anscombes-quartet.ipynb

{
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   "id": "6cbd4055-8acf-4be5-a743-f32d4280cc74",
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   "source": [
    "# Seeing Beyond Statistics: Anscombe's Quartet and the Power of Graphs\n",
    "\n",
    "In 1973, statistician Francis Anscombe challenged the commonly held belief that \"numerical calculations are exact, but graphs are rough\" (Anscombe, 1973).\n",
    "His strategy was to manually construct four small datasets---now known as Anscombe's Quartet---that have different distributions but nearly identical descriptive statistics.\n",
    "It's perhaps the best argument for visualizing data. \n",
    "\n",
    "In this notebook we replicate his paper by using Plotnine to visualize those datasets and Polars to verify their descriptive statistics.\n",
    "More specifically, we:\n",
    "\n",
    "- Turn the original values that Anscombe used into one long DataFrame\n",
    "- Verify the descriptive statistics\n",
    "- Create a couple of exploratory data visualizations\n",
    "- Fine-tune the visualization to make it suitable for communication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18471ea2-41dd-481a-96cd-eaa936c24f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl\n",
    "from plotnine import *"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f800c720-7604-4cc1-babb-4ba9e72f999a",
   "metadata": {},
   "source": [
    "## Create a Long DataFrame\n",
    "\n",
    "First, we turn the original four datasets into one Polars DataFrame.\n",
    "With 3 columns and 44 rows, the DataFrame is in long format, making it convenient for Polars to compute descriptive statistics and for Plotnine to visualize each row as a point.\n",
    "The column `dataset` indicates which rows belong to which dataset using the Roman numerals `I` through `IV`. (The Roman numerals are purely for visualization purposes.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d69cbc31-50ff-4624-a2ea-55b33df3e5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (44, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dataset</th><th>x</th><th>y</th></tr><tr><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;I&quot;</td><td>10.0</td><td>8.04</td></tr><tr><td>&quot;I&quot;</td><td>8.0</td><td>6.95</td></tr><tr><td>&quot;I&quot;</td><td>13.0</td><td>7.58</td></tr><tr><td>&quot;I&quot;</td><td>9.0</td><td>8.81</td></tr><tr><td>&quot;I&quot;</td><td>11.0</td><td>8.33</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;IV&quot;</td><td>8.0</td><td>5.25</td></tr><tr><td>&quot;IV&quot;</td><td>19.0</td><td>12.5</td></tr><tr><td>&quot;IV&quot;</td><td>8.0</td><td>5.56</td></tr><tr><td>&quot;IV&quot;</td><td>8.0</td><td>7.91</td></tr><tr><td>&quot;IV&quot;</td><td>8.0</td><td>6.89</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (44, 3)\n",
       "┌─────────┬──────┬──────┐\n",
       "│ dataset ┆ x    ┆ y    │\n",
       "│ ---     ┆ ---  ┆ ---  │\n",
       "│ str     ┆ f64  ┆ f64  │\n",
       "╞═════════╪══════╪══════╡\n",
       "│ I       ┆ 10.0 ┆ 8.04 │\n",
       "│ I       ┆ 8.0  ┆ 6.95 │\n",
       "│ I       ┆ 13.0 ┆ 7.58 │\n",
       "│ I       ┆ 9.0  ┆ 8.81 │\n",
       "│ I       ┆ 11.0 ┆ 8.33 │\n",
       "│ …       ┆ …    ┆ …    │\n",
       "│ IV      ┆ 8.0  ┆ 5.25 │\n",
       "│ IV      ┆ 19.0 ┆ 12.5 │\n",
       "│ IV      ┆ 8.0  ┆ 5.56 │\n",
       "│ IV      ┆ 8.0  ┆ 7.91 │\n",
       "│ IV      ┆ 8.0  ┆ 6.89 │\n",
       "└─────────┴──────┴──────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe_datasets = [\n",
    "    {\"x\": [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],\n",
    "     \"y\": [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]},\n",
    "    {\"x\": [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],\n",
    "     \"y\": [9.14, 8.14, 8.74, 8.77, 9.26, 8.1, 6.13, 3.1, 9.13, 7.26, 4.74]},\n",
    "    {\"x\": [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],\n",
    "     \"y\": [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]},\n",
    "    {\"x\": [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 19.0, 8.0, 8.0, 8.0],\n",
    "     \"y\": [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.5, 5.56, 7.91, 6.89]}\n",
    "]\n",
    "\n",
    "anscombe_quartet = pl.concat([\n",
    "    pl.DataFrame(xy).select(pl.lit(d).alias(\"dataset\"), \"x\", \"y\")\n",
    "    for d, xy in zip([\"I\", \"II\", \"III\", \"IV\"], anscombe_datasets)\n",
    "])\n",
    "\n",
    "anscombe_quartet.write_csv(\"anscombe_quartet.csv\")\n",
    "anscombe_quartet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9515db5a-3fac-4d3b-a18a-f057640cd029",
   "metadata": {},
   "source": [
    "## Compute Descriptive Statistics\n",
    "\n",
    "To verify that the four datasets have identical descriptive statistics, we use Polars to compute the mean and the variance of both `x` and `y`. We also compute the correlation between `x` and `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ec8b52e-413f-454a-8c7c-817272065d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (4, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dataset</th><th>mean_x</th><th>mean_y</th><th>variance_x</th><th>variance_y</th><th>correlation_xy</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;I&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.13</td><td>0.82</td></tr><tr><td>&quot;II&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.13</td><td>0.82</td></tr><tr><td>&quot;III&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.12</td><td>0.82</td></tr><tr><td>&quot;IV&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.12</td><td>0.82</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4, 6)\n",
       "┌─────────┬────────┬────────┬────────────┬────────────┬────────────────┐\n",
       "│ dataset ┆ mean_x ┆ mean_y ┆ variance_x ┆ variance_y ┆ correlation_xy │\n",
       "│ ---     ┆ ---    ┆ ---    ┆ ---        ┆ ---        ┆ ---            │\n",
       "│ str     ┆ f64    ┆ f64    ┆ f64        ┆ f64        ┆ f64            │\n",
       "╞═════════╪════════╪════════╪════════════╪════════════╪════════════════╡\n",
       "│ I       ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ 4.13       ┆ 0.82           │\n",
       "│ II      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ 4.13       ┆ 0.82           │\n",
       "│ III     ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ 4.12       ┆ 0.82           │\n",
       "│ IV      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ 4.12       ┆ 0.82           │\n",
       "└─────────┴────────┴────────┴────────────┴────────────┴────────────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.Config.set_float_precision(2)\n",
    "\n",
    "anscombe_quartet.group_by(\"dataset\", maintain_order=True).agg(\n",
    "    pl.col(\"x\", \"y\").mean().name.prefix(\"mean_\"),\n",
    "    pl.col(\"x\", \"y\").var().name.prefix(\"variance_\"),\n",
    "    pl.corr(\"x\", \"y\").alias(\"correlation_xy\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09381c5-c2ac-42ab-ab0e-8953343e104c",
   "metadata": {},
   "source": [
    "As you can see, the descriptive statistics are nearly identical.\n",
    "At the end of this notebook there's a bonus section where we use scikit-learn to fit a linear regression line to each dataset and verify that the intercept, slope, and $R^2$ are also identical. (Spoiler alert: they are!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fd841a-00ce-46f8-93e9-a753f6bfac7a",
   "metadata": {},
   "source": [
    "## Exploratory Data Visualization\n",
    "\n",
    "Let's create a few basic data visualizations to get a better feeling for the data.\n",
    "What's great about Plotnine is that you can get a first plot with relatively little code.\n",
    "We have two continuous variables `x` and `y`, so a scatter plot might work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d21d11ee-1cc5-4702-a417-540f76bcdbbc",
   "metadata": {},
   "outputs": [
    {
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"
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(anscombe_quartet, aes(\"x\", \"y\")) + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d7e139-d774-4d11-87e0-362abcf3993f",
   "metadata": {},
   "source": [
    "That doesn't make much sense yet.\n",
    "We need a way to distinguish between the datasets.\n",
    "Let's color each point according to the dataset it belongs to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d86ee6b4-4cad-4d06-837f-e9523db46e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(anscombe_quartet, aes(\"x\", \"y\", color=\"dataset\")) + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5130ee7b-c8cb-4251-a832-64a13e06488d",
   "metadata": {},
   "source": [
    "Well, that's rather messy. Let's create a panel for each dataset using the `facet_wrap()` function instead. To make the code easier to read and to edit, we'll put each function on its own line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2fb5cbc-424a-4587-ba14-63205ad2c51b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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"
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    ggplot(anscombe_quartet, aes(\"x\", \"y\", color=\"dataset\")) +\n",
    "    facet_wrap(\"dataset\") +\n",
    "    geom_point()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdaf04d9-668b-4b88-9691-b689e03c5061",
   "metadata": {},
   "source": [
    "That's better.\n",
    "The panels make the use of color redundant, so we'll make each point black again.\n",
    "Let's also add a regression line using the `geom_smooth()` function, to visualize that the four datasets have identical descriptive statistics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17d72892-96fe-407d-b9c6-e6114233a253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    ggplot(anscombe_quartet, aes(\"x\", \"y\")) +\n",
    "    geom_point() +\n",
    "    geom_smooth(method=\"lm\", se=False, fullrange=True, color=\"blue\") +\n",
    "    facet_wrap(\"dataset\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0346bb-dbd0-4ce4-b6df-8e6449977030",
   "metadata": {},
   "source": [
    "This data visualization clearly supports Anscombe's point: that datasets with different distributions can have the same descriptive statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57193aa-4572-4b3f-a7e9-13fbedb09894",
   "metadata": {},
   "source": [
    "## A Fine-Tuned Data Visualization\n",
    "\n",
    "Our final exploratory data visualization gets the job done, but it doesn't hurt to make it more visually appealing.\n",
    "In the following code snippet, we:\n",
    "\n",
    "* Remove many elements, including grid lines, tick marks, and axis labels. They're not needed to bring the point across.\n",
    "* Add a title.\n",
    "* Change the colors and the font.\n",
    "\n",
    "As you can see, there's quite some code needed for this fine-tuning. \n",
    "Luckily, because of its composable API, Plotnine allows you to gradually move from a exploratory data visualization to a fine-tuned explanatory one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "477b8c0d-c83c-4d8f-8230-4ccfa8b7891a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s6HeEjFPmTRxKAcQWYiYAAIhYfGqGLv/6b7Xlh7erYee24Hav36IDzZk60JwpSbJZRmLm8c/FPF5GySKd94XvKyW3cNrXDURLJBHzeK9VteqjFy6SxcyVyAAAAADmHn/A0Jb9TXqs9BNqNyWHfbxFfh0ILDxpzGw97rE4yblFsieGfy4AM4eYCQAApiQuOVUXfflnqnrur9rxv9+Td6Bv1D5ev2WMI4eYrVadcc0/afn7Pyaz1TbufkAsmWrEzEl16IMbF+jiMwoImQAAAADmnOGIee9Llapt65PCDJlWeXW59Wldb/2Tss3HJty3vS9e7f0jMTN70YpIlgxgBhEzERV+v19Hjx5VdXW1ent7ZTablZaWpoqKCuXn58/08gAA08xkMqn84vereO3FqnrhYR188kH1NNVMeIwjLVMVl1yj8ks+oERnzilaKTA10YyYNov55AcAAAAAwGlkVMQMUzgRc9jehqyQcfnF7w/7vABmFjFzjjtw4IBuv/12BQKBUb/76Ec/qquvvnrC4/1+vx555BE9/PDD6ujoGHOfwsJCXX/99dq0aVNU1gwAiF32xBQtuepmLb7yJvU01ai9ep86jx6Uu69bRsCQLSFR6cUVcpYuUVphmcyW8a/YBGLJlCNmmkMf3EDEBAAAADA3RSNiXmZ9RjdYH5p0xJSk2o5kHW1PC47Ti8qVveissM8PYGYRM+cwr9ern/zkJ2OGzMkYGBjQN77xDe3du3fC/erq6vS9731P27dv16233qr4+PiIzgcAmD1MJpNS8+YrNW++tPGKmV4OELGufrceevmwHiViAgAAAEDYphoxLYZP77I9FdaVmMO6BuK0vaogZNvKm26VycSjPoDZhpg5h/3pT39SbW1txMffddddY4ZMi8Uis9ksr9cbsn379u1yu936yle+IgtX4gAAgBhGxAQAAACAyE01YpoDPpV1vK7lx17Q5fP3KNvZE9bxrT0JeuFAsdy+kQRSdsF7VbBqY9hrATDziJlzlGEYSk1N1cc+9rHgtjfffFM7d+6c1PGvvPKKXnvttZBta9eu1fXXX6+SkhKZTCbV19fr4Ycf1lNPPRXc54033tDf/vY3XXPNNdF5IQAAAFEUjYh54zsR00rEBAAAADDH+AOGtuxr0r1bIrydrNmkpb5qzd//oBK93ZKkzQeLVZrVqVXFzUqw+yY83u0za3d9tvY1ZkoauQIztaBM53z0X8NeD4DYQMyco0wmk664IvS2f/39/ZOOmX/7299CxhdccIE+97nPhWwrKCjQpz71KRUXF+sXv/hFcPuDDz6oq666SnFxcZEtHgAAIMqidSXmJURMAAAAAHNQNCLm5WcV6vr1C5RuP0/PfuN1tR4Y+az68LF0HTmWpqKMHhVk9MiZ6FJSvFeSIZfHpvZ+hxq7knSkLU3+QOh7spS8+br0P+6RPTFlqi8TwAwhZiJsHR0d2r9/f3Bst9t1yy23jLv/VVddpS1btgSPcblc2rlzp9asWTPtawUAAJgIV2ICAAAAQOSiGTGzUx3B7Zd89X/0yi+/peoXHg5uM2RSTUeqajpSJz1//lkbtOEz31B8akbYawMQO4iZCNvBgwdlGEZwfMYZZyg1deJ/QM4999yQAFpTU0PMBAAAM4YrMQEAAAAgctMVMYO/j3Now6fv1Py1l+iVX3xD/W3NYc1vT0rR6g9/QQsueK9MJtPJDwAQ04iZCFsgENDq1auD4/Xr15/0GLvdHjLu6wv/HzgAAICp4kpMAAAAAIjcdEfMExWs2qSrf/K46l57XgeeekAtb78+4f4ZJYu08LLrVLLxCtniE8JeH4DYRMxE2NavXz+pgHm8qqqqkHFiYmI0lwQAADAhIiYAAAAARO5UR8zjWWw2zV9/meavv0yegT51HDmgjiMH5OnrlmEYsiUkKb24Qs7SxYpPSQ97bQBiHzET066zs1Nbt24N2VZWVjZDqwEAAHMJERMAAAAAIjeTEXMs9oQk5S5drdylq0++M4DTBjET0+7ee++Vx+MJjpOSkrRixYqZWxAAADjtBSPmjqNy+wJhH5+b5tCNG8t10fJ8IiYAAACAOSfWIiaAuY2YiWm1Y8cOPf300yHb3v3ud8tms83QigAAwOmss8+th16u1mOv1xAxAQAAACBMREwAsYiYiWlz7Ngx3X333SHbsrOz9f73v39mFgQAAE5bREwAAAAAiBwRE0AsI2ZiWng8Hv3Xf/2Xent7g9vMZrNuvfVWxcXFzeDKAADA6YSICQAAAACRI2ICmA2ImZgWP/3pT1VVVRWy7YMf/KCWLVs2QysCAACnEyImAAAAAERuqhHTZjHrshUFREwApwQxE1H35z//WS+88ELItjVr1ui6666boRUBAIDTBRETAAAAACIXjYh5+VmFum5dGRETwClDzERUvfLKK/q///u/kG0lJSW67bbbZDKZZmhVAABgtuvoG9RDLx/W40RMAAAAAAgbERPAbEbMRNQcPnxYd911lwKBkQ8YMzIydMcddyg+Pn4GVwYAAGarqUbMeekJ+uCGBURMAAAAAHMSERPA6YCYiajo7OzUN77xDQ0ODga3xcXF6Stf+YoyMzNncGUAAGA2ImICAAAAQOSImABOJ8RMTJnH49E3v/lNtbW1BbeZzWZ9/vOf14IFC2ZwZQAAYLYhYgIAAABA5KIRMS9bUaDr1y8gYgKIGcRMTNmPfvQjHTp0KGTbhz/8Ya1Zs2aGVgQAAGYbIiYAAAAARI4rMQGczoiZmJI//vGPeumll0K2XX755br66qtnaEUAAGA2IWICAAAAQOSImADmAmImIrZ161bdf//9IdsSEhI0b948PfzwwxMeW1paquXLl0/n8gAAQAwjYgIAAABA5LidLIC5hJiJiFRWVuruu++WYRgh2wcGBvTb3/72pMdfccUVxEwAAOYgIiYAAAAARI4rMQHMRcRMhK29vV3f/OY35fF4ZnopAABgliBiAgAAAEDk/AFDL+1r1L0vVaquvT/s44mYAGYzYibC4na7deedd6qjo2OmlwIAAGYBIiYAAAAARI6ICQDETBznxhtv1I033jjhPnFxcbr77rtPzYIAAMCsRcQEAAAAgMgRMQFgBDETAAAAUdPRN6iHth/W428QMQEAAAAgXERMABiNmAkAAIApG46Yj71RIw8REwAAAADCQsQEgPERMwEAABCxaETMGzcu0IXLiJgAAAAA5h4iJgCcHDETAAAAYeNKTAAAAACIHBETACaPmAkAAIBJi9aVmBctz5fFTMQEAAAAMLcQMQEgfMRMAAAAnBQREwAAAAAiF62Ief36MmWlEDEBzC3ETAAAAIyLiAkAAAAAkSNiAsDUETMBAAAwSnvvoB56+bAeJ2ICAAAAQNiImAAQPcRMAAAABEUjYn5oY7kuXJ5HxAQAAAAw5xAxASD6iJkAAAAgYgIAAADAFBAxAWD6EDMBAADmMCImAAAAAESOiAkA04+YCQAAMAe19w7qwe3V+vubtURMAAAAAAgTERMATh1iJgAAwBxCxAQAAACAyBExAeDUI2YCAADMAURMAAAAAIgcERMAZg4xEwAA4DRGxAQAAACAyBExAWDmETMBAABOQ0RMAAAAAIgcERMAYgcxEwAA4DRCxAQAAACAyBExASD2EDMBAABOA1ONmHkZCbpxAxETAAAAwNxExASA2EXMBAAAmMWGI+bjb9TK6ydiAgAAAEA4iJgAEPuImQAAALPQVCNmfkaibty4QBcsI2ICAAAAmHuImAAwexAzAQAAZhEiJgAAAABEzh8w9OLbjbpvCxETAGYLYiYAAMAsQMQEAAAAgMgNR8x7t1SqnogJALMKMRMAACCGETEBAAAAIHJETACY/YiZAAAAMYiICQAAAACRI2ICwOmDmAkAABBDohExP7Rxgc4nYgIAAACYg4iYAHD6IWYCAADEACImAAAAAESOiAkApy9iJgAAwAxq6xmKmH9/k4gJAAAAAOEiYgLA6Y+YCQAAMAOImAAAAAAQOSImAMwdxEwAAIBTiIgJAAAAAJEjYgLA3EPMBAAAOAXaewf1wDYiJgAAAABEgogJAHMXMRMAAGAacSUmAAAAAESOiAkAIGYCAABMAyImAAAAAESOiAkAGEbMBAAAiCIiJgAAAABEjogJADgRMRMAACAKphoxCzISdSMREwAAAMAcRcQEAIyHmAkAADAFbT2DemB7lZ54sy7iiPmhTeU6b2meLGbTNKwQAAAAAGIXERMAcDLETAAAgAgQMQEAAAAgckRMAMBkETMBAADCEI2IOXQ72XwiJgAAAIA5JxoR810rC3XdOiImAMwVxEwAAIBJ4EpMAAAAAIgcERMAECliJgAAwASImAAAAAAQOSImAGCqiJkAAABjaOsZ1B+3VenJnURMAAAAAAgXERMAEC3ETAAAgONM+UpMZ6I+tJGICQAAAGBuilbEvH7dAmWmxE/DCgEAsw0xEwAAQERMAAAAAJgKIiYAYLoQMwEAwJxGxAQAAACAyBExAQDTjZgJAADmJCImAAAAAEQuGDFfqlR9BxETADB9iJkAAGBOOdbj0gPbqvXkTiImAAAAAISLiAkAONWImQAAYE4gYgIAAABA5KIRMS8/q1A3rCdiAgDCQ8wEAACnNSImAAAAAESOKzEBADONmAkAAE5LREwAAAAAiBwREwAQK4iZAADgtELEBAAAAIDIETEBALGGmAkAAE4LREwAAAAAiBwREwAQq4iZAABgVmvtdunB7ZFHzEJnoj60qVyblhAxAQAAAMw9REwAQKwjZgIAgFmJiAkAAAAAkSNiAgBmC2ImAACYVYiYAAAAABA5IiYAYLYhZgIAgFmhtdulB7ZV6am36omYAAAAABCmYMTcUqn6diImAGD2IGYCAICYNhwxn9xZJ1/ACPt4IiYAAACAucwfCGjz3kbdt6WKKzEBALMSMRMAAMQkIiYAAAAARC4aEfOKlUW6bl0ZERMAMKOImQAAIKYQMQEAAAAgckRMAMDphpgJAABiAhETAAAAACJHxAQAnK6ImQAAYEZNNWIWZSbpQxvLtXHJPCImAAAAgDmHiAkAON0RMwEAwIwgYgIAAABA5KIRMd+1slDXr1tAxAQAxDRiJgAAOKWImAAAAAAQOa7EBADMNcRMAABwSkQjYt64cQHPxAQAAAAwJxExAQBzFTETAABMq9Zul/64rUpPcSUmAAAAAISNiAkAmOuImQAAYFoQMQEAAAAgckRMAACGEDMBAEBUETEBAAAAIHJETAAAQhEzAQBAVEQlYm4q18bFREwAAAAAc080IuaVq4YipjOZiAkAOH0QMwEAwJQQMQEAAAAgckRMAAAmRswEAAARaeka0B+3Vevpt4iYAAAAABCuaN1O9vr1REwAwOmNmAkAAMJCxAQAAACAyHElJgAA4SFmAgCASSFiAgAAAEDkiJgAAESGmAkAACYUlYi5sVwblxAxAQAAAMw9REwAAKaGmAkAAMbElZgAAAAAEDkiJgAA0UHMBAAAIaIRMW/aNHQlptlExAQAAAAwtxAxAQCILmImAACQdHpGTCMQUE9jjTpqDsrT3yuTTLInJit9foVS5hXLZDbP9BIBAAAQQwzDUF9rgzqOHJC7t0uGYcjmSFBaYbnSCktltvBRGsYXjYh5xcoiXb+eiAkAwPH4CwwAgDnudIuYRiCgpj2v6uBTD6px18vyDQ6MuZ/Nkai8Feu08LLrlbvsbJliYO0AAACYGW1Ve3XgyQdUv2Oz3H3dY+5jsccpZ/EqVVx2rQpXn0fYRFC0IuZ168qUmULEBADgRPzVBQDAHDXViFmclaSbNlVow+LcmIiYktS8d4de+eU31V1/+KT7el39qnn5GdW8/IzSi8q15p/vUPaiFdO/SAAAAMSMztpKvXLPN9R6YOdJ9/V73GrctV2Nu7YrMXOezr7liyo+96JTsErEKiImAACnBjETAIA55nSMmH6PW6///i4deOL+iI7vrK3UE1/5sJa8+2atvPGzsthsUV4hAAAAYokRCGjv336rtx74mQI+b9jH97c1afN/f04lG96lNf/0ZdkTU6ZhlYhVREwAAE4tYiYAAHPE6RgxJcnrGtDz3/6MmvfuGPW73JQ+5af3ypnoUkLc0IdU/W6b2vscqu9KUWtP4sjOhqF9j/xeXbVVuuCLd8sax4cKAAAAp6OA36/tP/uqqjc/Oup3GYkuFWX0yJnkUnKcRzIZGvRa1d7nUFN3kuo7kyWN/C18ZOsT6qqr1iX/cY8cqc5T+CowE4iYAADMDGImAACnudM1YkpSwOfVC/9966iQWZjeo5XFzUpLcI86JtXhUV5av5YXtKmzP05v1MxTQ1dy8PeNb23Xi9//gi740t08BwkAAOA0YxiGXvnFN0aFzKykAZ1d0qjMJJdO/JM31eFRTsqAluS1q2/Qpl312apqzQj+vrPmkJ75+r/o8jt/K3tC0ql4GTjFiJgAAMwsPqEDAOA0dTpHzGG7//xLNe1+JTg2mwJaV9ag0qyuUR9CjSU90a2LFh9VVWu6Xjmcp4BhliTVv/GS3n7k91p+9S3TtXQAAADMgCNb/67KZ/8csm1lUbOW5h+TeRJ/PybFe7V+QYOKnd3acqhIHr9FktR59KB2/Pa7Wv+pr03HsjFDiJgAAMQGYiYAAKeZuRAxJanjyAHt/vOvgmOzKaCLFx/VvLTwPmQwmaTynE457D49f6BYhjH0mt/6409VuPo8pRWWRXXdAAAAmBmuzja99qtvh2xbV1av8pzOsOcqSO/TZcsO66m9pcGgWfX8X1W89mIVrNwYlfVi5kQjYl65aihiOpOJmAAATBUxEwCA08RciZjDXv+/H8jw+4Lj1fObww6ZxytI79VZhc16s3aepKFb2L557w914e0/mvJaAQAAMPN2/ekXcvd1B8eLctsiCpnDMhIHtW5BvTYfLA5ue/1/v6/8szbINAv+nsZoREwAAGITMRMAgFlurkVMSepuPKqmXS8Hx9nJ/VqU2z7leZfmt6mmPVXt/QmShm4329faqKTsvCnPDQAAgJnjdfXr8Isjz8lMivNoZXHzlOctdvZovrNLR9vTJEnd9YfVvHeH5i0/Z8pz49TxBwJ6YW+j7idiAgAQk4iZAADMUsMR86m36uSfIxFzWOWzfwkZn1HQOqlnZJ6M2SQtLzgW/Ha9EQio6vm/asUNn5r65AAAAJgxR7Y9Ka9rJFItyWuTzRL+39BjOaOgNRgzJenQM38iZs4SwxHzvi1VaiBiAgAQs4iZAADMMnM5Yg5r2fd68OfEOI/y0vqiNndhRo8cNq9cXpskqXnfG1GbGwAAADOj5e2Rv+nMpoDKsiK/veyJ0hPdykru17HexKFz7X8zanNjehAxAQCYXYiZAADMEkTMIQGfVx1HDwXHOSn9Ubkqc5jZJGWnDKimPVWS1HF4v4xAQCazOXonAQAAwCnVXv128OeMxEHZrYFx9zUMqXfQLrfPIsMwyWoJKNXhlsU8/t/guSkjMdPV0aqBzmNKSM+K3gtAVBAxAQCYnYiZAADEuOauAf1xa5We3lUfUcScn5WsD20qn/URwM4MFAAAbzZJREFUc1hfa6MCXk9w7Ex0Rf0czkRXMGZ6Xf0a6GhVYmZu1M8DAACA6Rfw+9XdeDQ4HuvvR7fXoqrWdNV1Jqu9zyFfwBLye5PJUHrCoHJT+1SR06FUhyfk986k0Dm766qJmTGEiAkAwOxGzAQAIEZFI2LetKlc60+TiDnM5w79oCjO6o/6OeKsvtBzDkY/mAIAAODU8HvdQ5dbviPONvK3nttr0Zu1Oao+li5/YPw7cRiGSR39DnX0O7SvMUt5ab1aPb9J6QnuoTlP+JvU6+bvx1hAxAQA4PRAzAQAIMYQMSdmsoR+Sz5gRP81njjniecEAADA7GG2hH78Nfy3Xl1Hsl6uzg8+Kz0cjV3JemxXos4sbNWy/GM68c92s5mP3GYSERMAgNMLf1kBABAjphoxS7KT9aGNp2/EHBafmhEy7h20R/0cPYNxoedMSY/6OQAAAHBqmK022RKS5R3olST1uuK0tyFTb9TMG7WvzeJXXlqvMpNcSorzyGSSXF6rOvocaupOUp975G/PgGHWztpctfUmKDelN2QeR5pzel8UxkTEBADg9ETMBABghkUlYm4q1/pFp3fEHOZIdcqRkS1XR6skqa3PEfVztB83Z3JukeyJyVE/BwAAAE4Nk8kkZ8kiNb+9Q5LU1J2omo7UkH0cNq9WFLWoJLNLNsvYf5MbhtTQlaRddTlq60sIbq/rTFFH/0j4MlutSitaMA2vBOMhYgIAcHojZgIAMEOImJHLLFuiundiZktPolweixz26Dw7s99t07HekQ+nnGVLojIvAAAAZo6zbEkwZnr8oR+HlWR26dzSBsVZAxPOYTJJBel9ykvr077GTL1ZkytDQ3+H93tGrthMKyqXxRb9u4dgtGhEzCtWFun69URMAABiGTETAIBTrLlzQPdvq9IzRMyIFa+5RHU7Nksaur1XZWuGzig4FpW5DzZnBD+UGjrXxVGZFwAAADOneO0levuR/x21fcm8Y1o9v1nh/FltNknL8tuU4nBr84HikL8dJf5+PBW4EhMAgLmFmAkAwClCxIye+esu1Y7ffVfu3i5J0p76LJVmdikp3juleXtcdu1rygyOHelZKjrnginNCQAAgJmXWb5cjvRMuTrbgtsK0nvCDpnHK8ro1er5TdpxNC9ke/nF75/KUjEBIiYAAHMTMRMAgGlGxIw+iz1Oi674oHY98HNJki9g0baqAl285Kgs5vD/N5Ykf8CkrVUF8gfMwW1LrvqQzFZbVNYMAACAmRPw+eRzu4Nju9WntWUNEYfMYYvntaumI1WtPYnBba7ONjlSnVObGCGImAAAzG3ETAAApgkRc3otv/pjOrrtaXXXV0uSmnuS9NKhQm2qqAs7aPr8Jr14qEjHekc+hMooXawlV90c1TUDAABgZtS9/oK8A73B8eLcdiXYfVOe12SSzips1lNvlwW3HXzqQa395zumPDeImAAAYAgxEwCAKJvpiBnw+9RevU/t1fvUcWS/Bns6ZQQCssYnKK2wTM7SJcpedKbsiSlhzx1LLDa7NnzmTj3x5X9QwDf0QVRtR6r+vseu9QvqlZE4OKl52vvitbWqUF0DIx9umG12rf/UnVyVCQAAcJpofOvl4M8mGSrP6Yja3DkpA0pzDKrLNfT3ZOOul09yBE6GiAkAAI5HzAQAIEpmOmK6utp16Nk/69DTD2mgvWXCfc02u0rWX6aFl9+grPLlYZ8rVmQuWKaNn/22XvrBF2UEApKkjn6HHt9dppLMbi3MbVdmkmvU7cMMQzrWl6CDzRk6cixNhkZ2MFmsOu/z31XG/IpT+VIAAAAwjdqr3w7+nJYwqMS4qV+VOcxkkvLS+oIxs6+lXu6+HsUlze4vD84EIiYAABgLMRMAgCma6YhpBAI6+PRDeuP/7pJv0DWpYwJej6o3P6rqzY+qZOMVOudjtys+OS3sc8eC+esulcls1pYf/rv8nqGrMQOGWdXH0lV9LF1xVp8yEl1KjPNKkvrddrX3x8vjG/1nkDXeofNu+64KVm06pa8BAAAA08cIBNRVVxUcO5Mm9zdzOE6cs7PmkHKXro76eU5XREwAADARYiYAABGa6YgpSe7ebr1417+qafcrY/7ebAoowe6V2SQNeq3y+C2j9jmy5e9q2vOqzrvtu7P2A5fiNRcrvahc2376VbUe2BnyO7fPqqbu5JPOkbv0bK371NeUnFMwXcsEAADADPB73cHHEkiKyrMyT+Swe0PGXlf4QW4uImICAIDJIGYCABCmWIiYkjTY06mn/+Pj6qytDNkeb/WpPKdDRc4epScMymIeWqNhSH1um5q6k3SoOUPt/Qkjc3W165k7/0UXfulu5Z+1IeI1zaSUvGJd9vXf6PCLj2n/3+9Vx5EDkzous3yZFl/xIZVseJdMZvM0rxIAAACnXujf3OH/BR/BGafwd/5cMNWIabeadcVKIiYAAHMFMRMAgEmKlYgpSX6vV89961MhIdMkQ8vyj+nMwtZgwDyeySQlx3uVHN+pipxO1Xcma3t1vlwem6ShW8++8N+36V3f+J2cZUumtL6ZYrZYtODC96rsgveorWqvGnduU/vhfeo4ekjegV7JZJI9IUnp8xfJWbZEBWdtmLWvFQAAAJNjscfJYo+T3+OWJPW7bVE/R9+gPWRsT+R5mWPxBwJ6fk+j7t9KxAQAAJNHzAQA4CSaOgf0x61VemZ35BHzpk3lWheFiDls959+obbKvcGx3erTRYtqlJ0yMOk5CtJ79b4Vh/TCgWI19yRJkvyeQW398Zd11XcfkMVmP8kMsctkMimrfLmyypfP9FIAAAAww0wmk9KLK9RWuUeS1N7niPo52vuPm/Od82HEcMS8b2ulGjsm/55lGBETAIC5jZiJqGtsbNT+/fvV1dUli8Uip9OpZcuWKT09faaXBgBhicWIKUkdRw9qz19+FRxbzQFduuSInEmDYc9ltwZ00eKjemZfiVp7EyVJXXXV2vXQPVp542eitmYAAABgJjnLlgZjZrcrXj0uu1IcnqjMHTCkhs6RZ7Sn5s2XzZEwwRFzBxETAABEAzETUfPWW2/p97//vaqqqsb8/TnnnKMPf/jDKiwsPMUrA4DwxGrEHLbnL7+WEfAHxyuLmyMKmcOsFkMby+v08Fvl8gUskqT9j/9By977EdkTk09yNAAAABD7ClZt1MEn/xgcH2zO0NklzSH7eHxmNXYlqb3foY5+h9xeiwyZZLP4lZbgljPRpdzUPiXHe0OOa+xMVp975K4m+as2Tu+LmQWImAAAIJqImYiK++67Tw888IAMY/wP/V977TXt3r1bt912m9asWXMKVwcAkxPrEVOSBjqPqeaVZ4NjZ9KAFuW2T3nepHivVhS16PWjeZIk36BL1S8+psVXfHDKcwMAAAAzLX/FeiVl56uvtUGSdLDFqYW5HUpxeNTRH68DTU4dbkuTP2Ae8/iWdx7LIEn5ab1amNuugvReBQyT3qzNCdl34aXXTd8LiXFETAAAMB2ImZiyRx55RH/84x9HbbfZbPL7/QoEAsFtg4OD+u53v6tvfvObWrRo0alcJgCMKxoR8+bzKrR2Yc60RcxhR7c9KcPvC46XzGtTtE5Znt2pt2pzgldnVr/4KDETAAAApwWT2axFV9yo13/3XUmSP2DW1soCZSUPaH9TpgxN/o/qhq5kNXQlKz+tV8nxbnUOjDwvs2DVJqXMK4r6+mMdERMAAEwnYiampK2tTb///e9Dtq1atUo333yzSktL5ff7tXPnTv3qV79SY2OjJMnr9eqnP/2pfvjDH8psHvsbjwBwKkxXxPR73OqoOaSOw/vl6mpTwO+XNS5eqfklcpYtVWJmrkwRFshjh/YEf7aaAyp29kQ0z1js1qH5qo8NPeO48+gB+b0eWWz2kxwJAAAAxL7F77pBh198VB1HDkiSjvUl6lhf4pj7Jti9Sor3yCRDg16rul1x0gnBs6ErWdLIYxms8Q6dc8vt07X8mETEBAAApwIxE1Py7LPPyuPxBMdr167V7bffHvyQ3mKxaPXq1aqoqNDnP/95tbS0SJJqamq0b98+LVu2bEbWDWBua+oc0P1bK/XMrgYFJrg99njGipiGYah572s68MQfVf/Giwr4fOMen5g5T+WXXKOKi94vR3pmWOfuOLw/+HNGoksWc/jrn0hm0kAwZgZ8PnXWVCpzwdKongMAAACYCWarTes/face++INMvz+Ub9PT3BpYW6HijJ65LCH/j3v9ZvV3J2og80Z70TM0V9OXHXT55ScWzBdy48pREwAAHAqETMxJXv2jFwhZDKZ9PGPf3zMq41SUlL0wQ9+UHfffXdw2969e4mZAE6p6YiYktRx9KC2/+w/1F69b1Lz9Lc16a37f6LdD/2Plr7nwzrzuk9M+urHvmONwZ/TEgbDewGTcOKc/ccaiZkAAAA4bSRlzZM9MUXuns7gNpvFr7PnN2lBdue4j3CwWQIqzOhVYUavWnsStK2qQD2DcSM7mEzKKF08zaufeURMAAAwE4iZmJLOzpE//tPT05WVlTXuvhUVFSHjjo6OaVsXABxvuiKmYRja85df6a0Hfh7yHMvJCvh82vOXX6tux2Zt+tx3lF5cMeH+hmEo4PMGx1ZzYIK9I2OzhM7p93nG2RMAAACYfV777XdDQmZSnFuXLj2i5HjvBEeFyk4Z0LvPrNRLh4pU15kytNEwtO0nX9G7v/eQrHGnX6QjYgIAgJlEzMSUxMWNfAuxv79ffr9fFotlzH17e3tDxg6HY1rXBgBTjZilOSn60MYFWrcoNyRiSpIRCOjle76uymf/Muq4nJQ+zc/sVmaiSykOt8wmQ26fRR39DrX0JKq6NV2DvpF/grvqqvXEVz6qS+74mbIqzhx3PSaTSWabXQHvUGD0Bsb+7+1UeP2hc1qsPC8TAAAAp4eGt7ar+oWHg+N4m1eXLTuipLjJh8xhVouh8xbW6tl989XckyRJ6mms0a4Hf65VN38uamueaURMAAAQC4iZmJKCggJVV1dLktxut15++WVt2LBhzH2fe+65kHFRUdG0rw/A3BSNiHnTpvJRV2Ie7/X//f6okJmd3K9zSxuVkTj69q9Wi0+JcUO3pTqrqEWHWjL0Zk2OfO8ESe9Ar56585N61zf/V+lFC8ZdW3J2vrobjkiSuvqj/2FA50DonEk5c+OZPwAAADj97f3Lr0PGa8saIgqZwyxmQxvK6/TwWxXBLwUeePKPWn7NP8qekDSltc40IiYAAIglxExMyXnnnacXX3wxOP75z3+uzMxMLVq0KGS/Rx99VE8//XRwHB8fr7Vr156ydQKYG6IRMW/eVK41E0RMSap/Y4v2PfZ/IdtWFjVraf4xmcc/LMhiNrR4XrsK03v0wsFidfQPXanuHejV1h/9u6789r0yW21jHussWxKMmR0D8fL5TbJawn+t4znWmxD82Wy1Ka1w/LAKAAAAzBZdddVqfntHcFyQ3qOijN4JjpicxDifVhS2aMfRPEmSb9Cl6s2PavEVH5zy3DOBiAkAAGIRMRNTsnr1aq1evVqvv/66pKFbyd5+++0655xzVFFRIY/HozfeeEOVlZUhx91yyy1KSEgYa0oACFtjR7/u31qlZ3dHFjHL3rkS82QRU5I8/b16+X++FrLtnJJGLZ7XHvZ5k+K9umzpYT25t1SdA0NBs+PIAe3+y6+04rpPjHlMVsWZOvzS45Ikf8Cso+2pWpDdFfa5x+L2mVXbkRIcO8uWyGIbO6oCAAAAs8mRrU+GjJfMa4va3AuyO7WzduSuK0e2PjHrYiYREwAAxDJiJqbstttu07e+9S3t3btXkhQIBPTKK6/olVdeGXP/a6+9VpdffvmpXCKA01S0IubahTkynSRiDtv/xP0a6GgNjksyuyIKmcPs1oDOX1irR3aVyx8wS5L2/vW3WvyuGxWXnDpq//nrL9OO//1e8LmZ+xozVZrVNakrQk/mULMzuAZJWnDBe6c+KQAAABAD2qr2BH922LzKTe2P2tx2a0CFGb060pYmaegLigG/T2ZL7H/sRsQEAACzQez/VYWYl5SUpHXr1gVj5kQcDoeWLVt2ClYF4HQ2ExFTkgJ+nw49/VBwHGf16ZySxrDPf6IUh0dnFbbo9Zp5kiS/Z1BVmx/R0nffPGrf+JR0zV93qQ6/+JgkqXPAoX2NmVqWP7Vvlne77NpVnx0c2xKSVLLxiinNCQAAAMSKjsP7gz87k1wK423ApDiTBoIx0+8ZVHf9YaUXV0T3JFEUjYh55apiXbu2lIgJAACmHTETU2IYhr7zne9o+/btk9rf5XLpP//zP/XJT35Sl1122TSvDsDpZqoRs/SdiLkuzIgZPP+ulzXQ3hIcl+d0KN7mD3uesVTkdmhXfba8/qFbU1U++5cxY6YknfH+f9TRbU8p4PNKknbW5igzaUC5qeF/CCFJHp9ZLx0qCrkqc9l7PyJbPLcDBwAAwOzn93o12NMZHKc53FE/x4lz9re3xGTMJGICAIDZiJiJKXnwwQdHhcxzzz1X11xzjUpLS+XxeLR792794Q9/UH19vaShAPqLX/xCFRUVKikpmYllA5hlTuUzMSfSun9nyLg8p3OcPcNnswRUktmlQy1OSVJ3fbXcfT2KS0oZtW9qQYlWXP9JvXnvDyVJAcOs5/bP1/kLa5Wf3hfWeV0eq54/UKyOfkdwW0bpYi1730en8GoAAACA2BHwe0PGZnMg6uewmEPfpwT8vqifYyqImAAAYDYjZiJiHo9HDz/8cMi2q6++Wh/96MgH4Ha7XevWrdOKFSv0la98RVVVVZIkr9erv/71r7rttttO6ZoBzC5TjZjpriYtb3pGhW9X69j+Im0vW6rsRSs0f+2lsicmhz1f++F9wZ8dNq9S4j1hzzGRnJSBYMyUpI7D+zTvjDVj7rv0vR9W467tat67Q5LkC1j07P4SLcxt18qiZtmtE39AYxjSkbZUvXYkT27fyJ8DtoQkbfjMN2W22qLwigAAAICZZ7HZQ8bDd0OJJo/fHDK22mMj+BExAQDA6YCYiYjt3r1bfX0jVwDl5+frH/7hH8bcNyEhQbfeeqs+/elPB7dt375dt956q8xm85jHAJi7phwxBxq1rOU5FXTvk0mSIamrtkpdtVWqfuFh7fjtf6t001VafvXHlJSdN+l5e5pqgz9nJA6Gva6TrjvRFTLubqwZN2aaLVZd8KUf6pmv/7PaKvcEtx9sdqq6NU1lWV0qdnYrI8mluHfCpj9gUtdAnJq7k3SoJUM9g3Ehc1rjE3TRv/1Y6UULovzKAAAAgJljtlgVn+rUYHe7JKmjP/pR7vg7nUhSyryiqJ8jHERMAABwOiFmImKtra0h43PPPVcWy/jfbiwqKlJ+fr4aGhokDV3Z2dHRoczMzGldJ4DZIxoRc3nzs8rv2a+JbibrG3Tp0NMP6fBLj2v1hz+viks+MKlnaPrdIwHTZo3OszKPZ7eEzun3TPwsH3tCki79j19q64+/rNpXnwtu9wUsOtji1MF3rvKMs/pkMhny+CwKGGN/gSQpO0/nff77ylywdIqvAgAAAIg91riRINfe55DPb5LVEv57jvEc6x153rzJZFZi1uS/NBlNREwAAHA6ImYiYv39/SHjhISEcfYckZSUNGoOYiaAhnci5nNRipg2i1/ORJdSE9yymALy+i3q6I9X50B8SMzzDQ7olXvuVNOul7Xx1m+Puv3UiczWkX82A4HIn705noAROufx5xuPzZGg8//1Lh3Z+oRe+9V/yd3XPWqf428jO5aFl12nVTffJpvj5P8dBwAAAGaj459h6QtYdLQ9VQuyu6Iyd9+gTY1dI593GEZAnv5exSWlRGX+yYhGxLxiZZGuW1dGxAQAADGHmImIpaamhoxramom3N/v9wevyhxvDgBzS7QjZm5qnxbPa1NBeq/MY7RGj8+sw8fStL8pM+QWqzWvPCvff39OF3zxblls4z8rMiEjW/1tzZKkblfcuPtFqnsg9EODhPSsSR1nMplUuvEKFazcqOoXH9PBpx5Qd/3hCY+xJSRpwfnvUcVl1ymtoDTiNQMAAACxzggE5OpqC9m2rzFTpZldisaTb/Y2Zkkn3Bums+aQcpeunvrkJ8GVmAAAYC4gZiJiixcvDhm//PLLamhoUH5+/pj7P/300yHP2MzNzVVaWtp0LhFAjJpqxMz0tWtJ7ePBiGm3+nRuSZNKMrs00d1i7daAFs3rUHlOp96qy9bbDVky3vnQoeHNLXr9f7+ncz/+b+Men1G6WMcO7ZY0FDM9PrPs7zyPMhraTnjOjrNsSVjH2xOTtfiKD2rRu25QT+NRtVfvU/uR/XL3dMoIBGSNT1BaYZmcpUvkLF0siz36QRYAAACINX6vW4Y/9JEOnQMO7WnI0pmFx6Y0d0tPgg42Z4za7nX1j7F39BAxAQDAXELMRMQKCwtVXl6uyspKSZLP59Odd96pO+64Y1TQ3L59u37961+HbNu0adMpWyuA2DDViFmWk6J1pkr5nrw7+L3nRLtHly49ohSHZ9LzWMyGVhW3KCvJpRcPFQZvPXvgiftVdO5Fmrf8nDGPyyxbpoN64J2RSUfbUlWR2xn26xiLYUhHjqUFx3Ep6RE/Z8dkMik1v0Sp+SUq3XRlVNYHAAAAzF5jf+NxV32OMpNcyk/vG/P3J9PvtmnLocIx5zdN9C3LKSBiAgCAuYiYiSn5+Mc/rn//93+X/51vODY2Nuozn/mM1q5dq5KSEnk8Hu3evVv79u0LOS4jI0NXX331TCwZwAyYasRckJuiD20q1+KEfj162/8LflRgNft1yZKjYYXM4xU5e7S2rEHbqgqD27b//D919Y8eltk6+nazhedcIMsv4+X3DEqSDjQ7VZ7TOeHVoJPV2JUUcuvbkg3vmrYPQAAAAIC5xGKPk8UeJ7/HHbLdMEx6/kCxNpTXqyRz9LPnJ9LZH6fnD8xXv8c+5u/tidF9XibPxAQAAHMZMRNTsnjxYn3605/Wj3/8YwUCQ7da9Pl82rJli7Zs2TLmMQ6HQ1/+8peVmJh4KpcKYAY0dPTr/i1Vem5P5BHzpk0VWlORLZPJpFd+8Q0ZgZHbQ60qblZqgnuCGU6uLKtLdR0pqu0YeoZvX0u96nZsVvHaS0btG5eUopIN71LV83+VNHRrqv1NTi3Ja5/SGnx+k149EnoV5sLLrpvSnAAAAACGmEwmpRdXqK1yj6ShL0X6AhZJUsAw66VDRapt79Lq+U1KjPNNOJfXb9K+xkztrs8O3uHlxDn1zvmigSsxAQAAiJmIgosuukhOp1M/+tGP1NbWNuG+xcXF+td//VcVFRWdotUBmAnRjpjS0DNnql98LLhPqmNQC3M7prxWk0k6u6RJdR0pwednHnjqgTFjpiQtfc8/qPrFR2X4hz7keLM2V7mp/cpIHIzo/IYhvXY0T73HXZVZePYFSisojWg+AAAAAKM5y5YEY6YvYFFWUr+O9Y18yfpoe5pq2lNVlNGjImePnIkuJcV7ZDIZcnmsau93qLk7SVWt6fL6LSFzJ9i9kmEEY2Zq3nzZHAlTWi8REwAAYAQxE1GxYsUK3XPPPXrppZf02muvqaqqSt3dQ7doSUtL08KFC7Vu3TqtXbtWZrP5JLMBmK2mI2IOa9z9inyDI2/iF+Z2ROX2rpKUFOdVQUaP6t65OrN57w55+nvGvDVUWmGZzvjAP2rXAz+XJPkDZj3zdokuXnJEzqTwgqZhSK8fnafKlozgNltCss79x3+fwqsBAAAAcKKCVZt08MkHgmNn0oDSEtyqbB35W9yQSTUdqap5533BZGQl96s8p0Pbj3t0Rf7KjRGvk4gJAAAwGjETUWOz2XTRRRfpoosumumlADjFpjNiDmuvejtkXOwM75k2J1N8XMyUYaj98AHNW37OmPue8f6Pq/6NLWqv2itJGvRZ9fc9ZTqrqEVL8tpknkRk7XHZta26QK09obfcXvOP/65EZ86UXgsAAACAUHlnrlNSdr76WhskSZWtTr3nzEoVO7v1cnX+uM++HI/VHNCKwhZV5LbriT1lIb9beOm1Ya9vOGLeu6VSTZ1ETAAAgOMRMwEAEWto79d9Wyv13J4GRdAwtSA3RTefV6Fzy8ePmMM6jhwI/uyweZVgn/hZNuFyJrlOON/+cWOm2WrTRf/+Yz11xy3qbjgiaehZO2/UDF1luTC3XfMzu0etMRCQ2voSdKglQ0faUkOesSNJKz/0WZVuujKKrwoAAACAJJktFi264ka9/rvvShq6w8q2qgJdtvSwrl55SLUdKTrYnKGWnqQJ50mOd2thTocWZHcqzubXm7U56hxwBH9fsGqTUvKKJ70uIiYAAMDJETMBAGEbjpjP72lQIIKImT7QoMty+nXjB29RXNLkbuE02NsV/Dk53hP+SU/ixDkHe7rG3vEdjlSnLr/zt3r+2/9Pxw7tDm7vGYzTjqN52nE0Tw67V6nxbpnNhtxeizoH4kcFTEkyWaw6+yNf0OIrbozKawEAAAAw2uJ33aDDLz4a/KJka2+itlYVakN5nUoyu1WS2S23z6yOPoeaexLl8lhlGCbZrX5lpwwoM8mlBLs3+LiLg80Z2lOfHZzfGhevc265fVJrIWICAABMHjETADBp0YiYy5ufVX7PAZkOSY+8/bg2fOYbmnfGmpMeawT8wZ9NpghOfhInznn8+cYTn5qhy7/xO7398O/01gM/V8DnDfm9y2OTy2ObcI70+Qu14dN3KqNkUfiLBgAAADBpZqtN6z99px7/0gcV8A3dReVIW5pcHqvOLWlQa2+i6jpT1N7nkMsb+nd8ZatfGYku5ab2q8TZqQPNmTrQnBmyz8qbPqfk3IIJ10DEBAAACB8xEwBwUg3t/bp/a5We21MfUcQsDhzWeX2PyV5fo4HjnkUz0NGqZ77xSW367H9p/vrLJpzDFp8Q/HnQG/1/vk6c8/jzTcRssWr5+z+u4rWXav/f71P15kfkHeg76XHOsiVaeNn1Kt10lSy2iYMnAAAAgOjImL9Q6z99p7b88N81/KyM5p4kPbyrQtL4j77w+i1q6UlSS0+SdtVlj9q3/OJrtOhdN4x7PBETAAAgcsRMAMC4pnol5gJTlW623adzLa/JlCQFcqSa9lS9dmSeBt/5prPh9+mlu2+X1ZGggpUbx50rtaBULfvekCT1uOLk9Ztks0TvCs2OPkfIOK2wLKzjU+YV6dyP3a6VN/4/Ne7arvbqt9V+eL9cXe0K+HyyxjuUmj9fztIlylmySs7SxVFbOwAAAIDJK914pWRIW3785aEH20uaKGSOdkLIvOQDWvOPX5bJNHoOIiYAAMDUETMBAKNENWIe937ebJJKMrs1L7VPWysL1NCVImnolq5bf/wVvffuv8iR6hxzzsyypTqkh4b2l0ktPUkqSO8Nf3HjaOpODBk7y5ZENI/NkaDiNRereM3F0VgWAAAAgGngdfUHr8w8nsUcUG5KvzKSXEqO88hkMuTyWNXe71BLT2LwS5nH87kGJCMgyRLcRsQEAACIHmImACBouiLmieJtfl24qEabDxarrnMoaLp7OvXqL7+l87/w/TGPyVl2dsj4UHNG1GKmz29S9bH04DgpO0+JWXlRmRsAAABAbDn49EN65RffCNkWZ/VpecExLcjqlM3qV48rTm6fRYYhpScMavG8dplMUl1HinbXZ6lzYOTOLke2/l2GEdCmW7+tgETEBAAAiDJiJgDglEXM45nN0saKOj28s1z97zxHs+blZ9RWtVeZC5aN2j8lt1Dzzlijpt2vSJLqO5PV1udQZpIr/AWf4ECzU27fyD+JFZdcO+YtogAAAADMbscO7darv/xmyLbC9B6tLGpWfVeynj9QrI5+h3wBc8g+JhlKSxhUbmq/NpTXq6Y9RbvrR56deXjb0zqcvkIv9s8jYgIAAEQZMRMA5rD69j7dt6VKL+yNPGLeZLtPa8KImMezWQJau6BBz+4rCW47+NSDY8ZMSVp0+Q3BmGnIpG2VBbrqzCpZzJE/O7PbZddbdTnBsdlm14KL3hfxfAAAAABik9/j1raf3CEj+JxMqSyrQyaT9OjuBQoY5nGPNWRS54BDnQMO7W/K1LzUPq0satbrtXk6mn6W9uZeqL76VEnhhUy71ayrVhXrA0RMAACAcREzAWAOmmrEzHbX6lPJvwvrSszx5KX2KSPRpY7+ods0Hdn6hM655XbZHAmj9i085wLlLj9HzXtekyR1ueK1pbJAmyrqZI5gHS6PVc/vny//cd+6PvMD/zTuczsBAAAAzF77HvuDuhuOBMfpCS41dCWP+RzMk2noTtF2ywbtX3qhumxZYR8/HDGvXVeqjCQiJgAAwESImQBwGjEMQ30tDeptqZPf45bZalNSVp5S8oplMpunHDHTBxq0vPlZfTD3aZVau6OyZpNJqsjp0CuH8yUNfVu6/fA+5S5dPca+Jq3/5Nf08OfeL9/g0O1la9rT9Px+s9YvaJDD7pv0edv74vXioSL1DsYFtznLlmjZ1bdM8RUBAAAAiDUBv08HnvxjcGw2BUKeeznMbvUpP61PzkSXkuI9MpkMDXqsau93qKk7Sd2DDh1NXzF0JWZc+F+CJGICAACEj5gJALNcwOdV7WsvqOr5v+lY5W55+npG7TOQXKCDJVfpgLlIhsK/hDHP7taCA39Ufs8BmSTlVvRHYeUjslNC52uvfnvMmClJSdn52vCZb+rF738heHuohq4UPfxWglYWN6s0s0tWy/il1uWxal9jpt5uzAz53yI+zalNn/tvmS380wgAAACcburf2KKB9pbg+MRbyibYPVpR2KqScd5P+I1uPec7X3/13KhWU27Y5+d2sgAAAJHjE1sAmKWMQECHnvmTdj10j1ydx8bcpycuU3tzLlBN+goZpvGf/zKeBbkpuvm8Crn+9h3V9hyQJMXbvEoI4wrIyUh1uGU2BYIfKHQ3HJ1w/+I1F2vD//umtv7oKzICfkmS22fVy9UFeqMmV8UZPcpMGlBqglsWsyGvz6yOfodaexNU15kiwwgNuo60TF3yH/coZV5RVF8XAAAAgNjQsHPruL8ry+rUOSWNslsDo37nN8x6zn+B7vNeryYjT+F+N5QrMQEAAKaOmAkAs1Bfa6O2/fQONe/dMebvpxoxy+el6qZN5Tq3PFsmk0nPet3B39mt/ojXPR6zaWjeQe/QWv2ewZMeU7rxSiWkZ2vrT+5Q/7HG4HaPz6rK1gxVtmZM6ty5y87W+k/dqaTsvMgWDwAAACDmtVfvG3P70rxjWlXcLNMJkXJUxAyT3WLWVauJmAAAANFAzASAWabj6EE98/V/1mB3R8h2kwzFZyZoZ9ZF2mlfG5WIOcxssQR/DgTCn3cyjp/XNMlbveYuO1vv/cGf9ea9P9KhZ/+sgNcz6fPFp6TrzOs/oYWXXieTeXpeEwAAAICZZwQC6qytHLW9MKN7VMicasS0BLxa0PaqPnXL+7XwrCVTWTYAAADeQcwEgFmku+GInv7aP8nd0xncZpKhrEKLdmZerC3GJgVkmWCGsc1Pt+mjl545KmIOc6RnBX/ud9vk85smfC5luFweqzz+kXUnpGdO+libI1HnfvzfdOZ1/6Kq5/+mw1v+rq7aquDtZ49njYtXZsUZKr/oahWvuUQWmz0q6wcAAAAQu/xe96gvPsZZfVpb2hgMmdGKmEtaX5TD16cE/+XRWDoAAABEzASAWcPv9Wjz9z4fEjIDKSmqK1mv+03rFTDCj5gZA/Va3vycSs3HdNaH/zZmyJQkZ+ni4M+GTOrodyg7ZSD8FzGOtj7HCecL/xvM8SnpWva+j2rZ+z4qn3tQnTUH1d/eooDPJ6s9XsnzipSaXxJylSkAAACAuWD0+5xF89rlsPumfjtZuXVB4Bkl7Nslh69v5IzjvLcCAABA+IiZADBL7HroHnXVVkkaeibmobzzVJV6VkRXYi4wVWpp03NKaK6RSZJb0mu/+Y42fvZbY+6fuWBZyPhIW2pUY+aRtrSQsbNs6ZTms8bFK6viTGWdfFcAAAAApzmLzS6ZzJIRkDR0d5uy7C497btoShHzSusTus72J6WrS4/YytXlG3k2pi0hKWrrBwAAmOuImQAwC/Qda9Lev/1WPXGZ2ptzoWrSz4zsmZjmSt1ku0/nmnfIW2TW3zoq5PLYJEmHX3pMCy+/XtkLzxx1XPr8hUrJm6+exqOSpOpj6TqrqEV2a2BKr0uSBjxW1bSnBMdZFWcoKTv8DxMAAAAAYCwms1kWu11+96ACMqs1d6k+E/h/avJHFjGvsj6ha21/UoapK7g9L61PXa6RmJmYmRuNpQMAAEDETACYFbY9/rC25b8/4ohZYT6kD9nu17nmHcFnwtitAa0ra9Bz++cH9zv45B/HjJkmk0kLL7tOO37735Ikr9+i14/O07oFDRG9nmGGIb16OE8BY+Q1Lbzs+inNCQAAAADHMwIBeT1eHc1Yqb05F6ovzikZ4c0xXsQc5kxyhYz7WhuVlMWXNAEAAKKBmAkAMayurU/3bTmkF2rzZWQUhn18hfmQbrLdp3PMr2usR7YUpPcqM2lAbX0JkqSj25/W2bd8SfHJaaP2XXDBe7Xnz7/U4DvP7KxszVB+eq+KnT1hr2tYVWu6ajtSg+Ok7DzNX3dpxPMBAAAAwPH8gYCefvOoHlv0uaGIGaY4DepK6xO61vbnMSPmMIfdGzL2DvSNsycAAADCRcwEgBhU19an+7dW6YW9DQoYGnq+SxhOFjGPtzC3XW1VQzEz4POqdf9OFZ1zwaj97InJWvNPX9Hm730+uO2lQ4U6f2GtCjN6w1qfJB0+lqqXq/NDtq375NdksceFPRcAAAAAHM8fCOj5PY26d0ulmjoHpDBD5mQj5nhM5vDvqAMAAICxETMBIAq66qrVun+n2g/vU09jjXwel8wWmxIysuUsWyxn2TLlLD5LZqttwnlGRcwwhRMxh81LDf3GcHv1vjFjpiQVr71EpZuu1OGXHpckBQyznj9QrCXz2nVWUbOslpMv2uMz6/WaeapsyQjZvuiKGzVv+bmTWzQAAAAAjGFUxAxTpBGzb9AeMrYnpY6zJwAAAMJFzASACAV8Xh3Z+qQOPvWAjh3aPe5+R7c/JUlyZGSr4pJrtPDS6+RIC/1W8ExEzGEJdp/ibV4NeodCa2dt5YT7r/vEf8rV1a6m3a+8s8WkfU2ZOtqeqoqcDpVldyopLvQWS4Yh9brtqmpNV2VLevBcw4rOvUhnf+QL4S0cAAAAAN7hDwT03J4G3belagoR88lxn4l5Mu39jpGByaT0ovKw5wAAAMDYiJkAEIGOIwe09Sd3qPPowUkf4+po1a4Hfq59j/5B53z0iyq74D2qb++fsYg5zGSSHDZfMDD6XP0T7m+xx+mif/uxtvzw31TzyrPB7QMem96qy9FbdTly2LxKSxiU1RKQz29RZ3+8Bn1j/5NTdsF7te5fviqzhX+SAAAAAIQnOhEz8tvJSlLAkOo7k4Pj1PwS2RwJEc0FAACA0fjkGADCtO+xP+j1398lw+8b9TuTDKU43Iqz+uUPmNTtipMvYAnZxzvQqyd+9UMd2d6q/cqbsYh5PMMYmcRktkyw5xCLPU7nfeH7qn7hEb322/+WdyD0mZkur02u7olvqRufkq5z/+nLmr/20sgWDQAAAGDOmmrEtBluvdv29ylFzGENncnqd4/cZjZ78copzQcAAIBQxEwACMOuP/1Cb93/k5BtZlNAxc4eled0KCtpIOS5kYYhdbvidKQtTYda0tVqnqe9OReqJv1MGYY57PNnDNTrk0m/0Ya416ISMaWhbxH3uUfCoz0pZVLHmUwmLbjwvco7a50O/P0+VT77Fw32dJ70uOHb7S66/AbFp6RHvG4AAAAAc89UI6Yl4FF526ta1rZZ1y3dqVS7Z4rrMenNmtyQbSZT+O/1AAAAMD5iJgBMUuVzfx0VMrNT+rW+rF4pjrHfAJtMUlqCW5kFVj2ZfaVe9J8nI4I3thn9dVre/Jzyeg+qZPERmeIjeglj6jnh6lFn6eKwjk9Iz9LKD31WZ173CTW8tU1tlXvVcXif+lob5fd5ZbHZlZxTIGfZEmWWL1feGWtktk581SYAAAAAHG/KV2KaAipr2arFrVvk8PVJkrZVFeiypUdkMUdwu5x3vFWXrS5X6Bu01gNvRjwfAAAARiNmAsAk9LU26LXffDtk29K8Y1pV3DzhFZJ1gXzd571Bm/2bFJBFCvNqyoq8VH1geYaOfOffgofWdaSoIL0vvIkmUNcReiWms2xJRPNYbHYVnX2Bis6+IBrLAgAAAICpPxPTatZVq4uVsflu9Te+EvK7Y72J2lJZoE3ldTJHcDHl/ian9jZkj9reXX9YPrdL1jhH+JMCAABgFGImAEzC9v/5unyDruB4YW77hCGzNlCg+73Xj0TMMFXkpermTRU6e0GWJKmrsExdddWSpMPH0rSquFl2ayD8F3KCgCEdbMkIjuOSUpW9cMWU5wUAAACAqYhWxLx2bZnSEu2673e7jvutoeFvmta0p+kpr03rF9QrJX5yt5z1+s1642iuDrY4x/y9EQios6ZSWRVnhL1uAAAAjEbMBICTaKvaq6ZdLwfHaY5BnT2/acyQGc2IaTruBAsvv16v/vJbkiRfwKI3a3O1prQx/Bdzgn2Nmep324PjBRddLYs9bsrzAgAAAEAkohExr1xdrGvXliojaej2r36PO+TLqYXpParrTNFw0GztSdSjb5Vr0bw2LczpUFK8d8y5PT6zDh9L097GrJD3UZJUkN6t+s7U4Njd2x322gEAADA2YiYAnMSBJx8IGa8paxj1TJWpRkxnf51W9ryiW790j6xjxMSy896tt/74M7l7uyRJB5udKsroUV5a5Leb7eyP087anODYbLVp4WXXRTwfAAAAAERqOiLmMMMIvatNeqJbJVl12lpZoIAxdH9ZX8CsvQ3ZershSxmJLjmTXEqK88hkklxeqzr6HGrrS5AvMPp+tIty21SU0RMSM42AP+zXAAAAgLERMwFgAgGfVzXbnwqOMxJdyk4eeWM91YhZ6K1WWe1Lmtd7SCZJzXteVcGqTaP2szkSdc7HvqQtd/9bcNvmg0W6eMnRkPVMVrfLrmf2lwTfuEvSius/oeScgrDnAgAAAIBITWfEHGaxxclkscrw+yRJLo9VZxV1K9Xh1tbKAnUOjDzb0pBJ7f0Jau9PmMS5fTq3tFHznd062p4a8jubIzHs1wIAAICxETMBzHoBn1edtVVqP7xP3fVH5HO7ZDJb5Ehzylm6WM6ypXKkjf0sk5PpqquWzz0YHJdmdclkGoqY93lv0Iv+jRFFzIXmg7rZdp8WmXbrL72LgtvbKveMGTMlqWTDFap5+VnVvvqcJMnrt+jpt0u0sqhZi+e1j/v8zuMZhnSkLVWvHsmTxzfyT4BzwTItfe9Hwn4dAAAAABCJUxExh5nMZqUVlKqz5pAkqb1/KF5mJA7qyjOqtb/Jqf1NmRrw2CZ1bqvZr9KsLq0obJXDPhRI2/scIfukFS0I9yUBAABgHMRMALNWT1OtDj79oKqff1juvomfR5K9+CwtvOx6Fa+5RBbb5N6gSlL74f0hY09Smr7t/og2+zfJ0OjbC53McMRcbX5DJpNkxEvxVp8G3wmLJ57veCaTSRs+8w091das9uq3JUn+gFk7jubpSFualuQN3droxFvgSlLAkBq7krW/yanGruSQ3yVl5+vCL90ts4V/EgAAAABMr1MZMY/nLFsSjJmdA/FyeSxy2P2ymA0ty2/Tkrw21XWkqK4jWR39DnUNxMvQyDdGk+I8cia6lJvap9KsLtmtI7euNQypqTspOE7MylN8SnrYrw0AAABj45NrALOO1zWgN/5wtw4+9cDQu8ZJaN2/U637d2pn9o+17pNf07zl50zquIH2FklSd1yW9uZcqPutZ8jwTz1iDjOZpNSEQQ32DL3xHehonXAemyNRl/7HPXruW59R64Gdwe1tfQl66VCRbBa/MpNcSk9wyWYJyB8wq3MgTu19CcFgerzU/BJd8tV7lJCRHfZrAgAAAIDJmqmIOWze8nNV9fzfJEmGYVJVa4aWFxwL/t5skoqdPSp29ryzXpM8PrMMmWSzBGSzBMaaVpLU1udQR//IlZmTfb8JAACAySFmAphVjlXu0Ut3fVF9rQ3j7mM1BxQwFPI8yGF9rQ16+j8/rkXv+qDO/sgXZLZOfJVms8ukbUXXqyb9DMkUWcS8yXa/zja/Pu5tYI+/ktLv9Z50Tntiii772q+0+y+/0u4//TL43Bdp6NazTd1JId8KHndtl9+gVTfdKpvj5M+CAQAAAIBI+PxDEfP+rTMTMYcVr7lYr/3mO3L3dkmS9jU5VZ7ToXibf8z9LWZDDvvYvzueYUhv1eWEbKu49NqI1wkAAIDRiJkAZo3mvTv03H99Wr5BV8j2pDiPKnI6lJPSr/REl2wWQ4Yh9bttautzqKY9VTUdqTKMkZp44In71d/WpPM+//0xbztb29an+7ZUanNtvoyMgrDXOt6VmGPxHnelp8Vun9T8ZqtNK677hIrOuUi7H/of1b72gozAyd9oS1LeinVa/v6PK3fp6kntDwAAAADhipWIOcxij1P5Re/X3r/9RpI06LXptSN52lhed9L3bBOpak0PeZSHs2ypMhcsm+pyAQAAcBxiJoBZoePIAT33X58JCZlxVp/OLmlSaWbXqDefJpOUFO9VUrxX8zN71O9u0mtH8lTbkRrcp27HZm376R3a+Nn/kumdCYIRc2+jhq6XDO9dbTgRUxr6Fm9n/8gb85TcorDOlzG/Quf/613qb2/RkS2Pq/XgbnUc3qf+tubgPvGpGUNvqMuXqXTDFUrJKw7rHAAAAAAwWbEWMY+3/P236PBLjwUf73GkLU2pjkGdWXjsJEeOrak7Ua8czguOTWazzvnY7cH3lwAAAIgOYiaAmOf3erTlh/8m3+DIG+GspAFdsKhGDrtvgiNHJMb5dP7CWlUfS9P26oLgVZpHtvxd+SvWy7b0fN27pUovvj0cMcOzyHxAN9nu02rzm2F9q7dzIF6+gCU4dpYtjuDsUqIzR8ved0tw7Pd6FfB5ZLbaZLFN7mpPAAAAAIhULEfMYfbEFK39xH/ouW9+Krjtrbpcubw2rS5uktUyuXeDhiFVtqbr1cN5IY83WXLVzcpeeGbU1w0AADDXETMBxLxdD92jrrrq4DgzaUCXLD0imyUQ1jwmk7Qgu0tWc0AvHiqSZFJ3XJa++/f9OrrFfEoj5rDKlvSQcdbCFRGsYjSLzTbm7XMBAAAAIJpmQ8Q8XsHKjTrjA/+s3X+6J7jtYLNTjV1JWlnUrKKMHpnNYx9rGFJbn0Nv1eWE3FpWknKXna2zbvzMdC4dAABgziJmAohp7t5u7Xv098Gx1ezXeRW1YYfM483P7NHeAZsesVyjmrQzJNM471QnMNWIKUkuj1XVx0ZiZkrefGUvOiuyyQAAAADgFJptEfN4K274pAJ+n/b+9dfBbb2DcXrxULEcdq8K03vkTHQpKd4jk2novVt7v0PN3Unq6HeMmi932dm68PYfcVccAACAaULMBBDTql54WH6POzg+q6hFSfHeiOerDRToXu8NejFzkwyFHzGd/bVaeexpfbLsUSVY/BGvwzCklw/nyesfucXswsuu5dkqAAAAAGJaVCLmqmJdu+7UR8xhJpNJq276rNIKSvTqr78j70Bv8Hcuj02HWpyTm8ds1pKrbtZZN36GkAkAADCNiJkAYlrVC38L/myz+FWe0xHRPDWBQt3nvUEv+jdGFDHzrf0qP/ig5vUekknS1qoiXbT4qCzmSG5OK+1rcqquIzU4TsrOU/nF10Q0FwAAAABMt9l8JeZ4ys5/j3KXn6vXf/c91bzyjIzA5O8A5CxbqnM+djvPyAQAADgFiJkAosbvccvrdslkMsvmSJDZMrX/xHj6e9VVWxUcl2R2yWYJLx5ONWIuyk/TzedVaHluvB697dfqf+cLu03dSXpuf7E2VdQp3jb5KzQNQ9rTkKWdtbkh29d98uuyxSeEvT4AAAAAmE6nY8Q8XqIzR+d9/rvqbWnQm/f+UI27tsvT1zPmvpY4h5wli7Tixk9r3tKzT/FKAQAA5i5iJoCIeV39OrL1CTXteU3t1fvU21wb/J3ZalN6cbmcZUtVePb5yl+xXiZzeDGx48iBkHF28uTfOEcrYq4qzQze+nXD//umnvn6PyvgG7rNbVN3sh5+q1znljSq2Nlz0mdndg3E6eXqfLX2JoZsX/a+WzRv+TlhrxEAAAAApsvpHjGHBfw+HXzqQe1//F71NtdNuK/f7VLrgZ164du3qvzC9+mMD/yT4pJTJzwGAAAAU0fMBBC2we4O7XroHlVvfkReV/+Y+wR8XrVX71N79T4devohJeUUaMmVH9LCy6+f9BWbJ76RTE8cPOkx0xExh+UuXa3zvvA9vfi9LwSD5qDXphcPFSs53q3y7E5lp/QrI3FQNktAAUPqHohTW1+CjrSlqqk7edT5Ki69Vitv+mzY6wQAAACA6RCNiHnV6mJdu7ZM6Ulx07DC6Omqq9bWn9yh9qq9YR3nHejVvsf+T4e3/l1r//mrKjrngmlaIQAAACRiJoAwHd3+tF755Tfl7ukM67i+lnq99pvv6PCWx7X+U3cqrbDspMf4vZ6Qsc0y/u1cpxoxnf21Wtm9XZ//yu9HRczjFZ19gS6543+05Yf/poGO1uD23sE4vXncrWNNMmRo/HlMFqtWXP8JLX//xyc8HwAAAACcCnMpYkpS7Wsv6KUffFF+j3vU71Idg3ImuZQc55HJJLm8VrX3OdTRH6+AMfJ+c7CrXS9857Na9r5btPKmz/LeDgAAYJoQMwFMihEIaMf/fk/7H/vDqN9ZzAE5E11yJrkUb/PJMKTeQbs6+h3qHHCE7NtWuVePffEGbfrcf5/026tma+h/onyB0ZFyqhEzz31EFfUvaF5vpRxpzkm9+cxddrbee/dftON331fV838dc5+JQmZGySKt//Sdypi/MOz1AgAAAEA0zbWIKQ2FzM3fvU1GYOQLs2ZTQAtzO7Qwt12pDs+Yxw16LapqTde+xky5vLbg9r1/+40Cfp9Wf/jzBE0AAIBpQMwEcFKGYejVX39bB5/8Y8j2RLtHS/PbVJbVKbs1MOaxPS67DjY7daA5I/gNVr/Hrc3fvU3nf/EuFZ09ftBMzMwNGXcPxCs9Yehbs1ONmIvNB3ST7T7VH+xVt2vo+S2JztyTHDXCnpii9Z/6mpa97yM6+NSDqt78iDz9vePubzKblb9yoxZdfoPyzlwb9vNDAQAAACCa5mLElKSu+sN66QdfDAmZzqQBbVhQr7SE0VdpHi/e5tey/DaV53Rox5E8VR9LD/5u36O/V1phmcovunra1g4AADBXETMBnNTBpx4cFTIrctq1qrh53Ig5LMXh0dklTSrP6dC2qgK19SVIkoyAXy/d9UW9+3sPKjW/ZMxjnaVLQsbH+hwyZaREJWKuMr8pX8Csfa6RczjLlkxw5NhS80t0zi1f0uoPf15ddYfVfnifehqOyu8ZlMlqVUJGjpyli+UsXSybIzHs+QEAAAAgmuZqxJSkgN+vbT+5I+TWsgXpPTp/Ya0sZkOS5A+Y1Nkfr47+eLl9VhmSbJaA0hIG5Ux0yW4NKM4a0IbyeqUlDOqNmnnBuXb87rvKO3PtqC/mAgAAYGqImQAm1NtSrzf+766QbWfPb9SSvPaw5klLcOvyZYe1+WCR6jtTJA1dobntp1/V5Xf+TmaLZdQx8akZSsrOU19ro7rjsvWbuOt0ZPDMKUfM4bv+HD6WFnI72MwFy8Ked5jZYlXG/AplzK+IeA4AAAAAmC5zOWIOO/TMQ2qr3BMcOxMHdP7CWplNhuo7k3Sw2anGrqSQ52KeyJk4oIrcDpVkdmlZfpsGPDbtb8qUJHkH+vTab76jC774g2l/LQAAAHMJMRPAhF779bflG3QFx8vyjoUdModZzIbOX1irv+8pU0f/0LM0jx3cpcrn/qKFl1475jGJa67WU7vaVZO2XDJFFjFvtt2rleadOv7RJQFDOtDsHFmbPV5F514U9vwAAAAAEMuImEOMQED7H7s3ODabhq6ubO9zaHt1fvDxIyfT3p+gl6sT9EZNrlYVN+uswmY1dCWp553ja197Xn2tDUrKzp+W1wEAADAXETMBjKunqVb1b7wUHKclDGpFUcuU5rSYDW1YUKfHdi8Iftt1/+P3quKSD8h0XG2sOdar+7ZU6cXaAhnpBWGfZ7yIOWxfY6a6BkberJZufJfiklLCf0EAAAAAEIOGI+Z9WyrV3OU6+QEnOF0i5rCmPa+pp6kmOC7P7lBla7r2NWZKGuNN40l4fFa9XF2gmvZULcs7pu3VhUO/MAwdfPpPWnXTZ6O0cgAAABAzAYzr4NMPhoxXFTUHnyMyFemJbpVnd+pgy9CVkd31h9Xy9uvKXXb2SMR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     },
     "metadata": {
      "image/png": {
       "height": 691,
       "width": 921
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    ggplot(anscombe_quartet, aes(\"x\", \"y\")) +\n",
    "    geom_point(color=\"sienna\", fill=\"darkorange\", size=3) +\n",
    "    geom_smooth(method=\"lm\", se=False, fullrange=True, color=\"steelblue\", size=1) +\n",
    "    facet_wrap(\"dataset\") +\n",
    "    scale_y_continuous(breaks=(4, 8, 12)) +\n",
    "    coord_fixed(xlim=(3, 22), ylim=(2, 14)) +\n",
    "    labs(title=\"Anscombe’s Quartet\") +\n",
    "    theme_tufte(base_family=\"Futura\") +\n",
    "    theme(\n",
    "        axis_line=element_line(color=\"#4d4d4d\"),\n",
    "        axis_ticks_major=element_line(color=\"#00000000\"),\n",
    "        axis_title=element_blank(),\n",
    "        plot_background=element_rect(fill=\"#ffffff\", color=\"#ffffff\"),\n",
    "        dpi=144,\n",
    "        panel_spacing=0.09,\n",
    "        strip_text=element_text(size=12),\n",
    "        title=element_text(size=16, margin={\"b\": 20})\n",
    "    )\n",
    ")\n",
    "\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3eec7054-2f37-41e0-b68d-98596e330756",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.save(\"anscombes-quartet.png\", verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aae45dd-13dd-4b42-a6f2-b7a71d5db29f",
   "metadata": {},
   "source": [
    "## Bonus: Apply Linear Regression\n",
    "\n",
    "In this bonus section, we use scikit-learn to fit a linear regression line to each dataset and confirm that the intercept, slope, and $R^2$ are also identical.\n",
    "The `geom_smooth()` function added regression lines to our scatter plots, but now we get to see the raw values as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "430ba6d4-b14d-4ae8-8725-d85f99980474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (4, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dataset</th><th>mean_x</th><th>mean_y</th><th>variance_x</th><th>variance_y</th><th>correlation_xy</th><th>intercept</th><th>slope</th><th>r2</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;I&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.13</td><td>0.82</td><td>3.00</td><td>0.50</td><td>0.67</td></tr><tr><td>&quot;II&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.13</td><td>0.82</td><td>3.00</td><td>0.50</td><td>0.67</td></tr><tr><td>&quot;III&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.12</td><td>0.82</td><td>3.00</td><td>0.50</td><td>0.67</td></tr><tr><td>&quot;IV&quot;</td><td>9.00</td><td>7.50</td><td>11.00</td><td>4.12</td><td>0.82</td><td>3.00</td><td>0.50</td><td>0.67</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4, 9)\n",
       "┌─────────┬────────┬────────┬────────────┬───┬────────────────┬───────────┬───────┬──────┐\n",
       "│ dataset ┆ mean_x ┆ mean_y ┆ variance_x ┆ … ┆ correlation_xy ┆ intercept ┆ slope ┆ r2   │\n",
       "│ ---     ┆ ---    ┆ ---    ┆ ---        ┆   ┆ ---            ┆ ---       ┆ ---   ┆ ---  │\n",
       "│ str     ┆ f64    ┆ f64    ┆ f64        ┆   ┆ f64            ┆ f64       ┆ f64   ┆ f64  │\n",
       "╞═════════╪════════╪════════╪════════════╪═══╪════════════════╪═══════════╪═══════╪══════╡\n",
       "│ I       ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ II      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ III     ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ IV      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "└─────────┴────────┴────────┴────────────┴───┴────────────────┴───────────┴───────┴──────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def fit_lr(s):\n",
    "    lr = LinearRegression()\n",
    "    X = s.struct.field(\"x\").to_numpy().reshape(-1, 1)\n",
    "    y = s.struct.field(\"y\").to_numpy()\n",
    "\n",
    "    # Compute intercept and coefficient\n",
    "    lr.fit(X, y)\n",
    "    intercept = lr.intercept_\n",
    "    slope = lr.coef_[0]\n",
    "\n",
    "    # Compute R^2\n",
    "    r2 = r2_score(y, intercept + slope * X)\n",
    "    \n",
    "    return {\"intercept\": intercept, \"slope\": slope, \"r2\": r2}\n",
    "\n",
    "stats = anscombe_quartet.group_by(\"dataset\", maintain_order=True).agg(\n",
    "    pl.col(\"x\", \"y\").mean().name.prefix(\"mean_\"),\n",
    "    pl.col(\"x\", \"y\").var().name.prefix(\"variance_\"),\n",
    "    pl.corr(\"x\", \"y\").alias(\"correlation_xy\"),\n",
    "    (\n",
    "        pl.struct(\"x\", \"y\")\n",
    "        .map_elements(fit_lr, return_dtype=pl.Struct({\"intercept\": pl.Float64,\n",
    "                                                      \"slope\": pl.Float64,\n",
    "                                                      \"r2\": pl.Float64}))\n",
    "        .alias(\"lr\")\n",
    "    )\n",
    ").unnest(\"lr\")\n",
    "\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb781c4a-6e2e-4fc2-b8d5-dd9ece33c97a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars.selectors as cs\n",
    "from great_tables import GT, html, md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ad9abb6-812e-4eda-a23f-19cdf0a6e9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " \n",
       "</style>\n",
       "<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n",
       "<thead>\n",
       "\n",
       "<tr class=\"gt_col_headings\">\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"\"></th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$\\overline{x}$\">$\\overline{x}$</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$\\overline{y}$\">$\\overline{y}$</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$s^2_x$\">$s^2_x$</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$s^2_y$\">$s^2_y$</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$\\rho_{x,y}$\">$\\rho_{x,y}$</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"intercept\">intercept</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"slope\">slope</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"$R^2$\">$R^2$</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody class=\"gt_table_body\">\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">I</th>\n",
       "    <td class=\"gt_row gt_right\">9.00</td>\n",
       "    <td class=\"gt_row gt_right\">7.50</td>\n",
       "    <td class=\"gt_row gt_right\">11.00</td>\n",
       "    <td class=\"gt_row gt_right\">4.13</td>\n",
       "    <td class=\"gt_row gt_right\">0.82</td>\n",
       "    <td class=\"gt_row gt_right\">3.00</td>\n",
       "    <td class=\"gt_row gt_right\">0.50</td>\n",
       "    <td class=\"gt_row gt_right\">0.67</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">II</th>\n",
       "    <td class=\"gt_row gt_right\">9.00</td>\n",
       "    <td class=\"gt_row gt_right\">7.50</td>\n",
       "    <td class=\"gt_row gt_right\">11.00</td>\n",
       "    <td class=\"gt_row gt_right\">4.13</td>\n",
       "    <td class=\"gt_row gt_right\">0.82</td>\n",
       "    <td class=\"gt_row gt_right\">3.00</td>\n",
       "    <td class=\"gt_row gt_right\">0.50</td>\n",
       "    <td class=\"gt_row gt_right\">0.67</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">III</th>\n",
       "    <td class=\"gt_row gt_right\">9.00</td>\n",
       "    <td class=\"gt_row gt_right\">7.50</td>\n",
       "    <td class=\"gt_row gt_right\">11.00</td>\n",
       "    <td class=\"gt_row gt_right\">4.12</td>\n",
       "    <td class=\"gt_row gt_right\">0.82</td>\n",
       "    <td class=\"gt_row gt_right\">3.00</td>\n",
       "    <td class=\"gt_row gt_right\">0.50</td>\n",
       "    <td class=\"gt_row gt_right\">0.67</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">IV</th>\n",
       "    <td class=\"gt_row gt_right\">9.00</td>\n",
       "    <td class=\"gt_row gt_right\">7.50</td>\n",
       "    <td class=\"gt_row gt_right\">11.00</td>\n",
       "    <td class=\"gt_row gt_right\">4.12</td>\n",
       "    <td class=\"gt_row gt_right\">0.82</td>\n",
       "    <td class=\"gt_row gt_right\">3.00</td>\n",
       "    <td class=\"gt_row gt_right\">0.50</td>\n",
       "    <td class=\"gt_row gt_right\">0.67</td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "\n",
       "\n",
       "</table>\n",
       "\n",
       "</div>\n",
       "        "
      ],
      "text/plain": [
       "GT(_tbl_data=shape: (4, 9)\n",
       "┌─────────┬────────┬────────┬────────────┬───┬────────────────┬───────────┬───────┬──────┐\n",
       "│ dataset ┆ mean_x ┆ mean_y ┆ variance_x ┆ … ┆ correlation_xy ┆ intercept ┆ slope ┆ r2   │\n",
       "│ ---     ┆ ---    ┆ ---    ┆ ---        ┆   ┆ ---            ┆ ---       ┆ ---   ┆ ---  │\n",
       "│ str     ┆ f64    ┆ f64    ┆ f64        ┆   ┆ f64            ┆ f64       ┆ f64   ┆ f64  │\n",
       "╞═════════╪════════╪════════╪════════════╪═══╪════════════════╪═══════════╪═══════╪══════╡\n",
       "│ I       ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ II      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ III     ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "│ IV      ┆ 9.00   ┆ 7.50   ┆ 11.00      ┆ … ┆ 0.82           ┆ 3.00      ┆ 0.50  ┆ 0.67 │\n",
       "└─────────┴────────┴────────┴────────────┴───┴────────────────┴───────────┴───────┴──────┘, _body=<great_tables._gt_data.Body object at 0x14f384c20>, _boxhead=Boxhead([ColInfo(var='dataset', type=<ColInfoTypeEnum.stub: 2>, column_label='dataset', column_align='left', column_width=None), ColInfo(var='mean_x', type=<ColInfoTypeEnum.default: 1>, column_label='$\\\\overline{x}$', column_align='right', column_width=None), ColInfo(var='mean_y', type=<ColInfoTypeEnum.default: 1>, column_label='$\\\\overline{y}$', column_align='right', column_width=None), ColInfo(var='variance_x', type=<ColInfoTypeEnum.default: 1>, column_label='$s^2_x$', column_align='right', column_width=None), ColInfo(var='variance_y', type=<ColInfoTypeEnum.default: 1>, column_label='$s^2_y$', column_align='right', column_width=None), ColInfo(var='correlation_xy', type=<ColInfoTypeEnum.default: 1>, column_label=Md(text='$\\\\rho_{x,y}$'), column_align='right', column_width=None), ColInfo(var='intercept', 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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    GT(stats, rowname_col=\"dataset\")\n",
    "    .fmt_number(columns=cs.numeric(), decimals=2)\n",
    "    .opt_table_font(\"Futura\")\n",
    "    .cols_label(\n",
    "        mean_x=r\"$\\overline{x}$\",\n",
    "        mean_y=r\"$\\overline{y}$\",\n",
    "        variance_x=\"$s^2_x$\",\n",
    "        variance_y=\"$s^2_y$\",\n",
    "        correlation_xy=md(r\"$\\rho_{x,y}$\"),\n",
    "        r2=\"$R^2$\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ba960d-fc44-45cd-b75b-1c67ed0bdf23",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* Anscombe, F. J. (1973). \"Graphs in Statistical Analysis\". American Statistician. 27 (1): 17–21. "
   ]
  }
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