03.6-Outliers.ipynb

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    "## Outliers\n",
    "\n",
    "An outlier is a data point which is significantly different from the remaining data. \"An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.\" [D. Hawkins. Identification of Outliers, Chapman and Hall , 1980.]\n",
    "\n",
    "\n",
    "### Should outliers be removed?\n",
    "\n",
    "Depending on the context, outliers either deserve special attention or should be completely ignored. Take the example of revenue forecasting: if unusual spikes of revenue are observed, it's probably a good idea to pay extra attention to them and figure out what caused the spike. In the same way, an unusual transaction on a credit card is usually a sign of fraudulent activity, which is what the credit card issuer wants to prevent. So in instances like these, it is useful to look for and investigate further outlier values.\n",
    "\n",
    "If outliers are however, introduced due to mechanical error, measurement error or anything else that can't be generalised, it is a good idea to remove these outliers before feeding the data to the modeling algorithm. Why? Because some algorithms are sensitive to outliers. \n",
    "\n",
    "\n",
    "### Which machine learning models are sensitive to outliers?\n",
    "\n",
    "Some machine learning models are more sensitive to outliers than others. For instance, AdaBoost may treat outliers as \"hard\" cases and put tremendous weights on outliers, therefore producing a model with bad generalisation.\n",
    "\n",
    "Linear models, in particular Linear Regression, can be also sensitive to outliers.\n",
    "\n",
    "Decision trees tend to ignore the presence of outliers when creating the branches of their trees. Typically, trees make decisions by asking if variable x >= a certain value, and therefore the outlier will fall on each side of the branch, but it will be treated equally than the remaining values, regardless of its magnitude.\n",
    "\n",
    "A recent research article suggests that Neural Networks could also be sensitive to outliers, provided the number of outliers is high and the deviation is also high. I would argue that if the number of outliers is high (>15% as suggested in the article), then they are no longer outliers, and rather a fair representation of that variable. A link to this article can be found in the \"Additional reading resources\" lecture within this section of the course.\n",
    "\n",
    "\n",
    "### How can outliers be identified?\n",
    "\n",
    "Outlier analysis and anomaly detection are a huge field of research devoted to optimise methods and create new algorithms to reliably identify outliers. There are a huge number of ways optimised to detect outliers in different situations. These are mostly targeted to identify outliers when those are the observations that we indeed want to focus on, for example for fraudulent credit card activity.\n",
    "\n",
    "In this course, however, I will focus on identifying those outliers introduced by mechanical or measurement error. Those outliers that are indeed a rare case in the population, and that could be ignored. I will show how to identify those outliers, so that in later sections of the course, we can learn how to pre-process them before using the variable to train machine learning algorithms.\n",
    "\n",
    "\n",
    "### Extreme Value Analysis\n",
    "\n",
    "The most basic form of outlier detection is **Extreme Value Analysis** of 1-dimensional data. The key for this method is to determine the statistical tails of the underlying distribution of the variable, and then find the values that sit at the very end of the tails.\n",
    "\n",
    "If the the variable is Normally distributed (Gaussian), then the values that lie outside the mean plus or minus 3 times the standard deviation of the variable are considered outliers.\n",
    "\n",
    "- outliers = mean +/- 3* std\n",
    "\n",
    "\n",
    "If the variable is skewed distributed, a general approach is to calculate the quantiles, and then the inter-quantile range (IQR), as follows:\n",
    "\n",
    "- IQR = 75th quantile - 25th quantile\n",
    "\n",
    "An outlier will sit outside the following upper and lower boundaries:\n",
    "\n",
    "- Upper boundary = 75th quantile + (IQR * 1.5)\n",
    "\n",
    "- Lower boundary = 25th quantile - (IQR * 1.5)\n",
    "\n",
    "or for extreme cases:\n",
    "\n",
    "- Upper boundary = 75th quantile + (IQR * 3)\n",
    "\n",
    "- Lower boundary = 25th quantile - (IQR * 3)\n",
    "\n",
    "\n",
    "\n",
    "## Datasets for this notebook: \n",
    "\n",
    "In this demo, we will use the House Prices and Titanic datasets.\n",
    "\n",
    "- To download the datasets please refer to the lecture **Datasets** in **Section 1** of the course.\n",
    "\n",
    "We will also use a dataset included in Scikit-learn:\n",
    "\n",
    "### Boston house prices dataset"
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      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print information for boston dataset\n",
    "from sklearn.datasets import load_boston\n",
    "print(load_boston().DESCR)"
   ]
  },
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   "metadata": {},
   "source": [
    "## In this demo\n",
    "\n",
    "We will:\n",
    "\n",
    "- Identify outliers using complete case analysis in Normally distributed variables.\n",
    "- Identify outliers using complete case analysis in skewed variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# for Q-Q plots\n",
    "import pylab\n",
    "import scipy.stats as stats\n",
    "\n",
    "# boston house dataset for the demo\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>CRIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>4.98</td>\n",
       "      <td>0.00632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
       "      <td>9.14</td>\n",
       "      <td>0.02731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.185</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.02729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.998</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.03237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.147</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.06905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "      RM  LSTAT     CRIM\n",
       "0  6.575   4.98  0.00632\n",
       "1  6.421   9.14  0.02731\n",
       "2  7.185   4.03  0.02729\n",
       "3  6.998   2.94  0.03237\n",
       "4  7.147   5.33  0.06905"
      ]
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   "source": [
    "# load the the Boston House price data\n",
    "\n",
    "# load the boston dataset from sklearn\n",
    "boston_dataset = load_boston()\n",
    "\n",
    "# create a dataframe with the independent variables\n",
    "# I will use only 3 of the total variables for this demo\n",
    "\n",
    "boston = pd.DataFrame(boston_dataset.data,\n",
    "                      columns=boston_dataset.feature_names)[[\n",
    "                          'RM', 'LSTAT', 'CRIM'\n",
    "                      ]]\n",
    "\n",
    "\n",
    "boston.head()"
   ]
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   "execution_count": 4,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "    Age     Fare\n",
       "0  22.0   7.2500\n",
       "1  38.0  71.2833\n",
       "2  26.0   7.9250\n",
       "3  35.0  53.1000\n",
       "4  35.0   8.0500"
      ]
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   "source": [
    "# load the titanic dataset\n",
    "\n",
    "titanic = pd.read_csv('../titanic.csv',\n",
    "                      usecols=['Age', 'Fare'])\n",
    "\n",
    "# The variable age has missing values, I will\n",
    "# remove them for this demo\n",
    "titanic.dropna(subset=['Age'], inplace=True)\n",
    "\n",
    "titanic.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify variable distribution\n",
    "\n",
    "In Normally distributed variables, outliers are those values that lie beyond the mean plus or minus 3 times the standard deviation. If the variables are skewed however, we find outliers using the inter-quantile range. In order to decide which method to utilise to detect outliers, we first need to know the distribution of the variable.\n",
    "\n",
    "We can use histograms and Q-Q plots to determine if the variable is normally distributed. We can also use boxplots to directly visualise the outliers. Boxplots are a standard way of displaying the distribution of a variable utilising the first quartile, the median, the third quartile and the whiskers.\n",
    "\n",
    "Looking at a boxplot, you can easily identify:\n",
    "\n",
    "- The median, indicated by the line within the box.\n",
    "- The inter-quantile range (IQR), the box itself.\n",
    "- The quantiles, 25th (Q1) is the lower and 75th (Q3) the upper end of the box.\n",
    "- The wiskers, which extend to: \n",
    "  -- top whisker: Q3 + 1.5 x IQR\n",
    "  -- bottom whisker: Q1 -1.5 x IQR\n",
    "\n",
    "Any value sitting outside the whiskers is considered an outlier. Let's look at the examples below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create histogram, Q-Q plot and\n",
    "# boxplot\n",
    "\n",
    "\n",
    "def diagnostic_plots(df, variable):\n",
    "    # function takes a dataframe (df) and\n",
    "    # the variable of interest as arguments\n",
    "\n",
    "    # define figure size\n",
    "    plt.figure(figsize=(16, 4))\n",
    "\n",
    "    # histogram\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.distplot(df[variable], bins=30)\n",
    "    plt.title('Histogram')\n",
    "\n",
    "    # Q-Q plot\n",
    "    plt.subplot(1, 3, 2)\n",
    "    stats.probplot(df[variable], dist=\"norm\", plot=pylab)\n",
    "    plt.ylabel('RM quantiles')\n",
    "\n",
    "    # boxplot\n",
    "    plt.subplot(1, 3, 3)\n",
    "    sns.boxplot(y=df[variable])\n",
    "    plt.title('Boxplot')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normally distributed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
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boslLpq9fD9//Ppx2Gtx1VxNvJpL6Kioq6twWqY2SUhEREUkJscNz27ULvqalwTnnQEM/+3buDLfd1shA3OHJJ2HUKJgzB26/HR5+uJE3E2k90tLS6twWqY1+U0RERCTpVR+eG01C46313749ZGUFieyQITBzZiOLHK1ZA6ecElw8YgR88AFce23wBiJtXK9evercFqlNXNV3RURERMJ0/fUNH55rFiStQ4YEvaJNqrTrDr//PVx1VVA16c47Ydo0SE9vwk1FWpdNmzbVuS1SGyWlIiIikvQaWpQoPR0efzxBS77k5sLkyfDaa3DMMfDIIzB8eAJuLCIioOG7IiIikgIaUpSoc+cEJaSVlXD//TB6NPz733DfffDGG0pIRWrRuXPnOrdFaqOkVERERJLebbcFyWZ9srKaMF801ooVcOyxcOmlcOSRsGQJTJ0aVFYSkRrdcsstVbZvvfXWkCKRVKOWVVpU4fZS8gpLKNi2K+xQREQkhUycGCSbQ4YE29GpnNGvQ4bA7NmwaVMTE9KKimB5lzFjgiJGjzwCr7wSlP0VkTqNGzeOjIwMADIyMjj00ENDjkhSheaUSov5ZMNWHvvXKqKFEo/dvy8TDugXakwiIpL8cnKCQkerVwfDeGfPTtBc0eqWL4cLL4T//AdOPBEefBCys5vhjURar+HDh7N48WJGjBgRdiiSQtRTKi2ieEcZz8zPo0/Xjpx75BDGDurB3z/cwDufqiqbiIjULCcHunQJ1iHNzQ0K4ObmBkvD5OQk8I3Ky+EXv4CxY+Hjj4Osd84cJaQiDVRQUMDixYsBWLRoEQUFBSFHJKlCSam0iGffy6O0opKzDxvM/nt149RDBnJA/278ddE63l9dFHZ4IiKSZHJy4IILYPv2PY+VlAQ9pwmxcCEcfjhcdx2cfDIsWxZ0w5ol6A1E2o577rmnyvaMGTNCikRSjZJSaXbri3fy6cbtTDigH327dQIgPc04fdxAMju245dzP8TjXf1cJAmY2ZVmttTMlpjZH82sU9gxibQ2118PZWW1H2/oEjF7KC2Fn/0Mxo2D/Hx49tng1U/TSqLU1klDvfXWW1W233zzzXACkZSjpFSa3aL8LRgwdlCPKvs7tkvn2P378t9VhcxbviGc4EQayMyygWnAOHcfDaQDZ4YblUjrU1/S2ZAlYvawYEGQjN58M5x5ZtA7etppTbhh66O2TkRakpJSaVbuzqL8zQzrk0nXTu33OP6lob0Y1juTX738IRWV6i2VlNEOyDCzdkBnYG3I8Yi0Kjk5da+8YhYsEdNgO3fCNdcEw3ULCuDFF+GJJ4J1ZKQmauukQQYNGlTntkht4kpKzex4M/vIzFaY2TU1HL/LzD6IvD42s82JD1VS0dotOynYXsqY7B41Hk9PM6ZPGMEnG7bx1sfqLZXk5+5rgDuA1cA6YIu7vxp7jplNMbMFZrZg48aNYYQpkrKmToVJk4KVWWpz8cWNqL77738HhYx+9atgsurSpUGFXamR2jppjOnTp1fZvuKKK0KKRFJNvUmpmaUD9wEnACOBs8xsZOw57n6lu49197HADOBPzRGspJ7F+ZtJMxg1oFut53zrwP707dqRx/+d24KRiTSOmfUETgH2BgYAmWZ2Tuw57j7T3ce5+7g+ffqEEaZISsrJCVZhqa3MQFZWUBj3/vsbcNPt2+GKK+Doo4Oe0ldfhVmzoEfND0sloLZOGuPVV1+tc1ukNvH0lB4GrHD3le5eCjxF0EjV5izgj4kITlLfsnXFDO/bhc4da18St316GmcfPpi3Pt7Iqk01lFkUSS4TgM/cfaO7lxE8hDsq5JhEUl5ODpx7bu0JqRls2tTAHtI33oAxY+Duu4Mu2MWL4bjjEhJvG6C2Thps3rx5VbZff/31kCKRVBNPUpoN5MVs50f27cHMhhA8Uft7Lcc1zKMNKdlVzqZtpezdu0u955592GDapRlP/Ee9pZL0VgNHmFlnMzNgPLA85JhEUtrUqcFapJWVtZ/ToMJGxcVwySVw7LFBNvvWW3DvvdC1a5NjbUPU1olIi4knKa1poa7aKtKcCTzn7jXOBNEwj7Ylr6gEgEG9Muo9t2+3Thw/ei+eXZDHzrI6JhKJhMzd3wWeA94HFhO0ozNDDUokBeXkQO/eQc74wAN1n9ugwkYvvwyjR8NDD8FVV8GiRXDMMU2Ot61RWyeN0bFjxzq3RWpT+5jKL+QDsaWzBlJ79bUzgUubGpS0DqsLd2DAwB6d4zr/9HGD+Ouidbz50QaOH90fgCffrX1NgLMPb8p6ACKN5+43ATeFHYdIqsrJCWoN1bUOaay4ChsVFQVJ6GOPwQEHBIWNjjiiqaG2aWrrpKFKSkrq3BapTTw9pfOBEWa2t5l1IEg851Q/ycz2A3oC7yQ2RElV+UUl7NW9Ex3axbfy0FH7ZNG7S0de+N+aZo5MRETCNH16/AlpVlYchY3mzIFRo4LlXa67Dt5/XwmpiEgKqTdbcPdy4DLgFYK5BM+4+1Izu8XMTo459SzgKffaShRIW1LpTl5RCYN6xtdLCtAuPY2TDxrAGx9uZEtJnJ9WREQkpeTkBEuExqNDh6BGUa02bYKzz4ZTToE+feC//w3G+XbqlJBYRUSkZcTVheXuc919X3ffx91vi+y70d3nxJzzM3ffYw1TaZs2bd3FzrJKBvWKPykF+O7B2ZRWVDJ3ybpmikxERMJUbRnDWnXpAo8+WsuwXXd45hkYORKeew5uvhnmz4dDDklorCLSMOnp6XVui9QmvnGVIg20u8hRz/qLHMUand2NYX0yNYRXRKQVmjo1vl7SSy6BrVtrSUg//xxOPRXOOAOGDIH33oMbbwy6VUUkVO3atatzW6Q2+k2RZrG6cAed2qfRu2vDqq6ZGSeOGcC9f/+ETdt2NVN0IiLS0nJy6q+ym5UVDNettXf0iSfgiiugpAR+9augsJE+9EoKmzFjBitWrAg7jITp0qULu3btqrI9Pd7hEUls+PDhXH755WGH0aqpp1SaxbotOxjQI4M0q2lFobp9Y2Q/Kh3+vnxDM0QmIiJhqO9z6ezZwRTRGhPS/Hw48UQ477ygsu7ChfDjHyshFUky/fr1q3NbpDZqzSXh3J0Nxbs4dEjPRl0/akA3BnTvxKvL1nPs/n0THJ2IiIShrmG7WVl19I7OmgVXXw3l5UE36qWXguapSSvRGnvfTj31VAoKCjjllFO48sorww5HUoR6SiXhtuwoo7Sikr7dGrdgspnxjVF78Y9PNlJaXpng6EREpKXl5NR9vMYKu599BscdB1OmwKGHwuLFMG2aElKRJNevXz8yMzM599xzww5FUoiSUkm4DVuDuQR9uza+JP83RvZjV3kln2zYmqiwREQkJNdfX/uxzMxqvaSVlTBjBoweHSzx8uCD8PrrMGxYs8cpIk3Xvn17hg8fTlZWVtihSApRUioJt6F4JwB9G1jkKNaX9u5F94z2LFtbnKiwREQkJLm5tR976KGYjY8/hq9+NegR/cpXYMkS+OEPIU0fV0REWjO18pJwG7buIrNDOpkdGz9luX16Gsfs24cVG7bh7gmMTkREWtLUqbUf2z2XtKIC7rgDDjooSEQfewz+9jcYPLilwhQRkRApKZWE27B1F327NX7obtRXhvdm665y1hdraRgRkVQ0dWrty8CYReaSLl0KRx0FP/oRfPObsGxZUGW3EdXbRUQkNSkplYRydzZs3dmkobtRR4/oDaB5pSIiKai+dUnTvYyJn/0cDj4YVq6Ep56CF16A/v1bLkgREUkKSkolobbuLGdnWWVCktIBPTLo07UjKzZsS0BkIiLSUnJyYNKk2o8fxAd80P4wuOEG+N73gt7RM85Q76iISBulpFQSanfl3QQM3wUY3rcLn23aTlmFloYREUkFU6fCOecES4xW14Fd3MINzOdLDOu8Dv70p6CHtE+flg9URESShpJSSagNW5teeTfWiL5dKK90cgtKEnI/ERFpPnUN2T2Md3mfQ7iBn/N0u4lkrFwG3/1uywYoIiJJqfHlUUVqsGHrLjq1T6NLAyrvPvnu6lqP7d07k3QzVmzYxvC+XRIRooiINJMf/GDPfZ3YwS3cyFXcyVoGcGLaXM567ATo1fLxiYhIclJPqSRU4bZSenfpiCVoXlDHdulk98xgVcH2hNxPRESax4QJsHNn1X1f5p8s5CB+xB3M4geMZgln/eGEYBkYERGRCCWlklAF23fRK7NDQu85NCuTNUU7NK9URCRJTZgA8+Z9sZ3JNu5mGm9zDO0pYzyvczEPMfGS7kpIRURkD3ElpWZ2vJl9ZGYrzOyaWs453cyWmdlSM3sysWFKKigtr2RzSRlZmYmZTxo1tHdnKtzJK9S8UhGRZDNqVNWE9FjmsZgDuYx7mcHlHMhi/s54LrkE7r8/vDhFRCR51Tvxz8zSgfuA44B8YL6ZzXH3ZTHnjACuBb7s7kVm1re5ApbklV9UggNZCe4pHdIrEwNWFZQwrI/mlYqIJItRo4LVXAC6sYXf8COm8DAfsS/H8Db/4mgAZs9GPaQiIlKreHpKDwNWuPtKdy8FngJOqXbOZOA+dy8CcPcNiQ1TUkFupCczq0tik9KMDun069aJXM0rFRFJGrEJ6QnMZQmjuYhH+DU/Yiwf7E5Ix49XQioiInWLJynNBvJitvMj+2LtC+xrZv8ys/+Y2fE13cjMppjZAjNbsHHjxsZFLEkrd1OQNCZ6TinAkKzO5BaWUFFZw8J3IiLSYqZOBbMgIe1JIY9xHnP5NlvozpG8w0/4NTvJAIKE9PXXQw5YRESSXjxJaU1lVKtnBu2AEcDXgLOAWWbWY4+L3Ge6+zh3H9dHC2W3OrmFJXRIb9hyMPEa2juT0vJKPi/eWf/JIiLSLEaN+mId0u/wAssYydk8yS3cwKG8x3wO232uElIREYlXPElpPjAoZnsgsLaGc/7i7mXu/hnwEUGSKm1IbkEJvTI7JGw5mFhDszIBWLVJQ3hFRMIwYULQO9qHDTzFGbzA91jLAL7EfG7iFkr5osjdyJFKSEVEJH7xJKXzgRFmtreZdQDOBOZUO+fPwNcBzKw3wXDelYkMVJJfbsH2hM8njeqe0Z7uGe3JL1IFXhGRlhYs+eKcwVMsZRTf4c9cz885nHdZyNgq544cCUuXhhSoiIikpHqTUncvBy4DXgGWA8+4+1Izu8XMTo6c9gpQYGbLgDeAH7l7QXMFLcmnotLJK9zRLPNJowb2zCCvaEez3V9ERPY0YQIsm7eWF/guT3EWKxnGIbzP7VxPOe2rnKuEVEREGiOuyX/uPheYW23fjTHfO3BV5CVt0OfFOymtqEz4GqWxBvXszNK1xZTsKqdzM8xbFRGRqnJmOwPnPc6zXEkndvJ/3MHvuIJK0vc4VwmpiIg0VjzDd0Xq1ZyVd6MG9gyqOeZvVm+piEhze+Hu1WRNOoHHuIDFHMgYFnEn/1djQjp+vBJSERFpPCWlkhDNtUZprOweGRiQV6h5pRIeM9vPzD6IeRWb2RVhxyWSMJWV3Nz/QSZcMYqj+SeXMYOv8SYraqlfOHu2ihq1RmrrRKQlaQykJMTqwhLapxvdM9rXf3IjdWyfTp+uHcnXvFIJkbt/BEFlFzNLB9YAL4QalEiC3DjxU7725GRu4g1eZzyTeZhV7F3r+bNnw8SJLRigtBi1dSLSkpSUSkLkFZYwoEcGac2wHEysQb06s3xdMcE0ZpHQjQc+dffcsAMRaZKKCm7sdS8/Kb6OctrxAx7mES6i5qXKA5dcooS0DVFbJyLNSsN3JSHyinYwqGfnZn+fgT0zKCmtoKikrNnfS1o3M8s0s7TI9/ua2clm1tCu/jOBP9Zw7ylmtsDMFmzcuDER4Yo0nw8/ZEnWMdxSfAVv8jVGsZRH+AH1JaT3399yIUro1NaJSLNSUioJsaaoZHchouYUTXzztF6pNN3bQCczywbmARcAj8V7cWTd5pOBZ6sfc/eZ7j7O3cf16dMnQeGKJFh5OfzqVzB2LAO2LGcSf+BE/soaBtZ52ciRSkjbErV1ItISlJRKk5WUlrNpWymDejV/T2m/bp1ol2as1bxSaTpz9xLge8AMd/8uMLIB158AvO/u65slOpHmtHgxHHEEXHM+Qop6AAAgAElEQVQNf9r1LUayjNlMoq7eUdCyL22U2joRaXZKSqXJooWHWqKnND3N2Kt7J9ZoWRhpOjOzI4GJwEuRfQ2ZZ38WNQxnE0lqpaVw881UHHwoG95bzfd5hlN5nvXsVedlZkFRIyWkbZLaOhFpdip0JE2WHxlKO7BnZz76fGuzv9+AHhksyt+Mu2PNXFhJWrUrgGuBF9x9qZkNA96I50Iz6wwcB/ywGeMTSaz33oMLL4RFi3iKs5nO3RTQu97L1DvadqmtE5GWop5SabK8wqDXclCv5u8phWC90p1lleQWaF6pNJ67v+XuJwP3RrZXuvu0OK8tcfcsd9/SrEGKJMLOnXDddXD44axbvJGT+QvnkBNXQqre0bZNbZ2ItBQlpdJkeYUldGyXRp8uHVvk/bJ7BMnvojX6GymNZ2ZHmtkyYHlk+yAzU/kWaV3eeQcOPhh+8Qv+wLmM9KW8yMlxXTp+vJZ8ERGRlqGkVJosv2gHA3tmtNhQ2r7dOpKeZixRUipN8zvgm0ABgLsvBI4JNSKRRCkpgauuwr/8ZVZ/uJ1v8jLnVTzKZnrGdXmPHvD6680co4iISISSUmmyvKKSFqm8G9UuLY29unVicb6SUmkad8+rtqsilEBEEumtt2DMGLjrLh7wixnNEl7lm3FfPn48FBU1Y3wiIiLVKCmVJssv2rF7/dCWkt0zgyVrt+DuLfq+0qrkmdlRgJtZBzO7mshQXpGUtHUrXHopfO1r4M74tDe4lPvZSre4Lh8wANzVQyoiIi1PSak0SfHOMrbsKGuR5WBiZffIYOvOchU7kqa4GLgUyAbygbGRbZHU8+qrMHo0PPAAXHEF2QWL+Hvl1+K+fPx4WLOm+cITERGpi5JSaZK8wiApbMnhu/BFsaPFmlcqjeTum9x9orv3c/e+7n6OuxeEHZdIg2zeDBddBN/8JmRkMHHIP7Hf3cXaLZlxXR5df1S9oyIiEqa41ik1s+OBu4F0YJa7/7La8fOB3wDR56z3uvusBMYpSSq/KFgOpqV7Svt260iH9DSWrNnCSQcNaNH3ltRmZjOAWsd9x7ssjEjo/vpX+OEPYf16lp50DYe+eBO76BT35e3bQ2lpM8YnIiISp3qTUjNLB+4jWDw5H5hvZnPcfVm1U59298uaIUZJYrt7Slt4Tmm7tDT279+VRSp2JA23IOwARJqkoACmT4ecHDjwQE7v+BeefXFcg24xcqTWH5XUNGPGDFasWBF2GFKH6H+f6dOnhxyJ1GX48OFcfvnlYYexWzw9pYcBK9x9JYCZPQWcAlRPSqUNyi/aQZeO7ejRuX2Lv/fo7O68uHAt7t5iy9FI6nP3x8OOQaTRnnsuKGZUWAg33UTmbddRUt6hQbeYPVvrj0rqWrFiBR8sWU5F515hhyK1SCsNBiO9t3J9yJFIbdJLCsMOYQ/xJKXZQOyyCfnA4TWcd6qZHQN8DFxZw1ILmNkUYArA4MGDGx6tJJ3VhSUtukZprAOzu/Pku6vJLShhaO/45k+JmNnv3P0KM3uRGobxuvvJIYQlUrf164Nk9Pnn4ZBDeOmK1zjxujENvs348UpIJfVVdO7Fjv2/FXYYIikr48O5YYewh3iS0pqyjeof5F4E/ujuu8zsYuBx4Ng9LnKfCcwEGDdunNbyaAXyCsNLCA/M7g4ExY6UlEoDPBH5ekeoUYjEwz0Ypjt9OmzfDr/4BYPvuZq86+IqCVHFJZfA/fc3Q4wiIiJNFE/13XxgUMz2QGBt7AnuXuDuuyKbDwOHJiY8SWbuHsoapVH79uu6u9iRSLzc/b3It2Pd/a3YF8GyMCLJYc0aOPlkmDQJ9tuPCw/+H3btNeSta1hCOnJkkNsqIRURkWQVT1I6HxhhZnubWQfgTGBO7Alm1j9m82S0AH2bsGlbKTvKKhjUq2Ur70Z1aBcUO9KyMNJI59Ww7/yWDkJkD+7wyCNBNjlvHjd1u4v0d/7B7/9zQINuk5ER3EoFjUREJNnV+7jV3cvN7DLgFYIlYR5196VmdguwwN3nANPM7GSgHChEH+zahLyioPLu4BZeozSWih1JQ5nZWcDZwN5mFvuArSugdUolXKtWweTJ8PrrvMlX+QGz+HTH8AbfRtV1RUQklcQ1Bsjd5wJzq+27Meb7a4FrExuaJLvdy8GEmJSq2JE0wr+BdUBv4Lcx+7cCi0KJSKSyknsOeIALP/4JjvFj7uchfojHNaCpKlXXFRGRVNPwSgkiEflFOwAY2DOc4bugYkfScO6eC+QCR4YdiwgAK1Yw/6CLmFbyNq/wDaYwk9UMafBt2reH0tJmiE9ERKSZNfwRrEhEXmEJvbt0oHOH8J5tqNiRNJaZfc/MPjGzLWZWbGZbzaw47LikDamogDvvZMe+YxhRspALeJTjeblRCenIkUpIRUQkdamnVBotr6iEgSFV3o3q0C6N/fZSsSNplF8DJ7m7CrNJy1u2jOVHXsgBxe/yGidxMQ+yjgENvk2PHlBU1AzxiYiItCD1lEqj5RXuCHU+adTo7O4sWbMFdy19Kw2yXgmptLiyMrj9dnaNOpg+xSs4iyc5hb80OCFNTw/mjiohFRGR1kA9pdIoFZXO2s07OHFM//pPbmYHZnfnj/9dzerCEoZkaV6pxG2BmT0N/BmIrrOMu/8pvJCkVVu4kMJTLqBX7v/4C9/nMu5lI30bdAtV1RURkdZISak0yrotOyiv9KToKR0z8ItiR0pKpQG6ASXAN2L2OaCkVBKrtBRuu42yW26njF6cynP8iVPjvnzAAFizphnjExERCZmSUmmUvMKg8u6gkOeUwhfFjhav2cKJYxo+J0vaJne/IOwYpA2YP5/lR13IAeVL+COTuJK7KCQrrks1X1Sayszuqeu4u09rqVhEROqipFQaJa8oukZpeMvBRO0udpSvYkcSPzPrBFwEjAI6Rfe7+4WhBSWtxiEH7OCMD3/G1dxBV/rzbf7KXL4d9/VKSCVBLgaWAM8AawELNxwRkZopKZVGyS8sIc1gQI/wk1IIih29tGgt7o6Z/uZKXJ4APgS+CdwCTARU+EiaZNQo6LHsX/yRC9mPj5nJZH7Ebyime9z3uOQSuP/+ZgxS2pL+wPeBM4By4GngeXdP2Ucea9asIb1kCxkfzg07FJGUlV5SwJo15WGHUYWq70qj5BXtoH/3DNqnJ8ev0IHZ3SneWc7qwpKwQ5HUMdzdbwC2u/vjwLeBA0OOSVLQqFFgBpm2ncnLruAffIWO7GICr/FDZsadkI4cCe5KSCVx3L3A3R90968D5wM9gKVmNincyEREqlJPqTRKXmEJA3smRy8pBEkpqNiRNEhZ5OtmMxsNfA4MDS8cSTUTJsC8ecH3X+fvzOIHDOMzZnAZ1/ILttMl7nupd1Sak5kdApwFHAf8DXgv3IgaLzs7m893tWPH/t8KOxSRlJXx4Vyys/uFHUYVSkqlUfKKSvjKiD5hh7Hbvnt1oX26qdiRNMRMM+sJ3ADMAboAN4YbkqSKzp1hxw7oSjG/5sdczEN8wnCO4S3+wTFx30dLvEhzMrObgRMJpiY8BVzr7sk1Zk9EBCWl0gg7yypYX7wrKSrvRnVsl85+e3VlyRoVO5L4uPusyLdvAcPCjEVSR04OnHNO8P03eZmHmcwA1vIbruYmbmYH8bWLWuZFWsgNwErgoMjr9kjdBQPc3ceEGJuIyG5KSqXB1myOLAeTBJV3Yx2Y3Z2XFq1TsSOJi5nV2Cvq7rfEcW0PYBYwmmBt0wvd/Z3ERijJIicHJk0K5nsC9KCIO7mKC3iMZRzAUfyb/3J4XPdSMiotbO+mXKy2TkRaSnJUqZGUklcYXQ4meXpKAQ7M7qFiR9IQ22NeFcAJxD+n9G7gZXffn6D3QVV7W6GcHEhLC3pGownpyfyFZYxkEk/wc67nYP5Xb0JqBrNnB/dQQiotyd1za3oB+cDRcdxCbZ2ItAj1lEqD5RVFekqTaPguqNiRNIy7/zZ228zuIJhbWicz6wYcQ1DJEncvBUqbIUQJQfVe0ajebOQepnEWT/EBB/Et5vIBB9d5r/R0ePxxmDixGQMWqUOkvboUyCZo314DLgOuBj4Acuq5Vm2diLSIuHpKzex4M/vIzFaY2TV1nHeambmZjUtciJJs8gtL6NAujb5dO4YdShWxxY5EGqEz8c0tHQZsBH5vZv8zs1lmpqcgKS66rEtsr2jAOZ2nWcZITuV5fsqtfIn5tSaksb2i5eVKSCV0TwD7AYuBHwCvAqcBp7j7KfVcq7ZORFpMvUmpmaUD9xEMbRsJnGVmI2s4ryswDXg30UFKcskrCpaDSUtLrnmbKnYkDWFmi81sUeS1FPiIYKhafdoBhwAPuPvBBMN/qzysM7MpZrbAzBZs3Lgx4bFL4uTkBInksmV7HtuLdTzPqTzNmXzG3hzC+9zGTymnfY33Gj8eKiuViEpSGebu57v7QwRLwowDTnT3D+K4Vm2diLSYeHpKDwNWuPvKyNCNp4Canq7dCvwa2JnA+CQJ5RXuSLqhu1FjBvZgUd4WKiq9/pOlrTsROCny+gYwwN3vjeO6fCDf3aMP4J4j+OC2m7vPdPdx7j6uT5/kWTqprcvJgXbtgiQ0+opW0q3KmcQfWMoovsVcfsSvOYp/s5TRtd579mx4/fVmC12ksaLrMePuFcBn7r41zmvV1olIi4knKc0G8mK28yP7djOzg4FB7v7Xum6kJ2qtQ15RSdJV3o06dHBPtu4q5+P18f7NlTZsa8xrB9DNzHpFX7Vd5O6fA3lmtl9k13ighn42SRaxQ3MrKuo+dyB5vMS3+QPnsYyRHMRC7uBHVNRSgmH8+GCornpHJUkdZGbFkddWYEz0ezMrrutCtXUi0pLiKXRU0xjN3d1QZpYG3EVkInxd3H0mMBNg3Lhx6spKQVt3lrG5pCwpekqffHf1HvsKtu0CYEFuEQf079bSIUlqeR8YBBQRtHM9gOgvlVP3/NLLgRwz60CwBuAFzRinNNKECTBvXrxnO5N5mDu4mnQquJx7uI9L8Rqe3Y4cCUuXJjRUkWbh7ulNvEVStnXpJYVkfDg37DCkFmk7g+cdlZ30OSxZpZcUAv3CDqOKeJLSfIIPblEDgbUx210J1q96M7I25F7AHDM72d0XJCpQSQ65BcFyK4OTbDmYqF6ZHejTtSPvrSpk0hFDwg5HktvLwBx3nwtgZicAE9z9/+q7MDIfSwXdktTUqfDAA/GfvzcreZjJjOfvzONYJvMwn9XwTELJqLQ1ydjWDR8+POwQpB4rVgSj1YYPS66kR2L1S7r/l+JJSucDI8xsb2ANcCZwdvSgu28Beke3zexN4GolpK3TqoLtACxbV0xRSVk9Z7c8M2PckJ4syC0KOxRJfl9y94ujG+7+NzO7NcyApOlGjaq5aFFNjEou5T5+yTVUkM4UHuJhJhM7QGj8eM0VFUkml19+edghSD2mT58OwN13x1M7UCRQ75xSdy8nWNPqFYJFk59x96VmdouZndzcAUpyWbUpSEqzMpNrOZhYhw7pSX7RDtYXq+aW1GmTmf3UzIaa2RAzux4oCDsoaZypU2uvoluTEXzM2xzDDKbxFl9lFEt5mCmAVVnWRQmpiIhI84unp5TI8La51fbdWMu5X2t6WJKsPttUQrdO7ejQLq4lbkMxbmhQo2bBqiK+PaZ/yNFIEjsLuAl4gWAO6duRfZJCcnJg0qTqa4vWLp1yruQubuFGdtKJc3mcJ5hEtHdUPaMiIiItL66kVCRqVcF2srokby8pwKgB3ejUPo0FuYVKSqVW7l4ITA87Dmmchs4bBRjFEh7lQg5jPi/wHf549P0884/+/KF5QhQREZE4KSmVBlm1aTt7984MO4w6tU9PY+ygHsxfVRh2KCLSDLKzYe3a+s+LakcZ1/BLbuBWtqV3h5yn+O7pp/Ndq6m4vIiIiLS05B2DKUmneGcZBdtL6Z3kPaUARwzLYunaYrYkYTEmEWm8hiakY/kf8/kSt3Ija484lV7rlsEZZwQTUEVERCQpqKdU4ra7yFGXDiFHUrcn313N9l0VuMNvXvmIkQO+WCfr7MMHhxiZiDRGY4bqdmAXN3Ar1/BLtnToA0+/wNDvfKd5AhQREZEmUVIqcftsd1Ka/D2lg3pm0D7d+HTTtipJqYiZ3VPXcXef1lKxSO1ycuCHP4Tt2xt+7WG8y6NcyCiW8elXzmOfv9wFPXsmPkgRERFJCCWlErdVm0oAyMpM7p5SgHbpaQzplclnGxvxiVZau4uBJcAzwFpiF6WU0OXkwIUXQmlpw6/NoIRbuJEruYudvQbA7Lnsc8IJiQ9SREREEkpJqcRtVcF2BnTvRPv01JiKPKxPJq8uW8+2XeV06ahfddmtP/B94AygHHgaeN7di0KNSho1TDfqK7zNI1zECFbAD39I5q9/Dd00SkJERCQVpEZ2IUlhVcF2hmQld+XdWMMiVYKjw45FANy9wN0fdPevA+cDPYClZjYp3MjatpycxiWkmWxjhl3O23yVEXtXwLx58OCDSkhFRERSiJJSiduqTdsZmuTLwcTK7tmZDu3SWLlxW9ihSBIys0OAK4BzgL8B74UbUds2vRErxo7ndZalH8hl3AfTpsHixXDssYkPTkRERJqVxjRKXAq3l1JUUra79zEVpKcZw3pn8skGJaXyBTO7GTgRWA48BVzr7uXhRtW2TZ0KBQXxn9+NLdzB1UxmFuyzLzzyNhx9dPMFKCIiIs1KPaUSlxWRxG54vy4hR9IwI/p1pXB7KQXbdoUdiiSPG4DuwEHAL4D3zWyRmS02s0Xhhtb2TJjQsGG7p3Z6iXU9RzE57VH48Y/hgw+UkIqIiKQ49ZRKXHYnpX26sG7zzpCjid++fYMk+uP1WzkyBZaykRaxd9gBtGU5OcFQ3Yb0jAL83wWF3FE2HWbPhn1GwSsvwJe+1DxBioiISItSUipx+WTDVjLap5PdIyPsUBokq0tHemV24OP12zhyn95hhyNJwN1za9pvZunAmUCNx6XpGlNd95JL4P4Jf/pijO+NN8J110FHPWQSERFpLTR8V+KyYsM29umbSVpa6i3puG+/LqzctI3yisqwQ5EkYGbdzOxaM7vXzL5hgcuBlcDpYcfXGuXkBDlkQxPSvmzg/k2nw6mnwoABMH8+3HyzElIREZFWRkmpxGXFhm2M6Ns17DAaZd++XSmrcFYVlIQdiiSHJ4D9gMXAD4BXgdOAU9z9lDADa41ycuDcc6G0tCFXOWfxJJ92Ggl/+Qvcdhu8+y6MHdtcYYqIiEiINHxX6rVtVznrtuxkeN/UKnIUtXefTNLTjI8+Lw47FEkOw9z9QAAzmwVsAga7+9Zww2p9oglpZQMGKfRnLQ9yMSfzIhx0ODz6KIwc2XxBioiISOji6ik1s+PN7CMzW2Fm19Rw/OJI5coPzOyfZqZPEK3Ip5EiR/v0Sc2ktGO7dPbpk8nyz7fi7mGHI+Eri37j7hXAZ0pIE2/qVDjnnIYkpM75/J6P243k5E6vwW9/C//6lxJSERGRNqDepDRS/OM+4ARgJHBWDUnnk+5+oLuPBX4N3JnwSCU00XU+R6TYcjCx9t+rG4XbS3dXEZY27SAzK468tgJjot+bmbrTEyAnp2HzRweTy8scz++5kC5HHQSLFsFVV0F6evMFKSIiIkkjnp7Sw4AV7r7S3UsJFpuvMu/K3WM/yGUC6o5qRVZs2Eb7dGNIr85hh9JoB/TvBsBry9eHHImEzd3T3b1b5NXV3dvFfN8t7Phag+uvj++8jI6VvHv+A+R2Gc03M/8F994Lb7wBI0Y0b4AiIiKSVOJJSrOBvJjt/Mi+KszsUjP7lKCndFpNNzKzKWa2wMwWbNy4sTHxSghWbNjK3r0zaZeeunWxume0J7tHBq8tU1Iq0txy41hU54azP6XkyPEc9thUOOIIWLIELr0U0lK3nREREZHGieevf01rgOzRE+ru97n7PsBPgJ/WdCN3n+nu49x9XJ8+fRoWqYTmkw3bUrbIUawD+nflg7zNbNi6M+xQRFqtnBywOlaOSqOC+ef8jlteOBDefx9mzYJXX4WhQ1ssRhEREUku8SSl+cCgmO2BwNo6zn8K+E5TgpLksX1XOasLS9ivX+qPajygfzfcYd7yDWGHItJqTZ8OtdUT248P+aTfVxg3+0o49lhYuhQuuqjuLFZERERavXiS0vnACDPb28w6AGcCc2JPMLPYCUDfBj5JXIgSpg8/34p70MuY6vbq1okhWZ2Zu3hd2KGItEo5OVBQsOf+dMr5Cb9kafuxDCv7CGbPhhdfhIEDWz5IERERSTr1rlPq7uVmdhnwCpAOPOruS83sFmCBu88BLjOzCQRLLRQB5zVn0NJylq8LalhFCwWlMjPjWwf2Z+bbKynaXkrPzA5hhyTSqkyfvue+A1nEo1zION6Dk0+F++6Dfv1aPjgRERFJWnFVlHD3ue6+r7vv4+63RfbdGElIcffp7j7K3ce6+9fdfWlzBi0tZ/m6Yrp2asfAnhlhh5IQ3xrdn4pK59Vln4cdikirUr2XtD2l3MjNLGAcg1nN29OeheeeU0IqIiIie1CZQ6nT8nXFHLBXN6yVzPkand2NQb0yeGmxklKRRIrtJT2E91jAOG7mZzzD6RzdcxnH3H1aeMGJiIhIUlNSKrWqrHQ+/Hxrq5hPGhUdwvvvFZso2l4adjiSosxslZktNrMPzGxB2PGEJScHevcO6hQVFEBHdnI71/Iuh5NFAScxh0nM5qYZvcMOVUQaQW2diLQUJaVSq9WFJZSUVjByQOrPJ4114oEDKK90Xl6q3lJpkq9HpiyMCzuQMEydCuec88WQ3SP5Nx8wlmv5JY9xPqNYyl85iawsmDgx3FhFpEnadFsnIi1DSanUqjUVOYo1Orsbw/pk8sL/1oQdikhKyckJlhM1gwceCPZlUMJvuYp/cjQZ7OAbvMJkZrGFHgDcfXd48YqIiEhqqLf6rrRdzyzIx4D/rd7MkjXFYYeTMGbGd8Zmc+drH7Nm8w6ye7SOIk7Sohx41cwceMjdZ8YeNLMpwBSAwYMHhxBe4k2dCg8+WHUN0q/yJrP4AcP5lPuYyjX8km18MdxfvaQiKa/NtXUiEg71lEqtPt+yg95dO9I+vfX9mnxnbDYAcz5YG3IkkqK+7O6HACcAl5rZMbEH3X2mu49z93F9+vQJJ8IEqN4zGk1Iu7CV+5jKm3wdCJLTy7ivSkJqpl5SkVagTbR1IhK+1pdtSMKs2byDAd07hR1Gsxic1ZlDBvfgzxrCK43g7msjXzcALwCHhRtRYkULGJ1zDuTmVj32DV5hCaO5mAf5LVcxhkW8zVf3uMfFF6uXVCTVtfa2TkSSh5JSqdHnW3ZSvLOcQb06hx1Ks/nuwdl8tH4rS9ZsCTsUSSFmlmlmXaPfA98AloQbVeLk5MCUKVXXHAXozmZmcRGvcDwldObL/Iur+S07qNpGZGXB7Nlw//0tGLSIJFxrb+tEJLloTqnUaGH+ZgAG9my9SelJBw3g1peW8+yCPEZndw87HEkd/YAXImv3tgOedPeXww0pca6/HkpKqu47iTk8yMX0ZQO3cy23cCO7+GIUhVnQM6pEVKRVadVtXbKYMWMGK1asCDuMhIr+PNNjF7BOccOHD+fyyy8PO4xWTUmp1Ghh3mbSDPq3suG7T767usr2/nt15ZkF+Qzr04XzjhoaTlCSUtx9JXBQ2HE0l9Ux/4tksYm7mc5EnmQhYziJF3mfQ6ucP2QI3HabhuqKtDatva2T5pORoQKS0nBKSqVGC/M3s1f3Tq2yyFGscUN6sSh/C8vWtZ7qwiJNMXgw5OY6p/Ec93EpPdjMjdzML7mGMjoA6hkVEUkU9b6JBFp3xiGNUlnpLMrb0qqH7kYN65NJj87teW9VUdihiCSF3/7oc15IP41nOZ1chnAo73ErN1KRFiSkQ4bAE08oIRUREZHEUVIqe/isYDtbd5UzsA2s35lmxqGDe7Ji4zZyC7aHHY5IeNxh9mxOvXEUJ6W9xC97/JKjeIetQw5k9myoqAhOWbVKQ3VFREQksZSUyh4W5kWKHLXiyruxxg3tRZrtOd9UpM3Iz4eTToJJk2C//Uhf9AHXFP2EMm+nJFRERESanZJS2cPCvM107pBO364dww6lRXTPaM8B/bvx9II8dpZVhB2OSMtxh1mzYNQo+Pvf4Xe/g3/8A/bfP+zIREQkRRUUFDBt2jQKqq8tJlIHJaWyh/mrihg7qAdpQRn4NuGIYVlsLinjpUXrwg5FpGWsWgXHHQeTJ8Mhh8DixeT0ns7QfdJJS4OhQ4M1S0VERBrioYceYtGiRcycOTPsUCSFxJWUmtnxZvaRma0ws2tqOH6VmS0zs0VmNs/MhiQ+VGkJxTvLWP55MV8a2ivsUFrUsN6ZDOuTyR/eWYW7hx2OSPOprIR774XRo+Hdd/+/vXuPj6q+8z/++mQmdwiXAJFbCAooiFcQq1a3CtjUulqttlgsaLsPut16WX/1125/+NNtLX1s1193t1btSm0V1th6ay1WRQG13qqCXAVUAiYYQAiRWwgQknx+f5wDDpGQCYScmfB+Ph7zmDlnzpx5zzA5zGe+3/P9wq9/Tdm35tHr7BO49lqorAwaUCsrYcoUFaYiIpK8mpoa5s6dC8CcOXPUWipJa7UoNbMYcC/wJWAEcI2ZjWi22SJgtLufCjwB/Ht7B5WO8U7lFtxhzOBjqyg1M647t4QlVdt4p1Ij8UontWoVfOELcOON8PnPw/LllHX9R6b8YwYH+95QVwdTp3Z4ShERSVP3338/TU1NADQ1Nam1VJKWTEvpGKDc3de4ez3wB+DyxA3c/SV3rwsX3wQGtG9MOVoeeWvtAZeHXq8gw2DVxtqoo3W4q0YNoHteJve/sibqKCLtq7ERfvELOPVUWLYMHnwQnnsOiouZOjUoPluyVuN/iYhIkubNm3fA8r5WU5HWJFOU9pjbABAAACAASURBVAc+SliuCte15NvAcwe7w8ymmNkCM1tQXV2dfErpMBU1O+nfPZes+LF3unFeVpxvfm4Qc1duZHX1sVeUSye1YgWcey7ceitcfDEsXw7XXQfhOeOtFZ3FxUc/ooiIdA7WbDyS5ssiLUmm8jjYp+mgJ92Z2bXAaOCug93v7tPdfbS7j+7du3fyKaVD7G1somrLLkoK86OOEplJ55SQGcvggVc/jDqKyJHZuxemTYMzzoDVq+GRR+Cpp6Bfv/2blJVBxiH+F8jLC3YhIiKSjLFjxx5yWaQlyRSlVcDAhOUBwPrmG5nZOGAqcJm772mfeNKRqrbsorHJKel17Balvbtm89Uz+/Pkwio21+pjLGlq8WIYMwZuuw2+8pWgtfSaa/a3jkJQkE6ZEvTsPZjCQpg+XXOUiohI8q6++upDLou0JJmidD4w1MwGm1kWMAGYlbiBmZ0B3E9QkG5q/5jSET7cvBOAQT3zIk4SrX84/3jqG5qY+UZF1FFE2mbPHrj9djjrLNiwAZ58Eh59FPr0+cymLZ1LGovBww/D5s0qSEVEpG1mzZq1v8uumfH0009HnEjSRatFqbs3ADcAzwMrgcfcfbmZ/cTMLgs3uwvoAjxuZovNbFYLu5MUtrq6ln7dcsjLjkcdJRL7Bnt6a80nDD+uK7959UMeer0i6lgiyXn7bRg1Cu68E77xjaB19MorW9y8pXNJm5pUjIqIyOGZO3fu/qn13J05c+ZEnEjSRVKj2bj7s+4+zN1PcPdp4brb3X1WeHucuxe5++nh5bJD71FSzZ6GRtbW1DGkT9eoo6SE84f2ZtfeRt5Zq+lhJMXt2gU/+AGccw5s3QrPPAMzZkDPQ0/r1NIARhrYSEREDte4ceOIx4PGjXg8zvjx4yNOJOni2BtiVQ7qw807aXRnSJ8uUUdJCYMK8yjumcerH1RT39AUdRyRg3vtNTjtNLjrLvj2t4ORdS+55JAPKSuDkhKorDzgFFNAAxuJiMiRmTx58gHzlE6aNCniRJIuVJQKAKs21ZIZMwYVHtvnk+5jZlx4Yh+27trLHxdWRR1H5EC1tXDTTXDBBcEou3PmBKMSdet2yIftG9yosjJYdv+0MB00SAMbiYiISDRUlAoA5RtrGdwrn8yYPhL7DCvqQv/uudzzUjl7G9VaKinixRfh1FPhV7+CG26AZctg3LikHnqwwY3cg4K0okIFqYiIHJkZM2YcMNDRzJkzI04k6UIViLC1rp7q2j06n7QZM2PsSX2o2rKLPy1cF3UcOdZt2wbf+Q6MHQvxOLzyCtx9N3RJvst9S4MbtbReRESkLebOnUtjONdYY2OjBjqSpKkoFVZtqgXQ+aQHceJxXRnZv4B7XiqnQa2lEpXnnoORI+GBB+DWW4N5SM8/v8270eBGIiJyNGmgIzlcKkqFFeu30yMvk6Ku2VFHSTlmxk0XDWXtJ3U8tXh91HHkWPPJJzB5cjB4UUEBvPFGMKhR3uGd+z1t2mcfqsGNRESkvUyePJmMjKC8iMViGuhIkqai9Bi3Z28jq6trGdG3YP85AHKg8SOKGNG3gHteXKXWUuk4Tz0FJ58cjE50222wcCGcffYR7XLixGAwo0GDggGONLiRiIi0p8LCQkpLSzEzSktLKSwsjDqSpAkVpce4DzbV0tDkjOh36FE7j2Vmxk1jh1JRU8ef1VoqR1t1NUyYAFdcAccdB/Pnw513Qnb79GSYODEY1KipSYMbiYhI+5s8eTKnnHKKWkmlTVSUHuOWr99GXlZMU8G04uKwtfSX81ZpJF45eh59FEaMgD/+MShE334bzjgj6lQiIiJJ27JlC6tXr2bLli1RR5E0oqL0GFbf0MT7H+9geN8CMtR195AyMoxbvziMtZ/U8diCj6KOI53VW2/B4MGwaFHQZTczs827KCuDkhLIyAiu/+mfDlwuK2vnzCIiIgl++tOfsnPnTn76059GHUXSSDzqABKd18s3s6ehiZP7FkQdJS1ceGIfRg3qwd3zVvHVMweQkxmLOpJ0Nj/7WTDdS/zwDs1lZTBlyqdzkVZWwq9//en9lZXB/aBuuyIi0v7Ky8upqKgAoKKigvLycoYMGRJtKEkLaik9hj3xThV5WTGGFGkqmGSYGbdefCIbt+/h4Tcro44jnVFOzmEXpABTp35akLakri7YTkREpL01bx1Va6kkS0XpMWprXT1zVmzktIHdiWfoY5Csc04o5Pyhvbjv5dXU7mmIOo7IAdaubd/tRERE2mJfK2lLyyItUTVyjJq1ZD31jU2MKu4RdZS0c+vFJ/LJznp+99qHUUcROUBxcftuJyIi0hYlJSWHXBZpiYrSY9TjC6oY0beAft1zo46Sdk4b2J2LRxTxm1fWsGVnfdRxJAJmFjOzRWb2l6izJJo2DfJaGUg7Ly/YTkSkNal6rJPUddtttx1yWaQlKkqPQcuqtrFs3TauGjUg6ihp4ZG31n7mMrxvAbV7GvjlvFVRx5No3AysjDpEcxMnwvTpMGgQmAXX3/3ugcvTp2uQIxFJWkoe6yR1DRkyZH/raElJiQY5kqQlVZSaWamZvW9m5Wb2Lwe5/wIzW2hmDWZ2VfvHlPZ070vldM2Jc9VoFaWHq6ggh7MG9+ThNytZXV0bdRzpQGY2APgy8EDUWQ5m4kSoqICmpuD6vvsOXFZBKiLJSPVjnaSu2267jfz8fLWSSpu0WpSaWQy4F/gSMAK4xsxGNNtsLXAd8Eh7B5T2tWrjDmYv/5jrzy2hIKftcyDKp8YNLyI3M8bPntGPyMeY/wJ+ADS1tIGZTTGzBWa2oLq6uuOSiYi0Hx3r5LAMGTKEZ555Rq2k0ibJtJSOAcrdfY271wN/AC5P3MDdK9x9KYc4cElquO/l1eRlxbj+vMFRR0l7XbLj3HDREOa9t4m5KzZGHUc6gJldCmxy93cOtZ27T3f30e4+unfv3h2UTkSkfehYJ0eipqaGm266iZqamqijSBpJpijtD3yUsFwVrmsz/aIWraVVW/nz4nVMPLuYHvlZUcfpFK4/bzAnFnXl9j+/y05NEXMsOA+4zMwqCH6gu8jMHu7oEGVlUFICGRnBdVlZRycQkU4uJY51kp5mzJjBsmXLmDlzZtRRJI0kU5TaQdb54TyZflGLzp6GRm59fAl9uuZww0VDo47TaWTFM/jZlaewYftufvHCB1HHkaPM3X/k7gPcvQSYALzo7td2ZIayMpgyBSorwT24njJFhamItJ9UONZJeqqpqWH27Nm4O7Nnz1ZrqSQtnsQ2VcDAhOUBwPqjE0eOxCNvrT3o+m+cXczd81bxwcZaHrz+LLrl6lzS9rLvPR9T0pMHX/8Qd2doUVcgeN9F2tvUqVBXd+C6urpgvQYxEhGRKM2YMYOmpuBsvsbGRmbOnMktt9wScSpJB8m0lM4HhprZYDPLIvjFbNbRjSXt6eE3K7n3pdV8bfQALjyxT9RxOqUvjexLn4JsHl3wEdt27Y06jnQAd3/Z3S/t6Odde/DfnlpcLyJyJKI61kl6mjt3Lg0NwelMDQ0NzJkzJ+JEki5aLUrdvQG4AXieYK6qx9x9uZn9xMwuAzCzs8ysCrgauN/Mlh/N0JKcJndeXVXNbU+9y9iT+nDnV0ZGHanTyopncM2YYhoanbK3KtmztzHqSNJJFbfQAN/SehERkY4ybtw44vGgI2Y8Hmf8+PERJ5J0kUz3Xdz9WeDZZutuT7g9n6Bbr6SIDzfv5C9L17Nh224uHlHEr75xBtnxWNSxOrU+XXP42ugBPPL2Wh58o4KrzxpIl+zP/om11M0a1OVXWjdtWnAOaWIX3ry8YL2IiEiUJk+ezOzZswGIxWJMmjQp4kSSLpLpvitpZGtdPb9/ey2/eXUNdfWNTDhrIPd/c5QK0g4yol83vn5WMVVb6vjqfW/w7rptUUeSTmbiRJg+HQYNArPgevp0nU8qIiLRKywspLS0FDOjtLSUwsLCqCNJmkiqpVRS3+69jcx7byOvfFCNO1x0Uh8uGNqbrHgGZgcbQFmOllP6dyM7XsKzyzbwlXtf58oz+3PlmQM4s7gHWXH9DiRHbuJEFaEiIpKaJk+eTEVFhVpJpU1UlHYC5ZtqueGRhbz38Q5G9u/Gl0YeR488zUMapWFFXfnncUO56/n3eWrROh5bUEUswxjYIxczo0t2nC45cbrnZlLcM4/+3XOJxw6vYFV3YBEREUkVhYWF3H333VHHkDSjojTNvfjeRr5XtojcrBiTzxnEiccVRB1JQt3zsph2xSlM/fJwXn6/mpUbtrNm806Wr9vGhm272LGxgT0NwbDp2fEMTh3QnVMHdGNk/24RJxcRERER6TgqStPYc8s2cOPvFzGiXwG/mTSaeSs3RR1JDiIvK84lp/TlklP6Age2bNbuaWBtzU5WbNjO4o+2cOmvXmPc8D7cPHYYpwxQcSoiIiIinZ+K0jT11KJ1fP/xJZw+sDsPXn8WBTmZUUeSw9AlO86Ift0Y0a8bXz6lH39bU8Nr5dXMXbmJ4cd1ZezwIvp1zwXUFVdERERSX01NDT/+8Y+54447NNCRJE2jrqShR+ev5ZbHFjOmpCczvzVGBWknkZsV46KT+vCDL57E2OF9+LBmJ/e8VM7Db1ayZnMtTU0edUQ5isrKoKQEMjKC67KyqBOJiIi03YwZM1i2bBkzZ86MOoqkEbWUppkbf7+Ip5esZ1hRF0pHHsefF69v9TGHGghHjp7Dfd9zMmOMPamIc4/vxRurN/P66s2s2LCdZ5du4PNDezFqUA+G9OnKCb3z6a4BrTqFsrID5x6trAyWQaPsiohI+qipqWH27Nm4O7Nnz2bSpElqLZWkqChNI9NfWc3TS9YzvG8B15w18LBHa5X0kJsVY+zwIs4f2puVG7bzSV09L6zYyGMLqvZv0yMvk265mRT3zOeE3vkc37sLsQxNAZRupk79tCDdp64uWK+iVERE0sWMGTNoagoGcWxsbGTmzJnccsstEaeSdKCiNA00NDbxk7+sYObfKjmlfze+NnqgCo9jSFY8g9MGducbZxfT1ORUflLH6k21fLh5J2s27+S1VdW8Xr6ZV1ZVk58dZ1RxDz4/tBddsvXnnS7WttCo3tJ6ERGRVDR37lwaGhoAaGhoYM6cOSpKJSn61pri1m/dxf9+Ygmvl9fwnQuOZ2DPPDJMBemxKiPDGNwrn8G98veve+SttdQ3NLFq0w4Wrd3Kq6uqeXNNDZ87vpDSkcfRM//gXXw1v2nqKC4OuuwebL2IiEi6GDduHM8++ywNDQ3E43HGjx8fdSRJE+r/maL2NDQy440Kvvifr7Cwciv//tVT+dElw1WQykFlxTM4uV83rv3cIG4eN5ST+nbl1VXVnP/zF7nr+ffYWlcfdUQ5hGnTIC/vwHV5ecF6ERGRdDF58mQyMoLyIhaLMWnSpIgTSbpQS2mK+eiTOmYtWc///K2Sj7fv5pzjC/n5V0+luDCv9QeLAH265jDhrGIuPHE3q6true/l1cx4o5KrRg3gG2cXM6yoa9QRpZl9541OnRp02S0uDgpSnU8qIiLppLCwkNLSUp5++mlKS0s1yJEkTUVpOzjcbpBNTU5FzU5WbNjOko+28lp5DSs3bAfg+F75fOu8wZzQO5/XyjdDebvHlk6uqCCHW8YP48aPd3Dfy+U88tZaHnqjgkGFeZx7QiHbdjVQmJ9FYX4WPfKzyNTAWZGaOFFFqIiIpL/JkydTUVGhVlJpExWl7WBPQyO76hvZvbeJXXsbqW9opLHJaWhy8rJi1Dc2sbexia11e9m0fTcbt+9hw/bdrNq4g7r6RgCyYhmMGtSDH5aeRFOT06OF8wBF2urE47ryywlncMff1/OXpet55YNqnlm6ge27G/ZvY0BBbia9u2TTr3sufbvlcM4JheRkxqILLiIiImmnsLCQu+++O+oYkmZUlLbC3flkZz3rt+5m3dY61m3dzbotu1i3tY6qLbuo2rKLbbv2tvj4P8z/6IDlgpw4fQpyKCrI5mujBzKiXwEj+hYwtKgL2fGgANC8onI09MzPYtI5JUw6pwR357evfkjNzvrwsodPauvZuGP3/pF8czIzOO+EXlw0vA/jhhdRVJAT9UsQERERkU4oqaLUzEqBXwIx4AF3/7dm92cDM4FRQA3wdXevaN+oR2ZXfSObduxm0449PPlOFbV7GsJWzSbqG5rYs/+68YDlnfUN7G30A/aVmxljQI9cBvTIZdSgHmzavofcrBg5mTFyM2NkxzOIZdiBFzNys2Kf6SLZ0OgsrdrG0qptHfl2SBpqzx8rzIy87Dh52XEG9jzwfOW9jU0M7pXPi+9tYt57G5n33iam/uldThvYnYtHFDFueBHDirpgGnRLRERERNpBq0WpmcWAe4HxQBUw38xmufuKhM2+DWxx9yFmNgH4OfD19g7r7uxtdBqammhocvY2NFG7p4Ftu/aybdfeoHvsjj1s2h4UnxsTrnckdFVMFM8wsuMZZMUzyI7HyIpnkJMZoyA3k+x4BnlZcbrnZdI9N5MJY4rp3z2X7nmZB3whV8umdCaZsQwuGNabC4b15o6/H8GqTbXMWbGRF1Zs5K7n3+eu59+nZ34WZwzszuBe+QwqzKO4MJ/jCnLIz46RnxUnPztOZsxUuIqIiIhIq5JpKR0DlLv7GgAz+wNwOZBYlF4O/Gt4+wngHjMzdz+wifEwTfrd27xevpnGpuR2lxXPoE/XbIoKchjapwufH9KL3uFyn67ZLKjcQtfsODmZMWIZyX9pHtm/2+G+BJG0ZGYMK+rKsKKufO/CIXy8bTd//WAT8yu2sLRqK6+v3szuvU0tPj6WYXz/4mH80xeGdGBqEREREUkn1lrdaGZXAaXu/g/h8jeBs939hoRt3g23qQqXV4fbbG62rynAlHDxROD9JDL2Aja3ulXHU662S9VsqZoLUjfb0cw1yN17H6V9dzgzqwYq2/CQVP0330f5Dl8qZwPlO1JtzXesH+ukc0v1v1fpOEkd65JpKT1YU2LzSjaZbXD36cD0JJ7z0x2bLXD30W15TEdQrrZL1WypmgtSN1uq5kpFbf3SmervrfIdvlTOBsp3pFI939HWmQpsOXLH+t+DtF0yExNWAQMTlgcA61vaxsziQDfgk/YIKCIiIiIiIp1XMkXpfGComQ02syxgAjCr2TazgMnh7auAF9vrfFIRERERERHpvFrtvuvuDWZ2A/A8wZQwv3P35Wb2E2CBu88Cfgv8j5mVE7SQTmjHjG3q7tuBlKvtUjVbquaC1M2Wqrk6g1R/b5Xv8KVyNlC+I5Xq+UQ6kv4epE1aHehIRERERERE5GhJpvuuiIiIiIiIyFGholREREREREQik5JFqZnlmNnbZrbEzJab2Y+jzpTIzGJmtsjM/hJ1lkRmVmFmy8xssZktiDrPPmbW3cyeMLP3zGylmZ0TdSYAMzsxfK/2Xbab2T9HnQvAzG4JP/vvmtnvzSwn6kwAZnZzmGl5qrxXnZGZ3WlmS8PP5Qtm1i/qTInM7K7w73mpmf3JzLpHnWkfM7s6/Hw2mVnKTEdgZqVm9r6ZlZvZv0SdJ5GZ/c7MNoVzjqccMxtoZi+F/38sN7Obo86UKNW/s4iIpIOUPKfUzAzId/daM8sEXgNudvc3I44GgJn9L2A0UODul0adZx8zqwBGu3tKTVZsZjOAV939gXAE5zx33xp1rkRmFgPWAWe7e6STf5tZf4LP/Ah332VmjwHPuvtDEecaCfwBGAPUA7OB77r7qihzdUZmVuDu28PbNxF8Fv4x4lj7mdnFBKOsN5jZzwHc/YcRxwLAzIYDTcD9wK3uHvkPdOHx5QNgPMEUavOBa9x9RaTBQmZ2AVALzHT3kVHnac7M+gJ93X2hmXUF3gG+kkLvX0p/ZxERSQcp2VLqgdpwMTO8pET1bGYDgC8DD0SdJR2YWQFwAcEIzbh7faoVpKGxwOqoC9IEcSA3nPc3j8/ODRyF4cCb7l7n7g3AX4ErIs7UKe0rSEP5pMjxbx93fyH8DAC8STB/dUpw95Xu/n7UOZoZA5S7+xp3ryf4cefyiDPt5+6vkMJzi7v7BndfGN7eAawE+keb6lOp/J1FRCRdpGRRCvu7yC4GNgFz3P2tqDOF/gv4AcEv8anGgRfM7B0zmxJ1mNDxQDXwYNjl+QEzy4861EFMAH4fdQgAd18H/D9gLbAB2ObuL0SbCoB3gQvMrNDM8oBLgIERZ+q0zGyamX0ETARujzrPIXwLeC7qECmuP/BRwnIVKVRUpRMzKwHOAFLlOwGQ0t9ZRETSQsoWpe7e6O6nE/wCPybsOhgpM7sU2OTu70SdpQXnufuZwJeA74VdsqIWB84Efu3uZwA7gVQ7nyoLuAx4POosAGbWg6AVZTDQD8g3s2ujTRW0QAE/B+YQdN1dAjQc8kHSIjObG56f2/xyOYC7T3X3gUAZcEOq5Qu3mUrwGShLtWwpxg6yTi1pbWRmXYAngX9u1psgcqn4nUVEJJ3Eow7QGnffamYvA6UELTVROg+4zMwuAXKAAjN72N0jLxgA3H19eL3JzP5E0GXslWhTUQVUJfxq/AQpVpQSFPEL3X1j1EFC44AP3b0awMz+CJwLPBxpKsDdf0vYFdvMfkbw7yuHwd3HJbnpI8AzwB1HMc5ntJbPzCYDlwJjvYMHJ2jDe5cqqjiwV8EAUqNLftoIz9V8Eihz9z9GnaclKfadRUQkbaRkS6mZ9d43mqOZ5RJ8SX8v2lTg7j9y9wHuXkLQ3fPFVClIzSw/HACCsHvsxaTAf4ju/jHwkZmdGK4aC6TE4BQJriFFuu6G1gKfM7O8cACNsQTnUEXOzPqE18XAlaTW+9ZpmNnQhMXLSIHjXyIzKwV+CFzm7nVR50kD84GhZjY47JkxAZgVcaa0ER4HfwusdPf/iDpPc6n6nUVEJJ2kaktpX2BGOGJhBvCYu6fU9CspqAj4U/B/N3HgEXefHW2k/W4EysIvY2uA6yPOs194buR44DtRZ9nH3d8ysyeAhQRdIxcB06NNtd+TZlYI7AW+5+5bog7USf1b+ENOE1AJpMzIu6F7gGxgTnjMeTNVRgc2syuAXwG9gWfMbLG7fzHKTOEoxTcAzwMx4HfuvjzKTInM7PfAF4BeZlYF3BH2ikgV5wHfBJaF520C/B93fzbCTIn0nUVE5Ail5JQwIiIiIiIicmxIye67IiIiIiIicmxQUSoiIiIiIiKRUVEqIiIiIiIikVFRKiIiIiIiIpFRUSoiIiIiIiKRUVEqHcbMGs1ssZm9a2ZPJ8zrVmJmbmZ3Jmzby8z2mtk90SUWkSiZWWF4zFhsZh+b2brw9lYz69D5js3sdDO7JGH5MjP7l8PcV4WZ9Wq/dG167uvMrF/C8gNmNiLqXCIicmxTUSodaZe7n+7uI4FPgO8l3LcGuDRh+WogZebxE5GO5+414THjdOC/gf8Mb59OMIdruzKzQ83dfTqwvyh191nu/m/tnaEDXAfsL0rd/R/cvUMLfBERkeZUlEpU/gb0T1jeBaw0s9Hh8teBxzo8lYiki5iZ/cbMlpvZC2aWC2BmJ5jZbDN7x8xeNbOTwvWDzGyemS0Nr4vD9Q+Z2X+Y2UvAz80s38x+Z2bzzWyRmV1uZlnAT4Cvhy21Xw9bHO8J91FkZn8ysyXh5dxw/VNhjuVmNqW1F2Rm15vZB2b21/C17dv/Q2Z2VcJ2teF1l/C1LDSzZWZ2ebi+xMxWNn9/wn2MBsrC15FrZi8nHHcTs1xrZm+H291vZrHw8lDY22WZmd1yBP9+IiIi+6kolQ5nZjFgLDCr2V1/ACaY2QCgEVjf0dlEJG0MBe5195OBrcBXw/XTgRvdfRRwK3BfuP4eYKa7nwqUAXcn7GsYMM7dvw9MBV5097OAC4G7gEzgduDRsOX20WZZ7gb+6u6nAWfyaS+Pb4U5RgM3mVlhSy/GzPoCPwbOA8YDI5J4D3YDV7j7mWHWX5iZtfT+uPsTwAJgYvg6drWQZTjBD4PnhS3TjcBEgtbi/u4+0t1PAR5MIqOIiEirDtVVSaS95ZrZYqAEeAeY0+z+2cCdwEag+Zc+EZFEH7r74vD2O0CJmXUBzgUe/7Q2Izu8Pge4Mrz9P8C/J+zrcXdvDG9fDFxmZreGyzlAcStZLgImAYT72Rauv8nMrghvDyQoFGta2MfZwMvuXg1gZo8SFMuHYsDPzOwCgu7M/YGi8L7PvD+t7CvRWGAUMD98H3OBTcDTwPFm9ivgGeCFNuxTRESkRSpKpSPtcvfTzawb8BeCc0r3t1a4e72ZvQN8HzgZ+PtoYopIGtiTcLuRoHDKALaGrXut8YTbOxNuG0Gr4vuJG5vZ2W0JZ2ZfAMYB57h7nZm9TFDgJpspUQNhz6awJTQrXD8R6A2Mcve9ZlaR8BwHe3+Sjg/McPcffeYOs9OALxIcv78GfKsN+xURETkodd+VDufu24CbgFvNLLPZ3b8AfujuLbUmiIgclLtvBz40s6shKODCIgrgDWBCeHsi8FoLu3keuHFfN1gzOyNcvwPo2sJj5gHfDbePmVkB0A3YEhakJwGfayX+W8AXLBhxOJNgsLd9KghaLgEuJ+hOTPgcm8KC9EJgUCvP0drrSHw9V5lZn/A19QzPye0FZLj7k8D/JeiqLCIicsRUlEok3H0RsIRPvyTuW7/c3WdEk0pEOoGJwLfNbAnBuZ2Xh+tvAq43s6XAN4GbW3j8nQRF31IzezdcBngJGLFvoKNmj7kZuNDMlhF0lT2Z4HSEePh8dwJvHiq0u28A/pVgELi5wMKEu38D/J2ZvU3QzXdfy24ZMNrMFoSv+71DPUfoIeC/9w101EKWFcBtwAth/jlAX4LuwS+Hp2E8BHymJVVERORwmHtLvYVEREQkCmZ2HTDa3W+IOouIiMjRppZSERERERERiYxaSkVERERERCQyaikVERERERGRyKgoFRERERERkcioLdRr5wAAACNJREFUKBUREREREZHIqCgVERERERGRyKgoFRERERERkcj8f8cZFLqpfI97AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's start with the variable RM from the\n",
    "# boston house dataset.\n",
    "# RM is the average number of rooms per dwelling\n",
    "\n",
    "diagnostic_plots(boston, 'RM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram and the Q-Q plot, we see that the variable rm approximates a Gaussian distribution quite well. In the boxplot, we see that the variable could have outliers, as there are many dots sitting outside the whiskers, at both tails of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's inspect now the variable Age from the titanic\n",
    "# refers to the age of the passengers on board\n",
    "\n",
    "diagnostic_plots(titanic, 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram and the Q-Q plot, we see that the variable approximates fairly well a Gaussian distribution. There is a deviation from the distribution towards the smaller values of age. In the boxplot, we can see that the variable could have outliers, as there are many dots sitting outside the whiskers, at the right end of the distribution (top whisker in the boxplot).\n",
    "\n",
    "### Skewed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variable LSTAT from the boston house dataset\n",
    "# LSTAT is the % lower status of the population\n",
    "\n",
    "diagnostic_plots(boston, 'LSTAT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTAT is not normally distributed, it is skewed with a tail to the right. According to the boxplot, there are some outliers at the right end of the distribution of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variable CRIM from the boston house dataset\n",
    "# CRIM is the per capita crime rate by town\n",
    "\n",
    "diagnostic_plots(boston, 'CRIM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CRIM is heavily skewed, with a tail to the right. There seems to be quite a few outliers as well at the right end of the distribution, according to the boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lix2ao/wNYEKO8gA+2wtNMzPrTnsB90mC5PfijyPiV5KeAu6VdB7wCjAlrf8gcALJQ7tNwLm932QzKydlH8hu3uqMrJmZmVk2P7Qzs2JX9l2LN7lrsZmZmZmZWUkp+0C2vkVG1l2LzczMzMzMil3ZB7ItxshudiBrZmZmZmZW7Mo+kN3srsVmZmZmZmYlxYFsdtdiryNrZmZmZmZW9Mo+kM10Ld5jlxqPkTUzMzMzMysBZR/Ibm5ooqaqgiEDqlnvrsVmZmZmZmZFz4Hs1kYGVFeya/8qj5E1MzMzMzMrAQ5kG5oYWFPJrv2rPWuxmZmZmZlZCSj7QHbT1iYGVFcyeECVx8iamZmZmZmVgLIPZOsbmhhQU8mu/ardtdjMzMzMzKwElH0gm8nIeoysmZmZmZlZaagqdAMK5cdPvgLA0jWb6F9dyd/feIvNDU3c+YeXOeu9+xW4dWZmZmZmZtaWvDKykiZKelFSnaRLchzvJ+me9PiTksak5UdKeiZ9/VnSafles7c0NAXVlRX0r6oEYEtDU6GaYmZmZmZmZnnoMJCVVAl8DzgeOAg4U9JBraqdB6yNiLHADcD1aflzwLiIOAyYCPxAUlWe1+wVW5uaqamqYEB1EshudiBrZmZmZmZW1PLJyB4J1EXE4ojYCtwNTGpVZxIwK92eA0yQpIjYFBGZgaf9gejENXtFQ2Mz1ZWif3Xyp6hvbC5EM8zMzMzMzCxP+QSyI4GlWfvL0rKcddLAdR0wFEDSUZIWAX8BPpMez+eapOdPk7RA0oJVq1bl0dzO2drUTE1lBf3SjGy9M7JmZmZmZmZFLZ9AVjnKIt86EfFkRBwM/CNwqaT+eV6T9PyZETEuIsYNHz48j+bmLyJoaGpOxshWe4ysmZmZmZlZKcgnkF0G7Ju1PwpY3lYdSVXAbsCa7AoR8QLwFnBIntfscU3NQXNATVUF/avSrsUN7lpsZmZmZmZWzPIJZJ8CDpS0v6Qa4Axgbqs6c4Fz0u3JwLyIiPScKgBJ+wH/ACzJ85o9rqEpSQJnZ2TrG52RNTMzMzMzK2YdriMbEY2SLgIeAiqB2yJikaRrgAURMRe4FbhTUh1JJvaM9PR/Ai6R1AA0AxdGxGqAXNfs5u/Woa1NSfa1JiuQ9azFZmZmZmZmxa3DQBYgIh4EHmxVdmXWdj0wJcd5dwJ35nvN3taQzlBcXSUqK0R1pdjirsVmZmZmZmZFLZ+uxX1WdkYWoKqigsZmB7JmZmZmZmbFrKwD2YamTEY2+TNUV2rbuFkzs7ZIqpT0J0k/T/f3l/SkpJck3ZOO/UdSv3S/Lj0+ppDtNjMzM+sryjqQ3drYKiNbWbEtuDUza8fFwAtZ+9cDN0TEgcBa4Ly0/DxgbUSMBW5I65mZmZlZF5V1ILstI1u5PSPb6IysmbVD0ijgROCH6b6A8cCctMos4NR0e1K6T3p8QlrfzMzMzLqgrAPZ1mNkqys9RtbMOvQt4MskM7EDDAXejIjGdH8ZMDLdHgkshWQGeGBdWn8HkqZJWiBpwapVq3qq7WZmZmZ9QnkHso3pOrJVmcmePEbWzNom6SRgZUQszC7OUTXyONayMGJmRIyLiHHDhw/vYkvNzMzM+ra8lt/pqxpyZGTrvY6smbXt/cApkk4A+gODSTK0QyRVpVnXUcDytP4yYF9gmaQqYDeStbbNzMzMrAvKOyPbtH0dWUgyso3NzsiaWW4RcWlEjIqIMcAZwLyImArMByan1c4BHki356b7pMfnRYRvMmZWMjxLu5kVq7IOZBsam6kQVKZzr3jWYjPbSV8BviCpjmQM7K1p+a3A0LT8C8AlBWqfmdnO8iztZlaUyjqQ3drUTHVlBZlJRD1rsZnlKyIejYiT0u3FEXFkRIyNiCkRsSUtr0/3x6bHFxe21WZm+fMs7WZWzMo6kG1oat42PhackTUzMzPL0iOztJuZdYeyDmS3NjZvm7EYoLpCNHiMrJmZmZW5npql3UuNmVl3KetAtqEpdsjINjoja2ZmZpaZpX0JcDdJl+Jts7SndXLN0k57s7R7qTEz6y5lHsg2U1W5/QFidaVoDhzMmpmZWVnzLO1mVuzKOpBtbA6qKrYHslUVyZ9jS6MDWTMzM7McPEu7mRWFqo6r9F3NzbFDRhagvqGJXfqV9Z/GzMzMDEhmaQceTbcXA0fmqFMPTOnVhplZWSvrjGxTBJXZGdlKZ2TNzMzMzMyKXXkHss1BZUXWrMVZGVkzMzMzMzMrTmUdyDY2B1k9i7eNka1vcEbWzMzMzMysWJV1IJtkZHccI7ul0RlZMzMzMzOzYlXWgWxzq67FmTGyzsiamZmZmZkVr7wCWUkTJb0oqU7SDtOpS+on6Z70+JOSxqTlx0laKOkv6fv4rHMeTa/5TPras7u+VL52yMhWOCNrZmZmZmZW7DpcY0ZSJfA94DhgGfCUpLkR8XxWtfOAtRExVtIZwPXAx4DVwMkRsVzSIcBDwMis86ZGxIJu+i6d1tice9ZiZ2TNzMzMzMyKVz4Z2SOBuohYHBFbgbuBSa3qTAJmpdtzgAmSFBF/iojlafkioL+kft3R8O7QFC0ne6retvyOM7JmZmZmZmbFKp9AdiSwNGt/GS2zqi3qREQjsA4Y2qrO6cCfImJLVtmP0m7FV0gSvaz18jtVmcmenJE1MzMzMzMrWvkEsrkCzOhMHUkHk3Q3/nTW8akR8S7gA+nrrJwfLk2TtEDSglWrVuXR3PxERDrZU/asxc7ImpmZmZmZFbt8AtllwL5Z+6OA5W3VkVQF7AasSfdHAfcBZ0fE3zInRMSr6fsG4MckXZh3EBEzI2JcRIwbPnx4Pt8pL82RRNotxsim2x4ja2ZmZmZmVrzyCWSfAg6UtL+kGuAMYG6rOnOBc9LtycC8iAhJQ4BfAJdGxOOZypKqJA1Lt6uBk4DnuvZVOqepOUkYV+XIyNY3OCNrZmZmZmZWrDoMZNMxrxeRzDj8AnBvRCySdI2kU9JqtwJDJdUBXwAyS/RcBIwFrmi1zE4/4CFJzwLPAK8Ct3TnF+tIJpCtyApkK5T0kd7S6IysmZmZmZlZsepw+R2AiHgQeLBV2ZVZ2/XAlBznTQemt3HZI/JvZvdriiSQze5aLInqygpnZM3MzMzMzIpYPl2L+6RtXYtbTZZcVSlnZM3MzMzMzIpY2Qey2RlZwBlZMzMzMzOzIudAtlUgW1XhjKyZmZmZmVkxK/tAtsIZWTMzMzMzs5JS9oFsVeuMbKWod0bWzNogqb+kP0r6s6RFkq5Oy/eX9KSklyTdky5XhqR+6X5denxMIdtvZmZm1heUbyCbY9ZigKqKCrY4I2tW8iTtIqki3X67pFPSdau7agswPiIOBQ4DJko6GrgeuCEiDgTWAuel9c8D1kbEWOCGtJ6ZmZmZdUH5BrJtTvbkjKxZH/EY0F/SSOAR4Fzg9q5eNBIb093q9BXAeGBOWj4LODXdnpTukx6fILWaLt3MzMzMOsWBrHYcI+uMrFmfoIjYBHwUuDEiTgMO6pYLS5WSngFWAg8DfwPejIjGtMoyYGS6PRJYCpAeXwcM7Y52mJmZmZWrMg5kk6zrDl2LvY6sWV8hSe8FpgK/SMuquuPCEdEUEYcBo4AjgXfmqpZpRzvHtpE0TdICSQtWrVrVHc00MzMz67PKOJBN3nfoWuwxsmZ9xeeBS4H7ImKRpAOA+d35ARHxJvAocDQwRFImUB4FLE+3lwH7AqTHdwPW5LjWzIgYFxHjhg8f3p3NNDMzM+tzyjeQbWuyJ4+RNesTIuK3EXEK8N10f3FE/GtXrytpuKQh6fYA4EPACyRB8uS02jnAA+n23HSf9Pi8iNghI2tmZmZm+SvfQLaNrsUeI2vWN0h6r6TnSYJMJB0q6aZuuPQIYL6kZ4GngIcj4ufAV4AvSKojGQN7a1r/VmBoWv4F4JJuaIOZmZlZWeuW8WKlqK1Zi52RNeszvgV8hCQjSkT8WdIHu3rRiHgWeE+O8sUk42Vbl9cDU7r6uWZmZma2XdlmZBvbCmQrKmhqDhqaHMyalbqIWNqqyN0tzMzMzPqAsg1km9tcfifZ98zFZiVvqaT3ASGpRtIXSbsZm5mZmVlpK9tAtu2uxcmfpN7jZM1K3WeAz5Ks47oMOCzdNzMzM7MS5zGyOyy/44ysWV8QEatJ1pA1M7NOktQfeAzoR/J7cU5EfE3S/sDdwB7A08BZEbFVUj/gDuAI4A3gYxGxpCCNN7OyULYZ2cY2l99xRtaslEm6UdJ32noVun1mVli1tTBmDEhQVZX7fdiw5FVR0XJ7zJjk/DKxBRgfEYeS9GiZKOlo4Hrghog4EFgLnJfWPw9YGxFjgRvSemZmPaasM7IVgoq2xsg2OCNrVqIWFLoBZlacamth2jTYtCnZb2rK/f7GG9vPyd5++eXkfICpfby/R7re9cZ0tzp9BTAe+Je0fBZwFXAzMCndBpgDfFeSvG62mfWUsg1km5tjhyAWknVkAeobnZE1K0URMavQbTCz4nTZZduD2J21aVNynb4eyAJIqgQWAmOB7wF/A96MiMa0yjKSeQhI35cCRESjpHUka2qvbnXNacA0gNGjR/f0VzCzPqxsA9mm5tihWzFAVVrmrsVmpUnStyLi85J+RpI9aCEiTilAs8ysCLzySnFdp9hFRBNwmKQhwH3AO3NVS993/FGV+x48E5gJMG7cOGdrzWyn5RXISpoIfBuoBH4YEde1Op5zgL+k44DrgBpgK/CliJiXnnMEcDswAHgQuLg3u580thHIZjKynuzJrGTdmb7/V0FbYWZFZ/TopHtwd1ynnETEm5IeBY4GhkiqSrOyo4DlabVlwL7AMklVwG7AmkK018zKQ4eTPaXdSr4HHA8cBJwp6aBW1doa4L8aODki3gWcw/YfmJCMp5gGHJi+Jnbhe3RaU3Nsy75mq9o2RtYZWbNSFBEL083DIuK32S+SCUvMrMzU1iYTNnVHEDtwIMyY0fXrFDtJw9NMLJIGAB8iWYt7PjA5rXYO8EC6PTfdJz0+z+Njzawn5TNr8ZFAXUQsjoitJFOuT2pVZxLJgH9IBvhPSAf4/ykiMk/qFgH9JfWTNAIYHBF/SG9ydwCndvnbdEJbXYurK5yRNesjzslR9oneboSZFVZtLZx7bstJmzLSf/KprGz5PnRo8pJabu+3H8ycWR7jY4ERwHxJzwJPAQ9HxM+BrwBfkFRHMgb21rT+rcDQtPwLwCUFaLOZlZF8uhZvG7yfWgYc1Vaddgb4nw78KSK2SBqZXif7miPpRU2Re7KnTEbWY2TNSpOkM0lm1Nxf0tysQ7uSDH0wszJy2WXQ0JD72L77wpIlvdqckhERzwLvyVG+mCTJ0bq8HpjSC00zMwPyC2TzGbzfbh1JB5N0N/5wJ66ZObdHZrdrMyObjpHdvNWBrFmJ+j2wAhgGfDOrfAPwbEFaZGYF097ETOUyaZOZWV+UTyCbGbyfkT2wv3WdHQb4SxpFMtPd2RHxt6z6ozq4JtBzs9u1NUa2f3XSr2jd5sYdjplZ8YuIl4GXgfcWui1mVnjtTfBUbpM2mZn1JfmMkX0KOFDS/pJqgDNIBvRnyznAP50k4BfApRHxeKZyRKwANkg6WpKAs9k+WUCvaCsjW1khdu1fxdpNW3uzOWbWzSR9VNJLktZJWi9pg6T1hW6XmfWuGTOgunrH8pqa8pi0ycysr+owkE2nV78IeIhktrp7I2KRpGskZdZjbGuA/0Uki2hfIemZ9LVneuwC4IdAHckC27/sri+Vj7YCWYDdB9Y4kDUrff8BnBIRu0XE4IjYNSIGF7pRZta7pk6FH/0ombApY+hQuO22spm0CSU+LunKdH+0pB3GuZqZlZJ8MrJExIMR8faIeFtEzEjLroyIuel2fURMiYixEXFkOhEAETE9InaJiMOyXivTYwsi4pD0mhf19hTtTdFOILtLDWs3tTEzhJmVitcj4oVCN8LMel9muR0peX3840n5XXdBBKxeXT5BbOomkuEWZ6b7G0iWVjQruHnz5nHMMccwf/78QjfFSkw+Y2T7pKbmoF9V7jh+94HVrHnLGVmzErdA0j3A/cCWTGFE/LRwTTKznpZZbqf1TMVvvAGf/GSyXWZBLMBREXG4pD+zWh3VAAAgAElEQVQBRMTadLiYWcHNSPv4T58+nWOPPbbArbFSkldGti9qag4qcyy/A+5abNZHDAY2kcyWfnL6OqmgLTKzHtfecjtbtybHy1CDpErSFSIkDQeaC9sksyQb29SUrBTS1NTkrKx1SllnZNvqWjxkYDVr33LXYrNSFhHnFroNZtb7OlpSp0yX3PkOyQoSe0qaQTIx5+WFbZLZ9mxshrOy1hkOZHPYY2ANG7c0srWxmZo2uh+bWXGT1B84DzgY6J8pj4hPFqxRZtbj2ltuJ3O83EREraSFwARAwKmeQ8CKQSYb29a+WXvKNkpLJnvK/fWH7JIMG3lzs7sXm5WwO4G9gY8AvyVZr3pDQVtkZj0ie3Kn9oLYclxyR1KFpOci4q8R8b2I+K6DWDPrC8o3kG0OKtv49rsPTBacc/dis5I2NiKuAN6KiFnAicC7CtwmM+tmmcmd3nij/XrltuRORkQ0A3+WVIa5aCt2w4YNa3ffrD3uWpzD7gOTjKwnfDIraZknUW9KOgR4DRhTuOaYWU9ob3KnjP32gyVLeqU5xWoEsEjSH4G3MoURcUrhmmQG1113Heeff36LfbN8lXcg286sxQBvOpA1K2UzJe0OXAHMBQYBVxa2SWbW3fKZvKlMJ3jKdnWhG2Bm1t3KvGtxG+vI7pJ2Ld7krsVmpSoifhgRayPitxFxQETsGRHf7+p1Je0rab6kFyQtknRxWr6HpIclvZS+756WS9J3JNVJelbS4V1tg5ltl8/kTeU4wVO29D64w6vQ7TKbPn16u/tm7SnvjGwHXYvXvOWMrFmpkpQz+xoR13Tx0o3Av0fE05J2BRZKehj4BPBIRFwn6RLgEuArwPHAgenrKODm9N3MusGMGckY2ba6F5fjBE+tSToauBF4J1ADVJLMHzC4oA2zsrekVZ//1vtm7SnLjGxTcxDQ5mRP/asr6V9d4a7FZqXtraxXE0lAOaarF42IFRHxdLq9AXgBGAlMAmal1WYBp6bbk4A7IvEEMETSiK62w8wSU6fCj36UTObUWrlO8JTDd4EzgZeAAcD5aZlZQY0ZM6bdfbP2lGUg29DUDNBm12JI1pJ112Kz0hUR38x6zQCOIQk4u42kMcB7gCeBvSJiRfrZK4A902ojgaVZpy3L1Q5J0yQtkLRg1apV3dlMsz5v6lRYvRoiWr5Wr3YQmxERdUBlRDRFxI9I7olmBXX55Ze3u2/WnrIMZLduC2Rzdy0GGDKwxhlZs75lIHBAd11M0iDgJ8DnI2J9e1VzlMUOBREzI2JcRIwbPnx4dzXTzAxgk6Qa4BlJ/yHp34BdCt0os7Fjx27Lwo4ZM4axY8cWtkFWUsoykG1o7DiQ3X2Xao+RNSthkv6STq70rKRFwIvAt7vp2tUkQWxtRPw0LX4902U4fV+Zli8D9s06fRSwvDvaYWaWp7NIfvNdRDLcYl/g9IK2yCx12mmnAXD66f5P0jqnLCd7amxOkiFVbSy/A0lGdsWb7SVZzKzInZS13Qi8HhGNXb2oJAG3Ai9ExH9nHZoLnANcl74/kFV+kaS7SSZ5Wpfpgmxm1pMkjY6IVyLi5bSoHi/FY0XmlltuAeAHP/gBJ598coFbY6WkLDOyW9OMbEV7GdmB1ax112KzUrYh67UZGJwukbOHpD26cN33k2Q3xkt6Jn2dQBLAHifpJeC4dB/gQWAxUAfcAlzYhc82M+uM+zMbkn5SyIaY5bJgwQI2btwIwMaNG1m4cGGBW2SlpCwzsg15jJHdY2AN6zY3tLtMj5kVtadJus+tJRmnOgR4JT0W7OR42Yj4HbnHvQJMyFE/gM/uzGeZmXVR9r2q2+YIMOsuV111VYv9r33ta/z85z8vTGOs5JRlRrahKela3NFkT80B6zd75mKzEvUr4OSIGBYRQ0m6Gv80IvaPCP+gM7NyEG1smxWFTDa2rX2z9pRpIJtkZKs6mOwJcPdis9L1jxHxYGYnIn4J/L8CtsfMrLcdKmm9pA3Au9Pt9ZI2SPJEIFZwgwYNanffrD1lHch2lJEFvJasWelaLelySWMk7SfpMuCNQjfKzLpPbS0MGwZS8ho2LCmzRERURsTgiNg1IqrS7cz+4EK3z6x11+Krr/ZcZJa/Mg1kO+5aPHxQPwCWrd3UK20ys253JjAcuC99DU/LzKwPqK2Fc8+FN7IeT73xBnzykw5mzUrFuHHjWuwfccQRBWqJlaK8AllJEyW9KKlO0iU5jveTdE96/ElJY9LyoZLmS9oo6butznk0vWZm1s89u+ML5SOTka1oZ/mdd+y9K8MG1fCr517rrWaZWTeKiDURcXFEvCciDo+Iz0fEmkK3y8y6x2WXQUOOTlNbtybHzKz4zZs3r8X+/PnzC9QSK0UdBrKSKoHvAccDBwFnSjqoVbXzgLURMRa4Abg+La8HrgC+2Mblp0bEYelr5c58gZ2xNY8xslWVFZz07n145K8r2VDv7sVmZmbF5JVXdu6YmRWPa6+9tsX+jBkzCtQSK0X5ZGSPBOoiYnFEbAXuBia1qjMJmJVuzwEmSFJEvJUuVVHfbS3uBg2NHY+RBTj50H3Y2tjMrxe93hvNMjMzszxceCFEO3Pwjh7de20xs53X2NjY7r5Ze/IJZEcCS7P2l6VlOetERCOwDhiax7V/lHYrvkJqp59vN2ts7niMLMDho4cwavcBPPDn5b3RLDMzM2vDhRdCRUUyqdPNN7ddr6YGnNQxKw1VVVXt7pu1J59ANle01/o5aD51WpsaEe8CPpC+zsr54dI0SQskLVi1alWHjc1HPrMWp5/NyYfuw+N1q1nzlpfhMSsFkr7T3qvQ7TOzzvvQh5Lgtb0sLCSB7m23wdSpvdOuTouAZ5+FK6+Egw+G5cX7oFzSvuk8Jy9IWiTp4rR8D0kPS3opfd89LVd6n62T9Kykwwv7DawUfPWrX22xf5kHuFsn5BPILgP2zdofBbS+826rI6kK2A1od1KViHg1fd8A/JikC3OuejMjYlxEjBs+fHgeze3Y1kzX4jySwO9721CamoP/e31Dt3y2mfW4zwD/RHKfWgAsbPUysxJSWwuPPJJf3YgiDGIj4Jln4PLL4R3vgEMPTVLGe+3Vcsrl4tMI/HtEvBM4GvhsOkfKJcAjEXEg8Ei6D8lcKgemr2lAO3lzs8T48eO3ZWGrqqo49thjC9wiKyX5BLJPAQdK2l9SDXAGMLdVnbnAOen2ZGBeRNvPTSVVSRqWblcDJwHPdbbxOyuf5XcyRuzWH4DX1xfVMF8za9sIYCbwEZKeHtXA3IiYFRGz2j3TzIpOZxI0RTM2NgKefhouvRTe/nZ4z3vgG9+AffdNUsvLl8O8efCudxW6pW2KiBUR8XS6vQF4gWQoWfa8KLOAU9PtScAdkXgCGCJpRC8320pQJivrbKx1Vocd0SOiUdJFwENAJXBbRCySdA2wICLmArcCd0qqI8nEnpE5X9ISYDBQI+lU4MPAy8BDaRBbCfwGuKVbv1k78u1aDLDX4CSQfW2dA1mzUhARbwDfB74vaSTJ2rGLJH0lIu4sbOvMrLPynYG4qqrAY2MjYOFCmD0b5syBxYuhshLGj4cvfQlOOw26qWdZb0uXVXwP8CSwV0SsgCTYzVo+sa05VVa0utY0kowto4vmyYMV0vjx4xk/fnyhm2ElKK8R1RHxIPBgq7Irs7brgSltnDumjcsWbMXjzgSyu/avZlC/KlY4kDUrKen4rDOB44Bf4m7FZiWlthY+/emOx8UC9O8PP/xhAboVR8BTT20PXpcsSSLqCROSbOypp8KwYb3cqO4laRDwE+DzEbG+nbk585ovJSJmkvSaYdy4cXn8r2tmlltZTg3Wma7FAHsN7ueuxWYlQtLVJMMVXiBZLuzSdDZ1MysRtbVw1lkdB7FDh8K3v93LAWwEPPnk9uD1lVeS4PW44+CKK2DSpKRhfUDac+4nQG1E/DQtfl3SiDQbOwJYmZbnM6eKmVm3KdNANv+MLMDeu/V3RtasdFwBLAYOTV/XphkEARER7y5g28wsDxdf3H4QK0Fzc++1h+ZmeOKJJHj9yU9g6VKoroYPfxiuvjoJXnffvRcb1PPSZRFvBV6IiP/OOpSZF+W69P2BrPKLJN0NHAWsy3RBNjPrCWUbyAqoyHPp2r0HD+D3f1vds40ys+6yf6EbYGZd09Fkvvl0N+6y5mb4/e+3B6+vvposUvuRj8D06XDKKTBkSC80pGDeTzJh3l8kPZOWfZUkgL1X0nnAK2wfWvYgcAJQB2wCzu3d5ppZuSnLQHZrU3Pe2ViAvXfrx8oNW2hqjk6dZ2a9LyJezlUuqZJkIrqcx82sdFRW9tCFm5rg8ce3B68rVkC/fjBxIlx3HZx8Muy2Ww99eHGJiN+Re9wrwIQc9QP4bI82yswsS1kGso1N7QekP36y5RSJS9dspqk5uOWxxQweUM2/HOVZ9syKlaTBJD+mRpJ0dXsYuAj4IvAMUFu41plZezITPHVk2rRu/NCmJvjf/02C15/+FF57LZk96vjjYfJkOOkkGDy4Gz/QzMy6Q1kGsg2dzMjuNqAagPX1DQxOt82saN0JrAX+AJwPfAmoASZFxDPtnWhmhVNbC2ef3fHY1wsugJtu6uKHNTbCY49tD15XroQBA+CEE2DKlOR91127+CFmZtaTyjKQ3drYTFUnAtnB/dNAdnMD9K25HMz6ogMi4l0Akn4IrAZGR8SGwjbLzNrzmc+0H8QOHQqruzJdRWMjPPpoErzedx+sWgUDB8KJJyaZ1xNOgEGDuvABZmbWmyoK3YBCqG9ooqoy/68+eEAS76/b3NBTTTKz7rPt/6gR0QT83UGsWXE7+GDYuLH9OmvW7MSFGxrg17+GT30K9t47WSKnthbGj08C2pUr4d574Z//2UGsWYHU1dVx4oknUldXV+imWIkpy4xsfUPnMrK79KuiUmJ9vZeiNCsBh0pan24LGJDuZ5bf8WA3syJy8MHw/PMd1xud7/QUDQ3wyCNJoHr//UkEPGhQMlHT5MnJxE0DB3apzWbWfaZPn85bb73F9OnTuf322wvdHCshZRnIbmlsoroTGdkKiV0HVCVdi82sqEVET81nambdLN8gtqoKZsxop8LWrfCb3yTB6wMPwNq1yRjXU05JgtePfCQZA2tmRaWuro4lS5YAsGTJEurq6hg7dmxhG2UloywD2fqGZqorO7eMzuD+1e5abGZm1k0uvDC/IFaC22+HqVNbHdiyBR5+eHvwum5dMrvwpElJ8PrhDyezD5tZ0Zo+ffoO+87KWr7KM5DtZEYWYPCAal5bt7mHWmRmpULSbcBJwMqIOCQt2wO4BxgDLAH+OSLWShLwbeAEYBPwiYh4uhDtNis2M2fmV+/OO7OC2Pr6ZMzr7Nkwdy6sX5+s63rqqUnwetxxybqvZlYSMtnYtvbN2lOmkz01d2qyJ4Dd+lexfnMjyXrfZlbGbgcmtiq7BHgkIg4EHkn3AY4HDkxf04Cbe6mNZkWvqanjOhdcAFM/ujkZ6zp1Kuy5Z5Jx/cUv4PTTk/eVK5OU7UknOYg1KzFjxoxpd9+sPWWZkd3S0MQu/Tr31QcPqGZrUzNbGjtY4M7M+rSIeEzSmFbFk4Bj0u1ZwKPAV9LyOyJ5AvaEpCGSRkTEit5prVnxkqCtZ8ODKjfzi4t+yQdfnw17/jyZ0niPPZLZhSdPTmYdrqnp3QabWbe7/PLLOf/881vsm+WrLDOyWxqbO921eJeaJPDdtDWPR8hmVm72ygSn6fueaflIYGlWvWVp2Q4kTZO0QNKCVatW9WhjzQqltjaZQDhXEDuATZzOHB4c/DE29B/OB799ejIG9swz4aGH4LXX4Ic/TGYddhBr1ifMnz+/xf5jjz1WoJZYKSrLjGx9Q1OnJ3saWJNMhLppq5fgMbO85brR5MxBRcRMYCbAuHHjPIbB+pwLL4SbW3WuH8hbnMgvmMJsTuBBdmET9BsOH/14knk95phkymIz65Nqa2tb7N9xxx188pOfLFBrrNSU5b8OSSDbuYzs9kDWGVkz28HrmS7DkkYAK9PyZcC+WfVGAct7vXVmBZa9zM4ubOQkfs5k5nACDzKQzbzGXsziHOYwmXnLP+jg1czMOlSW/1LUNzZTVdG5jOyAbV2LnZE1sx3MBc4BrkvfH8gqv0jS3cBRwDqPj7VyUlsLH/84DGIDZ/IzJjOH4/klA6hnBXtzG59kNlP4Hf9EM+kS0GX5y8TMzDqr7P65aGhqpqk5Oj1rsTOyZgYg6X9IJnYaJmkZ8DWSAPZeSecBrwBT0uoPkiy9U0ey/M65vd5gs170oQ/BI48k24NZx8n8jPuYw0R+RX+28Cr7cAufYjZT+D3v2x68pnbZpQCNNrOCqayspClrCvPKysp2apu1VHaBbGbW4c6OkR1QU4lwIGtW7iLizDYOTchRN4DP9myLzIrDwIFQs/lNzmIuk5nDR3iIfmxlGSP5Pp9hNlP4A+8l2pln8gc/6MUGm1nBnXjiicydO3fb/kknnVTA1lipKbtAtr4hCUQ7O0a2QqJ/daUDWTMzsyzvfcda/uHFB7iXOXyYX1NDA6+wL9/js8xmCk9yVLvBK0D//smExFOn9lKjzawonHPOOS0C2bPPPruArbFSU8aBbOcyspB0L/YYWTMzK3tr1sD99/ObT8/mscbfUE0jS9iP7/CvzGYKT/GPHQavAAcdBIsW9UJ7zawo/epXv2qx//DDD3PGGWcUqDVWavJKS0qaKOlFSXWSLslxvJ+ke9LjT0oak5YPlTRf0kZJ3211zhGS/pKe8x1JnY8sd0J9Q9K1uKqi80voDqypZLMzsmZmVo5Wr07Sph/5COy1F5x3Hm9r/Cs38G/8I39kf/7Ol/gv/phHBhbgggscxJqVu1tuuaXF/ve///0CtcRKUYcZWUmVwPeA40iWknhK0tyIeD6r2nnA2ogYK+kM4HrgY0A9cAVwSPrKdjMwDXiCZEKUicAvu/Z1OtaVjOyAmkre2uJA1szMysSqVXDffTB7NsyfD01NcMABPPSuf+erf5rC0xxO7uWS27bPPvDqqz3TXDMzKx/5pCWPBOoiYnFEbAXuBia1qjMJmJVuzwEmSFJEvBURvyMJaLdJ11kcHBF/SCdDuQM4tStfJF+ZyZ46O2sxwMCaKnctNjOzvm3lSvj+92HCBNh7b/j0p2HJEvjyl+Hpp6Gujol/uo6nOYLOBrEXXOAg1szMukc+0dxIYGnW/rK0LGediGgE1gFDO7jmsg6uCYCkaZIWSFqwatWqPJrbvi07OdkTZMbIOiNrZmZ9zGuvwU03wbHHwogRScS5bBlceik88wz83/9x4ZvXosPfgzqxDvtdd0HE9tdNN/XgdzCzkvOpT32qxf5nPvOZArXESlE+kz3l+hcrdqLOTtWPiJnATIBx48a1d8281Dd2bbKnLY3NNDQ171QgbGZmVjRWrICf/CTpNvy//5tEmu94B1x2GUyZAoccAun0FbvvDm++mf+lPYmTmeVj6tSpLcbJeqIn64x8orFlwL5Z+6OA5W3VkVQF7Aas6eCaozq4Zo/o2mRPSdz/5qaGbm2TmZlZr3j1VfjOd+ADH4CRI+Fzn0smcbrySnjuOXj+ebjmGnjXu0Bi5Mgklu1MEOtJnMwsX/PmzWuxP3/+/AK1xEpRPtHcU8CBkvaXVAOcAcxtVWcucE66PRmYl459zSkiVgAbJB2dzlZ8NvBAp1u/E7q6/A7Am5u2dmubzMzMeszSpfCtb8H73w+jRsHFFyeR6VVXJRHnokXJ9sEHb8vAZgLY5Z18xHzQQe4+bGb5u/baa1vsz5gxo0AtsVLUYdfiiGiUdBHwEFAJ3BYRiyRdAyyIiLnArcCdkupIMrHb+gVIWgIMBmoknQp8OJ3x+ALgdmAAyWzFPT5jMXRtsqcBaSC71hlZMzMrZq+8AnPmJN2Gn3giKXv3u+HrX4fJk5MuxG0YObLzASwkga8zsWbWGY2Nje3um7UnnzGyRMSDJEvkZJddmbVdD0xp49wxbZQvYMcleXpc1zKyyZ9rrTOyZmZWbJYs2R68/vGPSdlhh8GMGUnw+va353WZnQliAe68c+fOMzMz2xl5BbJ9SWaM7M7OWgywzhlZMzMrBosXbw9eFyxIyg4/HL7xjSR4HTs2r8vU1sJZZyXzPXVWZSXMmgVTp3b+XDMzs51VhoFskpGt7MTyARkDt3UtdkbWzMwK5G9/SwLX2bOTdV0Bxo2D669PgtcDDujU5Q4+OJnjqbM8M7GZmRVS2a0hU9/YRE1VBRXqfCBbU1lBZYU8RtbMzHrXSy/BtdfCe96TZFkvvRSqquA//xP+/nd46in48pc7FcTW1ibjWjsbxFZWJuvDOojt2yTdJmmlpOeyyvaQ9LCkl9L33dNySfqOpDpJz0o6vHAtN7NyUXYZ2S0NzfSv2rn4XRIDayo9a7GZmfW8F1/cnnl99tmk7Oij4ZvfhNNPh/32y/tSnV0HNpd99klW77GycTvwXeCOrLJLgEci4jpJl6T7XwGOBw5MX0cBN6fvZmY9pvwC2cYm+ldX7vT5A6or3bXYzMx6xgsvbA9en0sTYe97H9xwQxK87rt9WfcPfQgeeaT3muYgtrxExGOSxrQqngQck27PAh4lCWQnAXekSy8+IWmIpBHpcotmbRoxYgQrVmz/z2SfffYpYGus1JRdIFvf0NylQHZgTZW7FpuZWfdZtGh78Pr880l/3/e/H7797SR4HTlyh1N2YnRMl1xwQe9+nhWtvTLBaUSskLRnWj4SWJpVb1latkMgK2kaMA1g9OjRPdtaK3pf//rXOf/887ftX3PNNQVsjZWaMgxkm+i3k12LAXctNjOzrolIsq2zZyczDr/wQhKZfuADcOON8NGPJv14W6mpgYYCPEe94AK46abe/1wrKbkereScAzsiZgIzAcaNG7cT82SbmSXKMpDtWka2klfWbOnGFpmZWZ8XkYxzzQSvL74IFRXwwQ/CRRclwevee+9wWm93H86oroatfmZrO3o902VY0ghgZVq+DNg3q94oYCdXJLZy8sUvfrHF/pe+9CXuu+++ArXGSk3ZBbJbGpvpX73zGdlB/apY89ZWmpuDip1YwsfMzMpEBDzzzPbg9aWXkuD1mGPg85+H006DvfYCdn4JnJ4wYQL85jeFboUVqbnAOcB16fsDWeUXSbqbZJKndR4fa/l4s9UsdGvXri1QS6wUlV0gW9/QxC79dv5rDx5QTWNzsPqtLey5a/9ubJmZmZW8CH4542n+fMVsJjOHsfyNRiqZz7HM5ovc13waq+cNh3lAkY079azElk3S/5BM7DRM0jLgayQB7L2SzgNeAaak1R8ETgDqgE3Aub3eYDMrO2UYyDazxy4737V4cP9qAF5bV+9A1syswC68EG6+udCtCMaxgCkkwevx/J0PUcUjTOA6LuF+TuUNhhW6kdsMGACbNhW6FVbsIuLMNg5NyFE3gM/2bIvMzFoqv0C2sYl+XehaPHhA8id7bV097x7VXa0yMys9tbVw2WXw8svJXEVRVtO2BEfyx23B6xhepoEqHuY4pnM5DzCJNQzt1RYddFAyAbKZmVk5KLtAdktDM/2rupCRHZBkZF9fX99dTTIzKzkXXgjf//724LUcgljRzFE8uS14Hc1StlLNr/kwV3EVDzCJN9m919vlDKuZmZWjnU9NlqgtjU1dnuypqkK85kDWzPIkaaKkFyXVSbqku65bWwtjxiTzB40Zk+x3pl7r8gsvzL0vQVVV8j5sGAwalHTnLZfg9X08zg18npfZjz/wPj7L93iGwzibWezJSk7m58ziE70axA4YkPz9IxzEmplZeSq7jGx9Q3OXlt+pkNhz136sWOdA1sw6JqkS+B5wHMkSFU9JmhsRXZqjtrYWpk3bHsS8/HKyDzB1asf1Hn8cZs1qWZ491rT1flNT8v7GG11pdWmooIn38XumMJvT+QkjWU49/fgVE7mUb/AzTmY9uxWkbZ6QyczMLFGGgWwT/aq6lojea7f+7lpsZvk6EqiLiMUA6fIUk4AuBbKXXbZjJm7TpqQ8O5Btq97MmduDU0uC13/id9uC1xG8xmb680uOZw6T+TknsYHBvdYeL4FjZm258cYbqaurK3QzeszFF19c6CZ0i7Fjx/K5z32u0M3o08oqkG1saqaxObqUkQXYe3B//u/1Dd3UKjPr40YCS7P2l5Gss9iCpGnANIDRo0d3eNFXXsmvvK16DmKhkkY+wP8yhdl8lJ+yN6+ziQE8yAnMYTK/4EQ2smuXP2fIEPDSiGZmZt2rrALZ+sZmgC6NkQXYa/D/b+/Ow6sqz72Pf+8MJCEJIUCYAgQwIKAChThXK3WiTlSFag9taZ1qax06vH31+LbH1uM5tZ7TU6u1k7W2zuKIShVF0epRBJQyiGhE5kiYCUPm+/1jrcCGZIcNCXsgv8915cpez1577d/eSdbad55nPSubf3y8oT0iicjhz1poa3Z2qbv/EfgjQFlZ2X7PPh0wIBj+21J7LOulpx9+xWxMvZj19fD66zB1Kjz1FKxfH5xweu65MGkSnc85h4l5eUyMS2IRkQN3OPXyjRs3Do+YcMHMuPPOOxOYSFJJh5rsqaYu+NTW5h7Zgmy219RTVV3XHrFE5PC2GugfsdwPWNvWjd52G3TuvHdb585BeyzrXXVV8/aD1b07PPjgnsmHEvUVtYitq4OXXw5edJ8+cMYZ8MADMG5cUNCuXx98/8pXgpmsREQkLm644Ya9ln/wgx8kKImkog5VyO7ukW3D5XcgGFoMugSPiMRkDjDEzAaZWSfgUmBaWzc6eXJwnmtJSTCbcElJsBx5fmxr691zT/P273yn5WUIenAhKFq7d9+zzoMPwoYNzZ834erq4KWX4IorguL1rLPgkUeCIvbJJ4Pi9bHHYOJEyM1NdFoRkQ5pwoQJu2+bGeeff34C00iq6VhDi45L+8EAAB0aSURBVMMe2azMNOprDn5MXe+CoJD9bGsNpT3bfv6UiBy+3L3ezL4HvASkA/e5++L22PbkybEVkNHWi/XxKaO2FmbODHpXn3kmODE1Px/OPx8mTYKzzw6GEYuISNLo27cva9euVW+sHLCYClkzGw/cSfAh7F53/8U+92cBfwPGAhuBS9x9eXjfTcDlQANwnbu/FLYvB6rC9np3L2uH19Oq3YVsRjo72lLIhj2yupasiMTC3acD0xOd47BUUxOMKZ46FZ59FrZsgS5d4IILguL1rLMgOzvRKUVEJIqioiKKiorUGysHbL+FbIzXQLwc2OzupWZ2KXA7cImZjSAYRncU0Bd4xcyGuntTFTnO3eM2a1J1XftM9tTUI6uhxSIiCVBdHZzzOnUqTJsGW7dCQQF8+cvBUOEzz4SsrESnFBERkUMolh7ZWK6BOAG4Jbz9BHC3mVnY/qi71wCfmll5uL232yf+gampb5/JnrIz0ynIyeSzrSpkRUTioro6OOe1qXitqoLCQrjooqB4PeMM6NQp0SlFREQkTmIpZGO5BuLudcLzwbYC3cP2d/Z5bHF424EZZubAH8JLTzRzoNdWbE3N7h7ZthWyEAwvrlAhKyJy6OzaBS++GBSvzz0H27dDt27B7MITJ8IXv6jiVUREpIOKpZCN5RqI0dZp7bEnu/taM+sJvGxmH7r7G81WPsBrK7ZmzzmybZ+suXdBNhVbd7V5OyIiEmHnTvj734Pi9fnnYceOYJrkr341KF7HjYPMzESnFBERkQSLpZCN5RqITeusNrMMoADY1Npj3b3pe6WZPU0w5LhZIdueqttpaDHAsD753PfmBqrrGtpleyIiHdaOHTB9elC8vvBCUMwWFcHXvhYUr6edBhkdapJ9ERER2Y9YuiZjuQbiNGBKeHsi8Kq7e9h+qZllmdkgYAjwrpnlmlk+gJnlAmcBi9r+clrXXpM9AZSVdKOuwfnnqi1t3paISIezffue67gWFQXDhV9/HaZMCS6hs3Yt/P73wbmvKmJFRERkH/v9dBDtGohm9nNgrrtPA/4MPBBO5rSJoNglXO9xgomh6oFr3L3BzHoBTwfzQZEBPOzuLx6C17eXmnBocXZG23tQx5YUAjB3xWaOH9y9zdsTETnsVVUFw4WnTg2GD1dXQ+/ecNllQUF7yimQrhEuIiIisn8x/Zu7pWsguvtPI25XA5OiPPY24LZ92pYBow40bFtV17ffZE/dcjtxRFEu81ZsbvO2REQOW9u2BRM1TZ0aTNxUUwN9+sCVVwbF68knq3gVERGRA9ahxmtt3llLp/S0dhlaDHDswG5MX1hBY6OTlma4O88vqKBsYCF9CnLa5TlERFLO1q3BJXKmTg0umVNbC8XFcPXVQfF60kmQ1j77YRGR1tx1112Ul5cnOoa0ounnc/311yc4ibSmtLSUa6+9NtEx9tKhCtn122ooys8iHNLcZmNLCnl0zirK129naK98Hp2zipueWkhJ9848cfVJFOVntcvziIgkvS1b4Nlng+J1xgyoq4P+/eGaa4Li9YQTVLyKSNyVl5czf9ESGjp3S3QUiSKtNrgoybxl6xKcRKJJ37kp0RFa1KEK2cqqGnp2ab/i8tiBwU5xzvJNGPCz5xYzql8BH63bzpT73uXxq08kLyv6W/yrGUsB+MFZR7ZbJhGRuNm0aU/x+sorQfE6YABcd11QvB53nIpXEUm4hs7d2DXsnETHEElZOR9O3/9KCdChCtl126oZXJTbbtsr6d6ZHnmduO2FJbhDbqcM/jSljA/WbuObf5nDo++u5IpTBrf42H98vJ7fvBoMpejXrTNfKevf4noiIkll40Z45pmgeJ05E+rrYeBAuOGGoHg99lhop1EvIiIiItF0qEK2sqqGE9phhuGHZ6/cffv0Yb34ZP120tOMsSWF9MzPpueR2Yzo04XpCytaLGR31TZw89OLGNQjl95dsvnJM4s4priA4X26tDmbiEi727ABnn46KF5ffRUaGmDwYPjhD4PidexYFa8iIiISVx2mkK2ua2Drrjp6tePQYoBR/bsyqn/X3ctNRW6/whxmfLCOe14rp2vnTns95pUl61i5aSeXf34QPfOzWLBmK9c98j4v/+AL7ZpNROSgVVbuKV5nzQqK19JS+PGPYdIkGD1axauIiIgkTIcpZNdX1QDQMz87Ls93dHEBMz5Yx6K12/h8aY/d7fWNjcxetpHhfbpwRFEeAMcNLGTW0vVUbN2l2Y5FJHE++2xP8fr669DYCEOHwo03BsXryJEqXkVERCQpdJhCtrKqGoCidu6RjaZHXhZ9CrJZtGbrXoXskooqdtQ2cNzAPbPnjRlQyGtL1/PUe2u4ZlxpXPKJiABQUQFPPRUUr2+8Ae4wbBjcfHNQvB59tIpXEUlZa9asIX3n1qSdrEYkFaTv3MiaNfWJjtFMxylktwU9sr3i1CMLcEzYK7tpRy3dcoPhxXOXb6IgJ5MhvfJ2r9c9L4tBPXKZOncV3z3tiHa7PJCISIvWrNlTvL75ZlC8jhgBP/1pULyOGKHiVURERJJaxylkm4YWx6lHFmB0/668+mElryxZx1fK+rN5Ry3lldsZN6wnaft8SBw7oJAn3lvNnOWbOW6QrnUmIu1s9Wp48smgeH3rraDt6KPhlluCCZtGjEhoPBGRQ6G4uJjPajJ0+R2RNsj5cDrFxb0SHaOZDlPIrttWTUaa0W2fiZcOpa6dO/H5IT2YtXQ9YwYU8lb5BgDKSgqbrXt0cQEvLv6MR95dqUJWRNrHypV7ite33w7aRo6EW28NitdhwxKbT0QkDtJ3btLQ4iSWVr0NgMZsXb0jWaXv3ASokE2YyqoaeuRlkZYW3+FyXxhaxLwVm/nLW58CcN7IPs1mMQbolJHGxWOKeeTdVdx87nB65MWv51hEDiPLl+8pXmfPDtpGj4bbbguK16FDExpPRA5fZjYeuBNIB+51918kOBKlpZp7JNmVl1cBUDo4+QoladIrKf+WOlQh296X3olFVkY6543sy/SFFVz4uWKG9sqPuu7XTxzIX99ewWNzVmnSJxGJ3aefwhNPBMXrnDlB25gx8J//GRSvSXjwEZHDi5mlA78FzgRWA3PMbJq7f5DIXNdee20in15icP311wNw5513JjiJpJqOU8huq6ZfYeeEPPcxxQUcU1yw3/VKe+bx+dIePPjOCr596mAy0tPikE5EUtbGjXD22TBvXrBcVga33w4XXwxHHJHYbCLS0RwHlLv7MgAzexSYACS0kD0c3XXXXZSXlyc6Rrtpei1NBe3horS0VP9IOcQ6TKVUWVUT14meDtbXTyyhYms1Mz5Yl+goIpLsunWDQYPgjjtg2bKgN/bHP1YRKyKJUAysilheHbbtxcyuMrO5ZjZ3/fr1cQsnySsnJ4ecnJxEx5AU1CF6ZGvrG9m0ozaul945WGcM70VJ98784fVP+NLRvXUpHhGJziwYTiwikngtfWDxZg3ufwT+CFBWVtbsftk/9fKJBDpEj+yG7fG/9M7BSk8zvn3qEfxz9Vb+95ONUdfbsrOWbdV1cUwmImY2ycwWm1mjmZXtc99NZlZuZkvN7OyI9vFhW7mZ3Rj/1CIicbEa6B+x3A9Ym6AsItIBdIhCdt22agB65id/IQtw0ZhiivKz+N2sT5rdN3/VFi67fw5l//4KJ/7HTO6a+TG7ahsSkFKkQ1oEXAS8EdloZiOAS4GjgPHAPWaWHjH5yZeAEcBXw3VFRA43c4AhZjbIzDoR7BOnJTiTiBzGOsTQ4j2FbHIPLX549srdt8cOKOTFxZ/xb88u5sjewUzHR/XtwuQ/vUPnrAwuP2UQKzbs5L9f/ojnFqzlT98oo6R7bszPtXVnHfe8Xk5dvTNxbD9G9NW1u0T2x92XAC0N+Z8APOruNcCnZlZOMPEJaPITEekA3L3ezL4HvERw+Z373H1xgmOJyGGsQxSyzy2oID8rg8FFsRd6iXbC4O7MX7WFR+es5MpTBrOtuo5/f+EDsjLSuOzkQRTkZFLSLZfeBdk8NmcV43/9Dy4e04/hffIxM+obGpm7YjMffraN3l1yuPLUQZw4uDsOPPXeau54aSmbdtSSkZbGfW99yrkj+/DLi0eSm5U6vxKzl23knlmfUNK9M98/YyiFuc2vzysSJ8XAOxHLkZOc7Dv5yfEtbcDMrgKuAhgwYMAhiCgicmi5+3RgeqJziEjHkDpVy0FauXEnf19YwZWnDk6pIq1TRhpTThrI72aVc/drwbTkXXMydxexTYb2yue7px3BQ7NX8uDsFQzpmUd2ZjrLN+6gqrqebrmd+KRyB298vJ7crAw6pRubd9bRvzCH755WStecTLbX1vObmR9Tvm47t08cyej+XdvtdTQ0Oi9/8BlLKqo4fXhPjikuaPMEVpt31PKTZxfx/IIKeuRl8Wb5Bp6dv5b/mjSKM0foYtrSNmb2CtC7hbtudvdnoz2shTan5dM3WpzcRBOgiIiIiMQupsrOzMYDdxIMFbnX3X+xz/1ZwN+AscBG4BJ3Xx7edxNwOdAAXOfuL8WyzfZy75vLSE8zLjt50KHY/CFVkJPJlJMG8ubHGxjepwvD+uSTkdb8c3H3vCyuGVfK259s4LWl68nOTGNAt84cN6gbpUV51DU45ZVVvL9qC7tqGzhvZF+G9c7fXVBecepgxpYUct0j7/Pl375FWUkhk8r6cdaI3gfdy+nuvLjoM25/8UOWb9wJwJ0zP2Zg986cP6ovJd1z+XTDdj7dsIMVG3dyRFEeE0b35ZQhRXTK2PMaGxudim3VbN5Ry/qqGpZt2MEfXv+EjdtrOX14T04pLWLTzlqenLeabz8wl4vH9OOOSaMOKrMcWrX1jcxcso6n3l+Du3NEUR5nH92bMQMKEx1tL+5+xkE8rLVJTjT5iYiIiEg7M/fW//EfTlbyEXAmwYe1OcBX3f2DiHW+C4x096vN7FLgQne/JJzU5BGCc8X6Aq8AQ8OHtbrNlpSVlfncuXNjemHbquuYuWQdNz21kAtG9eWXE/cubiLPRz2cuPsB93j+y/HBMMbtNfU8PmcVf3t7Ocs37iQjzTiptAdnH9WLkcVdKenRmeyMdDLTba/ncHfqG536BmdHbT1zl29m6txVzPywkt5dshk3rCeDeuSypGIbC1ZvYdn6HUFXlUG33E4Udu7E6s272FXXQE5mOsN655ORbuRnZzJvxWa27tp7duYje+Vz5ohe9O2655pjNXUNPPDOCpZt2MG5I/sw5cSBHNk7ny7ZGbqEURu0tH9oaZfR0l7kkXdXsqu2gcqqGpZUbOP9lZvZUdtAl+wMehdks3zDTmobGvncgK6cN7Ivny/tQd+u2eRlHdjPzMzmuXvZ/tdsP2Y2C/iRu88Nl48CHmbPvm4mMISgp/Yj4HRgDcG+7l/2d97YgezrRKRjSMS+7lDTvk5EWhLr/i6WHtnj2P9kJROAW8LbTwB3W/BJNCEToJRXVnHOnW9S29BIv8IcrhlX2h6bTQkHU7RFFvXZmelcecpg1m6tZuHqrSxas5U3Pmp+wfKsjDTMoL4hKGL3lZeVwb+eM4yczAzS04JMxw7sxrEDu1FVXUdNXSOFuZ1231ff2Eh55XYWrN7K0nVVpJvROSudIT3z6FfYmbysdPKyMijM7dRioZOVmc6UkwYyc0klb3y0nhcWVACQkWakNa0b8W1Pk4XvW8vvTbT/83gLpVusBV60O9q6zRaLzhbXixbq0EkzGN6nC2UlhQzplc/XTihhe009T8xdxUOzV3Lr83v+9DPTjVn/ZxzFXZPv4uhmdiFwF1AEvGBm8939bHdfbGaPE+zD6oFr3L0hfIwmPxERERFpZ7H0yE4Exrv7FeHy14Hj3f17EessCtdZHS5/QjChyS3AO+7+YNj+Z+Dv4cNa3WbEtndPgAIcCSw9uJfaTA9gQzttKx5SKW8qZQXlPdQOdd4Sdy86hNuPOzNbD6xowyaS9XckWXNB8mZL1lyQvNmSNRe0LZv2dXI4S+a/W4m/mPZ3sfTIRpvEJJZ12nUClPZkZnNTaYhOKuVNpaygvIdaquVNBm39sJqs73my5oLkzZasuSB5syVrLkjubIlwuBXmcvD0tyEHo6WCcl+tTWLSbB0zywAKgE2tPDaWbYqIiIiIiIg0E0shOwcYYmaDzKwTcCkwbZ91pgFTwtsTgVc9GLM8DbjUzLLMbBDB5CfvxrhNERERERERkWb2O7TY3etbmqzEzH4OzHX3acCfgQfCyZw2ERSmJPkEKO0+XPkQS6W8qZQVlPdQS7W8h4Nkfc+TNRckb7ZkzQXJmy1Zc0FyZxNJJP1tyAHb72RPIiIiIiIiIskklqHFIiIiIiIiIklDhayIiIiIiIiklA5XyJrZeDNbamblZnZjovMAmNl9ZlYZXo+3qa2bmb1sZh+H3wvDdjOz34T5F5jZmATk7W9mr5nZEjNbbGbXJ3NmM8s2s3fN7J9h3p+F7YPMbHaY97Fw4jHCyckeC/PONrOB8cwbZkg3s/fN7PkUyLrczBaa2Xwzmxu2JeXvQkdhZreG7+98M5thZn0TnamJmd1hZh+G+Z42s66JzgRgZpPC/UOjmSXFJSCS8XgFLR+zkkG0Y1OiRTsGiYhI23SoQtbM0oHfAl8CRgBfNbMRiU0FwP3A+H3abgRmuvsQYGa4DEH2IeHXVcDv4pQxUj3wQ3cfDpwAXBO+j8mauQb4oruPAkYD483sBOB24H/CvJuBy8P1Lwc2u3sp8D/hevF2PbAkYjmZswKMc/fREdeAS9bfhY7iDncf6e6jgeeBnyY6UISXgaPdfSTwEXBTgvM0WQRcBLyR6CCQ1McraPmYlQyiHZsSLdoxSERE2qBDFbLAcUC5uy9z91rgUWBCgjPh7m8QzPYcaQLw1/D2X4EvR7T/zQPvAF3NrE98kgbcvcLd3wtvVxEUXMXJmjl83u3hYmb45cAXgSei5G16HU8Ap5uZxSkuZtYPOBe4N1y2ZM3aiqT8Xego3H1bxGIuwe97UnD3Ge5eHy6+Q3Ad8YRz9yXuvjTROSIk5fEKoh6zEq6VY1NCtXIMEhGRNuhohWwxsCpieTVJcJCLope7V0BwcAZ6hu1J9RrCoayfA2aTxJnDobrzgUqCHqFPgC0RH6gjM+3OG96/Fegex7i/Bn4MNIbL3UnerBB8IJthZvPM7KqwLWl/FzoKM7vNzFYBk0muHtlIlwF/T3SIJKW/lTbY59iUcPseg9w9KXKJiKSyjlbIttRTlWr/FU2a12BmecCTwA379AA1W7WFtrhmdveGcJhlP4KejuGtZEpYXjM7D6h093mRza3kSfh7C5zs7mMIhkBeY2antrJuMuQ9LJjZK2a2qIWvCQDufrO79wceAr6XTNnCdW4mGAr6UDLlSiL6WzlIB3Bsipt9j0FmdnSiM4mIpLqMRAeIs9VA/4jlfsDaBGXZn3Vm1sfdK8Khl5Vhe1K8BjPLJPig8JC7PxU2J3VmAHffYmazCM6f6mpmGWFPZmSmpryrzSwDKCB+w+hOBi4ws3OAbKALQQ9tMmYFwN3Xht8rzexpgn8UJP3vQqpz9zNiXPVh4AXg3w5hnL3sL5uZTQHOA073OF7M/ADes2Sgv5WDEOXYlDQijkHjCc7LFhGRg9TRemTnAEMsmAG2E3ApMC3BmaKZBkwJb08Bno1o/0Y4++sJwNamIZzxEp6D+Wdgibv/KuKupMxsZkVNM6OaWQ5wBsG5U68BE6PkbXodE4FX4/Vh291vcvd+7j6Q4PfzVXefnIxZAcws18zym24DZxF8OEvK34WOwsyGRCxeAHyYqCz7MrPxwP8FLnD3nYnOk8RS6XiVFFo5NiVUlGNQ0vxNioikKovjZ96kEPZ0/RpIB+5z99sSHAkzewQ4DegBrCPoOXkGeBwYAKwEJrn7pvBAfTfBf3N3At9y97lxzvt54B/AQvacx/mvBOciJV1mMxtJMOFQOsE/bx5395+b2WCCCVS6Ae8DX3P3GjPLBh4gOL9qE3Cpuy+LV96I3KcBP3L385I1a5jr6XAxA3jY3W8zs+4k4e9CR2FmTwJHEvx9rgCudvc1iU0VMLNyIAvYGDa94+5XJzASAGZ2IXAXUARsAea7+9kJzpR0xyto+Zjl7n9OaCiiH5vcfXriUkU/BiUyk4jI4aDDFbIiIiIiIiKS2jra0GIRERERERFJcSpkRUREREREJKWokBUREREREZGUokJWREREREREUooKWREREREREUkpKmQlqZhZg5nNj/gamOhMIpIazKx7xL7jMzNbE97eYmYfxDnL6PDyOU3LF5jZjQe5reVm1qP90h3Qc3/TzPpGLN9rZiMSnUtERCQj0QFE9rHL3Ucf6IPMLN3dGw5FIBFJDe6+ERgNYGa3ANvd/b/Cf4g9397PZ2YZ7l4f5e7RQBkwPcw2DZjW3hni4JvAImAtgLtfkdA0IiIiIfXIStIzs4Fm9g8zey/8OilsP83MXjOzh4GFYdvXzOzdsBfmD2aWntDwIpIs0s3sT2a22MxmmFkOgJkdYWYvmtm8cD8zLGwvMbOZZrYg/D4gbL/fzH5lZq8Bt5tZrpndZ2ZzzOx9M5tgZp2AnwOXhPuiS8KezbvDbfQys6fN7J/hV9M+7Zkwx2Izu2p/L8jMvmVmH5nZ6+Fra9r+/WY2MWK97eH3vPC1vGdmC81sQtg+0MyW7Pv+hNsoAx4KX0eOmc0ys7IWsjTb94Zf95vZovD5vt+Gn5+IiMheVMhKssmJGBr4dNhWCZzp7mOAS4DfRKx/HHCzu48ws+Hh/SeHvboNwOR4hheRpDUE+K27HwVsAS4O2/8IXOvuY4EfAfeE7XcDf3P3kcBD7L3fGQqc4e4/BG4GXnX3Y4FxwB1AJvBT4DF3H+3uj+2T5TfA6+4+ChgDLA7bLwtzlAHXmVn3aC/GzPoAPwNOBs4ERsTwHlQDF4b70nHAf5uZRXt/3P0JYC4wOXwdu6JkibbvHQ0Uu/vR7n4M8JcYMoqIiMREQ4sl2bQ0tDgTuNvMmj4gDY247113/zS8fTowFpgTfjbLISiCRUQ+dff54e15wEAzywNOAqbuqefICr+fCFwU3n4A+GXEtqZGnMpwFnCBmf0oXM4GBuwnyxeBbwCE29katl9nZheGt/sTFJcbo2zjeGCWu68HMLPH2Hvf2BID/sPMTgUagWKgV3hfs/dnP9uKFG3f+xww2MzuAl4AZhzANkVERFqlQlZSwfeBdcAoglEE1RH37Yi4bcBf3f2mOGYTkdRQE3G7gaDYSgO2xHhevkfc3ne/c7G7L41c2cyOP5BwZnYacAZworvvNLNZBEVxrJki1ROOuAp7XDuF7ZOBImCsu9eZ2fKI52jp/Yk5PlH2vWY2CjgbuAb4CnDZAWxXREQkKg0tllRQAFS4eyPwdSDaea8zgYlm1hPAzLqZWUmcMopIinH3bcCnZjYJgqIvLLwA/he4NLw9GXgzymZeAq5tGqJrZp8L26uA/CiPmQl8J1w/3cy6EOznNodF7DDghP3Enw2cZsFMzZnApIj7lhP0kAJMIBjVQvgclWEROw6IZf/Y2uuIfD3N9r0WzGic5u5PAj8hGEYtIiLSLlTISiq4B5hiZu8QDJ3b0dJK7v4B8P+AGWa2AHgZ6BO3lCKSiiYDl5vZPwnOVZ0Qtl8HfCvcl3wduD7K428lKBQXmNmicBngNWBE02RP+zzmemCcmS0kGMZ7FPAikBE+363AO62FdvcK4BbgbeAV4L2Iu/8EfMHM3iUYgty0z3wIKDOzueHr/rC15wjdD/y+abKnKFmi7XuLgVlmNj/cjkbLiIhIuzH3aCOTREREJBWY2TeBMnf/XqKziIiIxIN6ZEVERERERCSlqEdWREREREREUop6ZEVERERERCSlqJAVERERERGRlKJCVkRERERERFKKClkRERERERFJKSpkRUREREREJKX8f184V0jNyi9wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variable Fare from the titanic dataset\n",
    "# Fare is the price paid for the ticket by\n",
    "# the passengers\n",
    "\n",
    "diagnostic_plots(titanic, 'Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare is also very skewed, and shows some unusual values at the right end of its distribution.\n",
    "\n",
    "In the next cells We will identify outliers using the mean and the standard deviation for the variables RM and Age from the boston and titanic datasets, respectively. Then we will use the inter-quantile range to identify outliers for the variables LSTAT, CRIM and Fare from the boston and titanic datasets.\n",
    "\n",
    "### Outlier detection for Normally distributed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to find upper and lower boundaries\n",
    "# for normally distributed variables\n",
    "\n",
    "\n",
    "def find_normal_boundaries(df, variable):\n",
    "\n",
    "    # calculate the boundaries outside which sit the outliers\n",
    "    # for a Gaussian distribution\n",
    "\n",
    "    upper_boundary = df[variable].mean() + 3 * df[variable].std()\n",
    "    lower_boundary = df[variable].mean() - 3 * df[variable].std()\n",
    "\n",
    "    return upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.392485817597757, 4.176782957105816)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate boundaries for RM\n",
    "upper_boundary, lower_boundary = find_normal_boundaries(boston, 'RM')\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above we conclude that values bigger than 8.4 or smaller than 4.2 occur very rarely for the variable RM. Therefore, we can consider them outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of houses: 506\n",
      "houses with more than 8.4 rooms (right end outliers): 4\n",
      "houses with less than 4.2 rooms (left end outliers: 4\n",
      "\n",
      "% right end outliers: 0.007905138339920948\n",
      "% left end outliers: 0.007905138339920948\n"
     ]
    }
   ],
   "source": [
    "# inspect the number and percentage of outliers for RM\n",
    "\n",
    "print('total number of houses: {}'.format(len(boston)))\n",
    "\n",
    "print('houses with more than 8.4 rooms (right end outliers): {}'.format(\n",
    "    len(boston[boston['RM'] > upper_boundary])))\n",
    "\n",
    "print('houses with less than 4.2 rooms (left end outliers: {}'.format(\n",
    "    len(boston[boston['RM'] < lower_boundary])))\n",
    "print()\n",
    "print('% right end outliers: {}'.format(\n",
    "    len(boston[boston['RM'] > upper_boundary]) / len(boston)))\n",
    "\n",
    "print('% left end outliers: {}'.format(\n",
    "    len(boston[boston['RM'] < lower_boundary]) / len(boston)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Extreme Value Analysis we identified outliers at both ends of the distribution of RM. The percentage of outliers is small (1.4% considering the 2 tails together), which makes sense, because we are finding precisely outliers. That is, rare values, rare occurrences.\n",
    "\n",
    "Let's move on to Age in the titanic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73.27860964406095, -13.88037434994331)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate boundaries for Age in the titanic\n",
    "\n",
    "upper_boundary, lower_boundary = find_normal_boundaries(titanic, 'Age')\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The upper boundary is 73 years, which means that passengers older than 73 were very few, if any, in the titanic. The lower boundary is negative. Because negative age does not exist, it only makes sense to look for outliers utilising the upper boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total passengers: 714\n",
      "passengers older than 73 rooms: 2\n",
      "\n",
      "% of passengers older than 73 rooms: 0.0028011204481792717\n"
     ]
    }
   ],
   "source": [
    "# lets look at the number and percentage of outliers\n",
    "\n",
    "print('total passengers: {}'.format(len(titanic)))\n",
    "\n",
    "print('passengers older than 73 rooms: {}'.format(\n",
    "    len(titanic[titanic['Age'] > upper_boundary])))\n",
    "print()\n",
    "print('% of passengers older than 73 rooms: {}'.format(\n",
    "    len(titanic[titanic['Age'] > upper_boundary]) / len(titanic)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 2 passengers older than 73 on board of the titanic, which could be considered outliers, as the majority of the population where much younger."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier detection for skewed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to find upper and lower boundaries\n",
    "# for skewed distributed variables\n",
    "\n",
    "\n",
    "def find_skewed_boundaries(df, variable, distance):\n",
    "\n",
    "    # Let's calculate the boundaries outside which sit the outliers\n",
    "    # for skewed distributions\n",
    "\n",
    "    # distance passed as an argument, gives us the option to\n",
    "    # estimate 1.5 times or 3 times the IQR to calculate\n",
    "    # the boundaries.\n",
    "\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31.962500000000006, -8.057500000000005)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking for outliers,\n",
    "# using the interquantile proximity rule\n",
    "# IQR * 1.5, the standard metric\n",
    "\n",
    "# for LSTAT in the boston house dataset\n",
    "\n",
    "upper_boundary, lower_boundary = find_skewed_boundaries(boston, 'LSTAT', 1.5)\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total houses: 506\n",
      "houses with LSTAT bigger than 32: 7\n",
      "\n",
      "% houses with LSTAT bigger than 32: 0.01383399209486166\n"
     ]
    }
   ],
   "source": [
    "# lets look at the number and percentage of outliers\n",
    "# for LSTAT\n",
    "\n",
    "print('total houses: {}'.format(len(boston)))\n",
    "\n",
    "print('houses with LSTAT bigger than 32: {}'.format(\n",
    "    len(boston[boston['LSTAT'] > upper_boundary])))\n",
    "print()\n",
    "print('% houses with LSTAT bigger than 32: {}'.format(\n",
    "    len(boston[boston['LSTAT'] > upper_boundary])/len(boston)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The upper boundary shows a value of ~32. The lower boundary is negative, however the variable LSTAT does not take negative values. So to calculate the outliers for LSTAT we only use the upper boundary. This coincides with what we observed in the boxplot earlier in the notebook. Outliers sit only at the right tail of LSTAT's distribution.\n",
    "\n",
    "We observe 7 houses, 1.3 % of the dataset, with extremely high values for LSTAT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.462195000000001, -10.7030675)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking for outliers,\n",
    "# using the interquantile proximity rule\n",
    "# IQR * 3, now I am looking for extremely high values\n",
    "\n",
    "upper_boundary, lower_boundary = find_skewed_boundaries(boston, 'CRIM', 3)\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total houses: 506\n",
      "houses with CRIM bigger than 14: 30\n",
      "\n",
      "% houses with CRIM bigger than 14s: 0.05928853754940711\n"
     ]
    }
   ],
   "source": [
    "# lets look at the number and percentage of outliers\n",
    "# for CRIM\n",
    "\n",
    "print('total houses: {}'.format(len(boston)))\n",
    "\n",
    "print('houses with CRIM bigger than 14: {}'.format(\n",
    "    len(boston[boston['CRIM'] > upper_boundary])))\n",
    "print()\n",
    "print('% houses with CRIM bigger than 14s: {}'.format(\n",
    "    len(boston[boston['CRIM'] > upper_boundary]) / len(boston)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using the 3  times inter-quantile range to find outliers, we find that ~6% of the houses show unusually high crime rate areas. For CRIM as well, the lower boundary is negative, so it only makes sense to use the upper boundary to calculate outliers, as the variable takes only positive values. This coincides with what we observed in CRIM's boxplot earlier in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109.35, -67.925)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, identify outliers in Fare in the\n",
    "# titanic dataset. I will look again for extreme values\n",
    "# using IQR * 3\n",
    "\n",
    "upper_boundary, lower_boundary = find_skewed_boundaries(titanic, 'Fare', 3)\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total passengers: 714\n",
      "passengers older than 73 rooms: 44\n",
      "\n",
      "passengers older than 73 rooms: 0.06162464985994398\n"
     ]
    }
   ],
   "source": [
    "# lets look at the number and percentage of passengers\n",
    "# who paid extremely high Fares\n",
    "\n",
    "print('total passengers: {}'.format(len(titanic)))\n",
    "\n",
    "print('passengers older than 73 rooms: {}'.format(\n",
    "    len(titanic[titanic['Fare'] > upper_boundary])))\n",
    "print()\n",
    "print('passengers older than 73 rooms: {}'.format(\n",
    "    len(titanic[titanic['Fare'] > upper_boundary])/len(titanic)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Fare, as well as for all the other variables in this notebook which show a tail to the right, the lower boundary is negative. So we will use the upper boundary to determine the outliers. We observe that 6% of the values of the dataset fall above the boundary. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**That is all for this demonstration. I hope you enjoyed the notebook, and see you in the next one.**"
   ]
  }
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