anscombes-quartet-dataset.ipynb

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   "source": [
    "# Anscombe's Quartet Dataset"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following assignment concerns the well-known Anscombe’s quartet dataset. \n",
    "\n",
    "There are four distinct tasks carried out:\n",
    "1. Explain the background to the dataset – who created it, when it was created, and any speculation regarding how it might have been created. \n",
    "2. Plot the interesting aspects of the dataset. \n",
    "3. Calculate the descriptive statistics of the variables in the dataset. \n",
    "4. Explain why the dataset is interesting, referring to the plots and statistics above."
   ]
  },
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   "source": [
    "### Anscombe's Quartet Dataset Background\n",
    "\n",
    "Anscombe's Quartet Dataset was created in 1973 by F. Anscombe an English statistician for his published paper titled 'Graphs in Statistical Analysis' and the aim was to show the importance of statistical graphs. The Anscombe's dataset comprises of four datasets, each one has two continuous variable (eleven x and dependent y points), that have almost identical statistical properties. It is still not know how F. Anscombe created this dataset. (1,2). \n",
    "\n",
    "<img src=\"https://image.ibb.co/hqtfL0/anscombe.gif\" alt=\"anscombe\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot"
   ]
  },
  {
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>II</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>II</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>II</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>II</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>II</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>II</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>II</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>II</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>III</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>III</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>III</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>III</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>III</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>III</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>III</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>III</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>III</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IV</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y\n",
       "0        I  10.0   8.04\n",
       "1        I   8.0   6.95\n",
       "2        I  13.0   7.58\n",
       "3        I   9.0   8.81\n",
       "4        I  11.0   8.33\n",
       "5        I  14.0   9.96\n",
       "6        I   6.0   7.24\n",
       "7        I   4.0   4.26\n",
       "8        I  12.0  10.84\n",
       "9        I   7.0   4.82\n",
       "10       I   5.0   5.68\n",
       "11      II  10.0   9.14\n",
       "12      II   8.0   8.14\n",
       "13      II  13.0   8.74\n",
       "14      II   9.0   8.77\n",
       "15      II  11.0   9.26\n",
       "16      II  14.0   8.10\n",
       "17      II   6.0   6.13\n",
       "18      II   4.0   3.10\n",
       "19      II  12.0   9.13\n",
       "20      II   7.0   7.26\n",
       "21      II   5.0   4.74\n",
       "22     III  10.0   7.46\n",
       "23     III   8.0   6.77\n",
       "24     III  13.0  12.74\n",
       "25     III   9.0   7.11\n",
       "26     III  11.0   7.81\n",
       "27     III  14.0   8.84\n",
       "28     III   6.0   6.08\n",
       "29     III   4.0   5.39\n",
       "30     III  12.0   8.15\n",
       "31     III   7.0   6.42\n",
       "32     III   5.0   5.73\n",
       "33      IV   8.0   6.58\n",
       "34      IV   8.0   5.76\n",
       "35      IV   8.0   7.71\n",
       "36      IV   8.0   8.84\n",
       "37      IV   8.0   8.47\n",
       "38      IV   8.0   7.04\n",
       "39      IV   8.0   5.25\n",
       "40      IV  19.0  12.50\n",
       "41      IV   8.0   5.56\n",
       "42      IV   8.0   7.91\n",
       "43      IV   8.0   6.89"
      ]
     },
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   ],
   "source": [
    "#Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "#Load Anscombe's Quartet Dataset\n",
    "ans = sns.load_dataset(\"anscombe\")\n",
    "ans"
   ]
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ik52ZqbypXFBQgCZNmsDJyQmTJk1CYGAggEfPA61evRpCCNja2uKDDz5Ajx49cPfuXcTE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\nhf8PlBfKvqYyCScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf136394b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dts1 = ans [ans ['dataset'] == 'I']\n",
    "dts2 = ans [ans ['dataset'] == 'II']\n",
    "dts3 = ans [ans ['dataset'] == 'III']\n",
    "dts4 = ans [ans ['dataset'] == 'IV']\n",
    "\n",
    "fig_ans = plt.figure()\n",
    "\n",
    "#Create subplots\n",
    "a1 = fig_ans.add_subplot(2,2,1)\n",
    "a2 = fig_ans.add_subplot(2,2,2)\n",
    "a3 = fig_ans.add_subplot (2,2,3)\n",
    "a4 = fig_ans.add_subplot (2,2,4)\n",
    "\n",
    "#Plot each axis created\n",
    "a1.plot (dts1 ['x'], dts1 ['y'], 'o')\n",
    "a2.plot (dts2 ['x'], dts2 ['y'], 'o')\n",
    "a3.plot (dts3 ['x'], dts3 ['y'], 'o')\n",
    "a4.plot (dts4 ['x'], dts4 ['y'], 'o')\n",
    "\n",
    "#Title of each subplot and the figure\n",
    "a1.set_title (\"Set 1\")\n",
    "a2.set_title (\"Set 2\")\n",
    "a3.set_title (\"Set 3\")\n",
    "a4.set_title (\"Set 4\")\n",
    "fig_ans.suptitle (\"Anscombe's Dataset\")\n",
    "\n",
    "# Adjusting subplots\n",
    "fig_ans.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.500682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.198837</td>\n",
       "      <td>1.958925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.117500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.747500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dataset          x          y\n",
       "count       44  44.000000  44.000000\n",
       "unique       4        NaN        NaN\n",
       "top          I        NaN        NaN\n",
       "freq        11        NaN        NaN\n",
       "mean       NaN   9.000000   7.500682\n",
       "std        NaN   3.198837   1.958925\n",
       "min        NaN   4.000000   3.100000\n",
       "25%        NaN   7.000000   6.117500\n",
       "50%        NaN   8.000000   7.520000\n",
       "75%        NaN  11.000000   8.747500\n",
       "max        NaN  19.000000  12.740000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary statistics of all columns\n",
    "ans.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Dataset 1\n",
    "ans.iloc[0:11].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    3.316625\n",
       "y    2.031568\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation Dataset 1\n",
    "ans.iloc[0:11].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.00\n",
       "y    7.58\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median Dataset 1\n",
    "ans.iloc[0:11].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    0.000000\n",
       "y   -0.065036\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skewness of Dataset 1\n",
    "ans.iloc[0:11].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Dataset 2\n",
    "ans.iloc[11:22].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    3.316625\n",
       "y    2.031657\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation Dataset 2\n",
    "ans.iloc[11:22].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.00\n",
       "y    8.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median Dataset 2\n",
    "ans.iloc[11:22].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    0.000000\n",
       "y   -1.315798\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skewness of Dataset 2\n",
    "ans.iloc[11:22].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.0\n",
       "y    7.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Dataset 3\n",
    "ans.iloc[22:33].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    3.316625\n",
       "y    2.030424\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation Dataset 3\n",
    "ans.iloc[22:33].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.00\n",
       "y    7.11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median Dataset 3\n",
    "ans.iloc[22:33].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    0.000000\n",
       "y    1.855495\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skewness Dataset 3\n",
    "ans.iloc[22:33].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Dataset 4\n",
    "ans.iloc[33:44].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    3.316625\n",
       "y    2.030579\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation Dataset 4\n",
    "ans.iloc[33:44].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    8.00\n",
       "y    7.04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median Dataset 4\n",
    "ans.iloc[33:44].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    3.316625\n",
       "y    1.506818\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skewness Dataset 4\n",
    "ans.iloc[33:44].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Why the dataset is interesting\n",
    "\n",
    "The mean, median, standard deviation and skew of the variable x remained the same in all datasets. While the mean, the median and the std of the y variable showed a slightly difference among the datasets. The skewness of the y values of the datasets was different. (dataset 1 presented negative skew, but nearly zero. The dataset 2 the skew was negative as well and the dataset 3 and 4 showed positive skewness). If the skewness value is equal to zero, it's roughly symmetric the distribution. In the case of negative skewness value the tail is on the left of the distribution and when the skewness value is positive the tail is on the right side of the distribution.\n",
    "\n",
    "The scatter plot of the first dataset (Set 1) looks like to be a simple linear relationship and the distributed normally. The scatter plot of the Dataset 2 (Set 2) appears that the variable have a correlation, but not linear. The third plot (Set 3) the distribution looks like is linear and the scatter plot of the fourth dataset (Set 4) doesn't show relationship between the variables.\n",
    "\n",
    "<img src=\"https://image.ibb.co/jSEtq0/Negative-and-positive-skew-diagrams-English-svg.png\" alt=\"Negative-and-positive-skew-diagrams-English-svg\" border=\"0\"> By <a href=\"https://en.wikipedia.org/wiki/User:Rodolfo_Hermans\" class=\"extiw\" title=\"en:User:Rodolfo Hermans\">Rodolfo Hermans (Godot)</a> at en.wikipedia. - <span class=\"int-own-work\" lang=\"en\">Own work</span>; transferred from <a class=\"external text\" href=\"http://en.wikipedia.org\">en.wikipedia</a> by <a href=\"//commons.wikimedia.org/wiki/User:Rodolfo_Hermans\" title=\"User:Rodolfo Hermans\">Rodolfo Hermans (Godot)</a>., <a href=\"https://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=4567445\">Link</a>\n",
    "\n",
    "In essence, there are 4 different datasets in the Anscombe's Quartet Dataset even if they look similar, with the x values being the same for the first three datasets. It becomes clear that they have different patterns in the data after plotting each of the dataset. This is a good example of the importance of data visualization. Moreover, it shows the importance of a well-constructed graphs in helping to understand the data.\n",
    "Therefore, the Anscombe's Quartet Dataset is an example that illustrates the usefulness of graphics, in other words, it shows that the data should be graphically looked before analysing the data according to the type of relationship between the variables. Besides, it shows that is not adequate just to check the basic statistic properties to describe datasets.\n",
    "\n",
    "##### References\n",
    "1. IDRIS, I. Python Data Analysis Cookbook.2016.\n",
    "2. Wikipedia. Anscoombe's Quartet. Available: https://en.wikipedia.org/wiki/Anscombe%27s_quartet"
   ]
  }
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