjbti10Oh3ef/992f9/Snv/SCG2nHBhDcUt0bp48aIYMmSICAwMFG5ubqJixYqiVatWhok6QhSdNPjIF198IVq0aGGYbKVWq8Xu3buteyFWsn//fgFAvPrqq0blz8K1P1Lc14gQ5pcoFhQUiNmzZ4tGjRoJtVotAAhvb29x8uTJsgm4hB79GzZo0MCoPCMjQwAQ9evXN5Tp9XqxYMECUb9+feHi4iKqVKkiwsPDDf+2xS3VrF69umGp5owZM4Svr6/h/KOlmo+bP3++qFmzpuH12bNnRZs2bYSbm5tdLNU8fPiw6NKliyhfvrzw8PAQTZo0ETNnzjTUL26pplqtFg0aNBBbt24VAMSuXbuEEOb/H/39999FJitHR0eL5557TvZSzeI8aammEEIkJCSIKlWqGJaiooQTJh9JTk4W4eHholKlSsLV1VU0aNBAxMXFiatXrxrqPFqq6eLiImrUqCFmz55t1MaVK1fEyy+/LDw8PES9evXEjh07zE6YVPr+kfL4VE2ZMjIyEBISgrZt22LNmjUlzvwdwbN87Y87evQowsLCMHjwYLMbSz0rhgwZgjNnzmDPnj22DsVm9u3bh/bt2+PChQtP3HiLyJE803MeLBEQEIC0tDQ0aNDgmdu//lm+9sf94x//QEpKCjw8PJ64zbMjmTNnDo4dO4YLFy7g008/xapVq8wOcTiy7777DsnJycjIyMBPP/2EoUOHol27dkwc6JnDngciKpFevXohLS0Nd+/eRe3atTFy5Eizz1pwZKtXr8aMGTOQmZmJypUrIywsDHPnzn2q5//MmjULs2bNMnvupZdews6dO8s4InoaMHkgInqG3bp1C7du3TJ7zs3NrchyXiKAyQMRERHJxDkPREREJAuTByIiIpKFyQMRERHJwuSBiIiIZGHyQERERLIweSAiIiJZmDwQERGRLP8P9ZA2Kph86VcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Ячейка для кода № 14\n",
-    "mcorr = ???\n",
+    "\n",
+    "mcorr = menDf[['FSIQ','VIQ','PIQ','Weight','Height','MRI_Count']].corr()\n",
+    "sns.heatmap(mcorr)\n",
     "#\n",
     "#"
    ]
@@ -421,7 +1348,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(ответ)"
+    "(ответ)Зависимость одного параметра от другого почти минимальная или отсутствует "
    ]
   },
   {
@@ -435,7 +1362,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(ответ)"
+    "(ответ)Чем больше однородность данных в выборке тем меньше факторов смещающих выборочную корреляцию в какую - либо сторону, поэтому лучше сделать в данном случае разделение по полу"
    ]
   },
   {
@@ -449,7 +1376,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(ответ)"
+    "(ответ)Наиболее сильную корреляцию с размером мозга имеет PIQ,FSIQ,VIQ - Performance IQ, полная шкала IQ,вербальный интелект - у мужчин, у женщин есть корреляция между весом и размером мозга. Вывод - IQ человека зависит от физических размеров мозга - ожидаемый результат"
    ]
   }
  ],
@@ -470,7 +1397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,