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   "source": [
    "# Lecture 5 - Probability, Conditional Probability, Independence, and Prediction\n"
   ]
  },
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    "## Announcements\n",
    "* Quiz 1 graded; you should have gotten an email from Gradescope; about half of you have viewed your quiz.\n",
    "  * Grades will be entered into Canvas about a week after they're released on Gradescope\n",
    "  * Please check for grading errors this week and let me know if you find any.\n",
    "* Data Ethics 1 due Wednesday; discussion in class.\n",
    "\n",
    "* **Tomorrow 11-3pm in KB 122** the CS department is hosting an [**AI Panel**](https://news.wwu.edu/ai-experts-weigh-in-on-how-to-thrive-in-the-ai-age-april-14)\n",
    "\n",
    "  * 10 experts from industry will answer questions about AI in industry in the present and future\n",
    "  * Some impressive [panelists](https://cs.wwu.edu/ai-computer-science-panel) - including senior people from Microsoft, Dell, Snowflake, and a VP of engineering at Meta\n",
    "  * Schedule:\n",
    "    * 11 a.m. - noon: Panel 1: AI in the Workplace - the present\n",
    "    * Noon -1 p.m.: Networking lunch for students and panelists\n",
    "    * 1 - 2 p.m.: Panel 2: AI in the Workplace - the future\n",
    "    * 2- 3 p.m.: Networking open session"
   ]
  },
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   "cell_type": "markdown",
   "id": "rE3V7VB6DOGX",
   "metadata": {
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   "source": [
    "\n",
    "## Goals:\n",
    "\n",
    "* Know how to compute and interpret basic summary statistics (Skiena 2.2):\n",
    "  * Centrality measures: Arithmetic mean, Geometric Mean, Median, (Mode)\n",
    "  * Variability measures: Standard Deviation, Variance\n",
    "\n",
    "* Know how to compute summary statistics in pandas.\n",
    "\n",
    "* Know the definition and implications of independence\n",
    "  * Know the definition of have intuition for conditional probability\n",
    "  * Wrongly assuming independence can lead to poor modeling/predictions\n",
    "  * A lack of independence leads to correlations, which is where modeling/predictive power comes from\n",
    "\n",
    "* Think about what makes a good data science question and practice formulating some of your own. (Skiena 1.2)\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "db12f5d8",
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   "source": [
    "### Probability and Statistics, Continued\n",
    "\n",
    "As a reminder,\n",
    "* Probability: how we model real-world data generating processes\n",
    "* Statistics: how we infer things about those processes (e.g., estimate the parameters of the model)\n",
    "\n",
    "There are many dual concepts:\n",
    "* We saw last time that we can use a **histogram** as an estimate of the **probability density function**."
   ]
  },
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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1, 2, figsize=(6,3))\n",
    "# probability density function of a fair coin toss:\n",
    "axs[0].bar([\"0\", \"1\"], [0.5, 0.5])\n",
    "axs[0].set_xlabel(\"V(s)\")\n",
    "axs[0].set_ylabel(\"P(s)\")\n",
    "\n",
    "# histogram of 10,000 fair coin tosses:\n",
    "import random\n",
    "N = 10000\n",
    "outcomes = []\n",
    "for i in range(N):\n",
    "    outcomes.append(random.choice((\"H\", \"T\")))\n",
    "\n",
    "n_heads = 0\n",
    "n_tails = 0\n",
    "for out in outcomes:\n",
    "    if out == \"H\":\n",
    "        n_heads += 1\n",
    "    if out == \"T\":\n",
    "        n_tails += 1\n",
    "\n",
    "axs[1].bar([\"0\", \"1\"], [n_tails, n_heads])\n",
    "axs[1].set_xlabel(\"Outcome\")\n",
    "axs[1].set_ylabel(\"Frequency\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8adf5740-a243-4015-8cfa-9973d3f6a857",
   "metadata": {},
   "source": [
    "* The **mean** of a dataset gives an estimate of the **expected value** of the random variable being measured.\n",
    "* (and many more) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5d49b7-3e3d-4cbd-9783-0b787330c6a6",
   "metadata": {
    "id": "a21ec033"
   },
   "source": [
    "### Summary Statistics\n",
    "\n",
    "Real-world experiments of interest are more complicated - more complicated processes, more complicated sets of outcomes, etc. It's often useful to summarize the salient properties of an observed distribution. For this, we use **summary statistics**.\n",
    "\n",
    "#### Central Tendency Measures\n",
    "These tell you something about where the data is \"centered\".\n",
    "\n",
    "**(Arithmetic) Mean**, aka \"average\": The sum of the values divided by the number of values: $$\\mu_X = \\frac{1}{n} \\sum_{i=1}^n x_i$$.\n",
    "\n",
    "This works well for data sets where there aren't many outliers; for example: the average height of a female American is 5 feet 4 inches.\n",
    "\n",
    "**Geometric Mean**: The $n$th root of the product of $n$ values: $$\\left(\\prod_{i=1}^n a_i\\right)^\\frac{1}{n}$$\n",
    "\n",
    "This is a weird one, and not as often applicable. If you have a single zero, the geometric mean is zero. But it's useful for measuring the central tendency of a collection of **ratios**.\n",
    "\n",
    "**Median**: The middle value - the element appearing exaclty in the middle if the data were sorted. This is useful in the presence of **outliers** or more generally when the distribution is weirdly-shaped.\n",
    "\n",
    "**\\*-iles**\n",
    "\n",
    "These generalize the median to fractions other than one half. For example, the five quartiles of a dataset are the minimum, the value that is larger than one quarter of the data, the median, the value that is larger than three quarters of the data, and the maximum.\n",
    "\n",
    "Common examples aside from quartiles include percentiles (divide the data into 100ths), deciles (10ths), and quintiles (fifths).\n",
    "\n",
    "#### Variability Measures\n",
    "These tell you something about the *spread* of the data, i.e., how far measurements tend to be from the center.\n",
    "\n",
    "**Standard Deviation** ($\\sigma$): The square root of the sum of squared differences between the elements and the mean: $$\\sqrt{\\frac{\\sum_{i=1}^n (a_i - \\bar{a})^2}{n-1}}$$\n",
    "\n",
    "**Variance**: the square of the Standard Deviation (i.e., same thing without the square root).\n",
    "\n",
    "Variance is easier to intuit: it's the average sqaured distance from the mean, with a small caveat that it's divided by n-1 rather than by n.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3848e818-2d0c-459e-9511-be9a581bc411",
   "metadata": {
    "id": "58BsHyFIW1hV"
   },
   "source": [
    "## Summary Statistics in Pandas\n",
    "\n",
    "There are built-in functions that do all of the above for us. To demo, we'll use a dataset of body measurements from a sample of humans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d616ac13-50f8-45d2-bff5-8bdf3bb1ef25",
   "metadata": {
    "executionInfo": {
     "elapsed": 372,
     "status": "ok",
     "timestamp": 1673462763291,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "yrLC-Fy9XIUM"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd0929d-772f-4471-96a9-9fce89a47a21",
   "metadata": {
    "id": "zbAL8dxhXKnd"
   },
   "source": [
    "Load the data and do a little tidying:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8d08412-8b2e-4ea3-871d-a5bf09b443c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQN</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Leg</th>\n",
       "      <th>Arm</th>\n",
       "      <th>Arm Cir</th>\n",
       "      <th>Waist Cir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93703.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>88.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>48.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93704.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>94.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93705.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>158.3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>101.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93706.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>175.7</td>\n",
       "      <td>46.6</td>\n",
       "      <td>38.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>79.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93707.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>45.4</td>\n",
       "      <td>158.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>21.5</td>\n",
       "      <td>64.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8699</th>\n",
       "      <td>102952.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>156.5</td>\n",
       "      <td>34.4</td>\n",
       "      <td>32.6</td>\n",
       "      <td>25.1</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8700</th>\n",
       "      <td>102953.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>97.4</td>\n",
       "      <td>164.9</td>\n",
       "      <td>38.2</td>\n",
       "      <td>36.6</td>\n",
       "      <td>40.6</td>\n",
       "      <td>114.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8701</th>\n",
       "      <td>102954.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>69.1</td>\n",
       "      <td>162.6</td>\n",
       "      <td>39.2</td>\n",
       "      <td>35.2</td>\n",
       "      <td>26.8</td>\n",
       "      <td>86.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8702</th>\n",
       "      <td>102955.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>156.6</td>\n",
       "      <td>39.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>113.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8703</th>\n",
       "      <td>102956.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>111.5</td>\n",
       "      <td>175.8</td>\n",
       "      <td>42.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8704 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SEQN  Gender   Age  Weight  Height   Leg   Arm  Arm Cir  Waist Cir\n",
       "0      93703.0     2.0   2.0    13.7    88.6   NaN  18.0     16.2       48.2\n",
       "1      93704.0     1.0   2.0    13.9    94.2   NaN  18.6     15.2       50.0\n",
       "2      93705.0     2.0  66.0    79.5   158.3  37.0  36.0     32.0      101.8\n",
       "3      93706.0     1.0  18.0    66.3   175.7  46.6  38.8     27.0       79.3\n",
       "4      93707.0     1.0  13.0    45.4   158.4  38.1  33.8     21.5       64.1\n",
       "...        ...     ...   ...     ...     ...   ...   ...      ...        ...\n",
       "8699  102952.0     2.0  70.0    49.0   156.5  34.4  32.6     25.1       82.2\n",
       "8700  102953.0     1.0  42.0    97.4   164.9  38.2  36.6     40.6      114.8\n",
       "8701  102954.0     2.0  41.0    69.1   162.6  39.2  35.2     26.8       86.4\n",
       "8702  102955.0     2.0  14.0   111.9   156.6  39.2  35.0     44.5      113.5\n",
       "8703  102956.0     1.0  38.0   111.5   175.8  42.5  38.0     40.0      122.0\n",
       "\n",
       "[8704 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = '/cluster/academic/DATA311/202620/NHANES/NHANES.csv'\n",
    "df = pd.read_csv(url).rename(\n",
    "   columns={\"SEQN\": \"SEQN\",\n",
    "            \"RIAGENDR\": \"Gender\", # 1 = M, 2 = F\n",
    "            \"RIDAGEYR\": \"Age\", # years\n",
    "            \"BMXWT\": \"Weight\", # kg\n",
    "            \"BMXHT\": \"Height\", # cm\n",
    "            \"BMXLEG\": \"Leg\", # cm\n",
    "            \"BMXARML\": \"Arm\", # cm\n",
    "            \"BMXARMC\": \"Arm Cir\", # cm\n",
    "            \"BMXWAIST\": \"Waist Cir\"} # cm\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd185787-04ac-45c3-b949-3ec36e4b9bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Height\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b2551ef-9f3b-46bf-845c-f2616f08455b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "29128101-1a9a-4b1f-9cc8-2e0e838fafe1",
   "metadata": {},
   "source": [
    "We can see the names and datatypes of all the columns with `info`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c70522ae-eb88-4b1a-bbfe-bbd2d862f0fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 8704 entries, 0 to 8703\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   SEQN       8704 non-null   float64\n",
      " 1   Gender     8704 non-null   float64\n",
      " 2   Age        8704 non-null   float64\n",
      " 3   Weight     8580 non-null   float64\n",
      " 4   Height     8016 non-null   float64\n",
      " 5   Leg        6703 non-null   float64\n",
      " 6   Arm        8177 non-null   float64\n",
      " 7   Arm Cir    8173 non-null   float64\n",
      " 8   Waist Cir  7601 non-null   float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 612.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb96c46b-536f-468a-a092-c056ee72e90b",
   "metadata": {},
   "source": [
    "To compute a useful collection of summary statistics on each column, we can use `describe`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e006ded4-fc3b-48db-b880-7eeef60c029f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQN</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Leg</th>\n",
       "      <th>Arm</th>\n",
       "      <th>Arm Cir</th>\n",
       "      <th>Waist Cir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8704.000000</td>\n",
       "      <td>8704.000000</td>\n",
       "      <td>8.704000e+03</td>\n",
       "      <td>8580.000000</td>\n",
       "      <td>8016.000000</td>\n",
       "      <td>6703.000000</td>\n",
       "      <td>8177.000000</td>\n",
       "      <td>8173.000000</td>\n",
       "      <td>7601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>98315.452091</td>\n",
       "      <td>1.509076</td>\n",
       "      <td>3.443865e+01</td>\n",
       "      <td>65.138508</td>\n",
       "      <td>156.593401</td>\n",
       "      <td>38.643980</td>\n",
       "      <td>33.667996</td>\n",
       "      <td>29.193589</td>\n",
       "      <td>89.928851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2669.112899</td>\n",
       "      <td>0.499946</td>\n",
       "      <td>2.537904e+01</td>\n",
       "      <td>32.890754</td>\n",
       "      <td>22.257858</td>\n",
       "      <td>4.158013</td>\n",
       "      <td>7.229185</td>\n",
       "      <td>7.970648</td>\n",
       "      <td>22.805093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>93703.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>78.300000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96000.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>151.400000</td>\n",
       "      <td>35.800000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>73.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>98308.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>67.750000</td>\n",
       "      <td>161.900000</td>\n",
       "      <td>38.800000</td>\n",
       "      <td>35.800000</td>\n",
       "      <td>30.100000</td>\n",
       "      <td>91.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100625.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>171.200000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>34.700000</td>\n",
       "      <td>105.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102956.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>242.600000</td>\n",
       "      <td>197.700000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>49.900000</td>\n",
       "      <td>56.300000</td>\n",
       "      <td>169.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SEQN       Gender           Age       Weight       Height  \\\n",
       "count    8704.000000  8704.000000  8.704000e+03  8580.000000  8016.000000   \n",
       "mean    98315.452091     1.509076  3.443865e+01    65.138508   156.593401   \n",
       "std      2669.112899     0.499946  2.537904e+01    32.890754    22.257858   \n",
       "min     93703.000000     1.000000  5.397605e-79     3.200000    78.300000   \n",
       "25%     96000.750000     1.000000  1.100000e+01    43.100000   151.400000   \n",
       "50%     98308.500000     2.000000  3.100000e+01    67.750000   161.900000   \n",
       "75%    100625.250000     2.000000  5.800000e+01    85.600000   171.200000   \n",
       "max    102956.000000     2.000000  8.000000e+01   242.600000   197.700000   \n",
       "\n",
       "               Leg          Arm      Arm Cir    Waist Cir  \n",
       "count  6703.000000  8177.000000  8173.000000  7601.000000  \n",
       "mean     38.643980    33.667996    29.193589    89.928851  \n",
       "std       4.158013     7.229185     7.970648    22.805093  \n",
       "min      24.800000     9.400000    11.200000    40.000000  \n",
       "25%      35.800000    32.000000    23.800000    73.900000  \n",
       "50%      38.800000    35.800000    30.100000    91.200000  \n",
       "75%      41.500000    38.400000    34.700000   105.300000  \n",
       "max      55.000000    49.900000    56.300000   169.500000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73438b9a-f382-4a01-867c-538fd9d4e2c6",
   "metadata": {},
   "source": [
    "Poke around! Some ideas:\n",
    "* `mean`, `median`, `min`, `max` per column\n",
    "* `.plot.hist` per column\n",
    "* Perform some unit conversions\n",
    "* Extract a subset of rows meeting a criterion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228aeb37-f244-40d0-aa8e-63d9521ac34a",
   "metadata": {},
   "source": [
    "**Exercise 1**: Standard deviation is related to the mean, and thus sensitive to outliers. Can you devise a variability measure that would be more robust to outliers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59a794bf-58ba-4384-b1bc-0730a8f4c15c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f79e2da7",
   "metadata": {
    "id": "f79e2da7"
   },
   "source": [
    "### Conditional Probability and Independence\n",
    "\n",
    "Simple probability experiments like rolling two dice produce math that's friendly and easy to work with. The outcome of one die doesn't affect the outcome of the other.\n",
    "\n",
    "Real life is rarely so simple.\n",
    "\n",
    "A **joint probability distribution** on two random variables $P(X, Y)$ is the probability of each possible combination of values of $X$ and $Y$.\n",
    "\n",
    "If $X$ is the number on the first die and $Y$ is the number on the second die, $P(X,Y)$ has a friendly property: $$P(X, Y) = P(X) P(Y)$$\n",
    "\n",
    "Let's see why with our physically-improbable three-sided dice (written notes)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c64df66",
   "metadata": {
    "id": "6c64df66"
   },
   "source": [
    "In convincing ourselves of the above properly, we sneakily started talking about **conditional probability**: a probability of something *given* that you know something else. In our dice example, the probability of die 2 being 1 *given* that die1 was a 1 was $1/3$. We write this in math as: \n",
    "$$P(Y=1 | X = 1) = 1/3$$\n",
    "where the vertical bar $|$ is read as \"given\", or \"conditioned on\".\n",
    "\n",
    "**Independence** is the property we described above: two events are **independent** if the outcome of one event doesn't affect, or change our understanding of the probability of another.\n",
    "\n",
    "Another way to view independence is that the **conditional probability** is equal to the **unconditional** probability. For example,\n",
    "$$P(Y = 1 | X = 1) = 1/3 = P(Y = 1)$$\n",
    "The information that $X = 1$ doesn't add anything to our understanding of the situation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9ba9b2",
   "metadata": {
    "id": "ac9ba9b2"
   },
   "source": [
    "When are events **not** independent? Most of the time.\n",
    "\n",
    "An abstract, probability-theory-type example might be: you flip a fair coin; if that fair coin is heads, you roll a fair 3-sided die, but if it's tails you roll a weighted three-sided die whose odds of coming up 1 are 0.6, while the odds coming up 2 or 3 are 0.2 each."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb027331",
   "metadata": {
    "id": "eb027331"
   },
   "source": [
    "**Exercise 1:** Let $C$ be the outcome of the coin flip and $D$ be the outcome of the die roll. write down the full **joint distribution** $P(C, D)$ for this experiment. I've given you the first one:\n",
    "* $P(C=H, D=1) = 1/6$\n",
    "* $P(C=H, D=2) = \\hspace{1.6em}$\n",
    "* $P(C=H, D=3) = \\hspace{1.6em}$\n",
    "* $P(C=T, D=1) = \\hspace{1.6em}$\n",
    "* $P(C=T, D=2) = \\hspace{1.6em}$\n",
    "* $P(C=T, D=3) = \\hspace{1.6em}$\n",
    "\n",
    "**Exercise 2:** What is $P(D=1 | C=T)$?\n",
    "\n",
    "**Exercise 3:** What is $P(D=1)$?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3452d49f",
   "metadata": {
    "id": "3452d49f"
   },
   "source": [
    "### Less abstractly...\n",
    "\n",
    "A fundamental assumption that data scientists implicitly make is that their data is generated by some process that follows the laws of probability. Let's look at a dataset and think about these concepts in terms of some of the columns therein.\n",
    "\n",
    "This dataset is called NHANES and it consists of a bunch of different body measurements from a population of people. The code below loads the dataset, renames the columns with more sensible labesls, and drops the unique identifier for each person (SEQN), which we don't need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a221c269",
   "metadata": {
    "executionInfo": {
     "elapsed": 424,
     "status": "ok",
     "timestamp": 1674067704615,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "a221c269"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data_url = \"/cluster/academic/DATA311/202620/NHANES/NHANES.csv\"\n",
    "cols_renamed = {\"SEQN\": \"SEQN\",\n",
    "                \"RIAGENDR\": \"Gender\", # 1 = M, 2 = F\n",
    "                \"RIDAGEYR\": \"Age\", # years\n",
    "                \"BMXWT\": \"Weight\", # kg\n",
    "                \"BMXHT\": \"Height\", # cm\n",
    "                \"BMXLEG\": \"Leg\", # cm\n",
    "                \"BMXARML\": \"Arm\", # cm\n",
    "                \"BMXARMC\": \"Arm Cir\", # cm\n",
    "                \"BMXWAIST\": \"Waist Cir\"} # cm\n",
    "\n",
    "df = pd.read_csv(data_url)\n",
    "df = df.rename(cols_renamed, axis='columns')\n",
    "df = df.drop(\"SEQN\", axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d51d91f",
   "metadata": {
    "id": "9d51d91f"
   },
   "source": [
    "Remember that **histograms** are like empircal estimates of probability distributions. If we think of two columns as random variables of the outcomes of an experiment in which \"A human grows to be an adult\", we can similarly about the empirical estimate of the joint probability distribution, whether the columns are independent, and the conditional probability of one column's value given the other.\n",
    "\n",
    "First, let's filter out children:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29929d2b",
   "metadata": {
    "executionInfo": {
     "elapsed": 687,
     "status": "ok",
     "timestamp": 1674067719085,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "29929d2b"
   },
   "outputs": [],
   "source": [
    "df = df[df[\"Age\"] >= 21]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a882294",
   "metadata": {
    "id": "3a882294"
   },
   "source": [
    "Now let's consider the **age** and **height** columns. I'm going to use a nifty visualization from the Seaborn library, which we'll use when we dig deeper into visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f5b7191",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "executionInfo": {
     "elapsed": 2092,
     "status": "ok",
     "timestamp": 1674067730811,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "6f5b7191",
    "outputId": "b202e85b-b292-4c4a-d6b2-3406bcf3e40a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x14611b2ff1a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.jointplot(x=\"Age\", y=\"Height\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3251189",
   "metadata": {
    "id": "a3251189"
   },
   "source": [
    "From the scatter plot, it doesn't appear that these variables have much to do with each other. This matches our intuition - once they reach adulthood, people don't grow or shrink (much) in height as they age. This leads to a hypothesis that these variables are **independent**. We can think about this in terms of conditional probability by seeing if the distribution of heights conditioned on each age is the same as the unconditional distribution.\n",
    "\n",
    "The following plot shows our empirical estimate of $P($Height$)$ alongside an empirical estimate of $P($Height $|$ the person is in their 20s$)$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb14081c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "executionInfo": {
     "elapsed": 548,
     "status": "ok",
     "timestamp": 1674067771584,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "cb14081c",
    "outputId": "03f1d3c1-6b07-4790-b53f-1556ee029373"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Height\"].plot.hist(legend=True, label=\"P(Height)\", density=True)\n",
    "twenties = df[(df[\"Age\"] >= 21) & (df[\"Age\"] < 30)]\n",
    "twenties[\"Height\"].plot.hist(legend=True, label=\"P(height|20s)\", density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0445abcc",
   "metadata": {
    "id": "0445abcc"
   },
   "source": [
    "The `density=True` argument tells the plotting library to divide by the total so instead of counts, we get probability-like values that all sum to one. This allows the $y$ axis scale to be comparable across two histograms of different total values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b0bec5",
   "metadata": {
    "id": "16b0bec5"
   },
   "source": [
    "Let's pick a different pair of columns and do a similar analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cabf414d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "executionInfo": {
     "elapsed": 1236,
     "status": "ok",
     "timestamp": 1674067805049,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "cabf414d",
    "outputId": "65007c49-fd93-41de-a438-9f124823654c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x14611aa08aa0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"Height\", y=\"Leg\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7648bfd2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 194,
     "status": "ok",
     "timestamp": 1674067826578,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "7648bfd2",
    "outputId": "20144b33-a400-4183-f6c4-35042bd7d91a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CjNbT+uPHj5dhw4bJ6tWrXXXS0tKkU6dOcvToUfnggw/k4MGD8u6770rNmjUL8ZMBAAB/FuBwOBy++uUtW7aUsLAwWbBggausadOmEhERIdOnT89Wf+zYsRITEyMHDhxwlWnrz969e2Xbtm3meVRUlGlR+s9//iMlS5bM03mlpKRISEiInDt3TsqXL5+nYwC2KIotEygctAChsOXm+9tnLUDaUrNz507p3LmzW7k+j4uL8/geDTlZ63fp0kV27Nghly9fNs81ILVq1UpeeOEFqVq1qoSGhsqrr74qGRkZBfhpAABAUeKzMUDJyckmlGhIyUyfnzp1yuN7tNxT/fT0dHO86tWry5EjR+SLL76Q/v37y9q1a+Xw4cMmDGmdv/zlLx6Pm5qaah6ZEyQAACi+fD4LLCAgwO259shlLbte/czlV65ckZtvvlkWLlwo4eHh0rdvXzPGKHM3W1ba3aZNZs5H7dq18/mpAACAP/NZAKpcubIEBgZma+1JSkrK1srjVK1aNY/1g4KCpFKlSua5tgI1btzYHDvzuCJ9n3a7eTJu3DjTX+h8HD9+3AufEAAAFKsApLOx8is4ONi00GzYsMGtXJ+3bt3a43t0bE/W+rGxsdKiRQvXgOc2bdrIf//7X9MS5HTo0CETjPR3elKqVCkzWCrzAwAAFF95CkANGzaUDh06yHvvvSeXLl3K8y/X9XwWLVokS5YsMTO7Ro4caabAO9f10ZaZgQMHuupr+bFjx8z7tL6+b/HixTJmzBhXneeee05Onz4tw4cPN8FnzZo1ZhC0jgMCAADIcwDSaed33XWXjB492nRL/elPf5Lt27fn+jh9+vQxCx9OmTJF7rzzTvnyyy/NwOU6deqY13VxxMxrAumChvr6pk2bTP2pU6fKnDlzpHfv3q46On5HW4W+/fZbueOOO8w6QRqGIiMj+S8OAADyvw6Qzqz697//LX//+99l3bp10qhRIxkyZIgMGDBAqlSpIkUV6wABOcc6QLga1gFCsV0HSAcfP/LII/L+++/LzJkz5ccffzTdUbVq1TJdV9qCAwAA4G/yFYB0AcLnn3/eDDCeNWuWCT8agnQdnsTEROnVq5f3zhQAAMCXCyFq2Fm6dKnZZ0v35lq+fLn5WaJECddYnXfeeUduvfVWb50nAACAbwOQLir41FNPyeDBg80gaE9uueUWM0MLAACgWAQg3V7ienTNnUGDBuXl8AAAAP43Bki7v/71r39lK9eyZcuWeeO8AAAA/CsAzZgxw2xlkZXuwaWLDgIAABS7AKSrMetA56x0AcPMCxcCAAAUmwCkLT379u3zuEK0c1NSAACAYhWA+vbta7aY2Lhxo2RkZJiHrv2jW07oawAAAMVuFti0adNMN1jHjh3NatBKd1/X1Z8ZAwQAAIplANIp7qtWrTKbkWq3V5kyZeT22293bWIKAABQ7AKQU+PGjc0DAACg2AcgHfOjO8B//vnnkpSUZLq/MtPxQAByj53VAcCPA5AOdtYA1KNHDwkNDZWAgADvnxkAAIA/BaCVK1fK+++/bzZABQAAsGIavA6CbtiwoffPBgAAwF8D0OjRo+Xtt98Wh8Ph/TMCAADwxy6wrVu3mkUQ161bJ7fddpuULFnS7fXo6GhvnR8AAIB/BKAKFSrII4884v2zAQAA8NcAtHTpUu+fCQAAgD+PAVLp6eny2WefyTvvvCPnz583ZT/99JP89ttv3jw/AAAA/2gB0n3AunbtKgkJCZKamiqdOnWScuXKyWuvvSaXLl2SqKgo758pAACAL1uAdCHEFi1ayJkzZ8w+YE46LkhXhwYAACiWs8C++uorsx5QZroZamJiorfODQAAwH9agHTvL90PLKsTJ06YrjAAAIBiF4B0zM/s2bNdz3UvMB38PGnSJLbHAAAAxbML7K233pIOHTpIs2bNzKDnxx9/XA4fPiyVK1eWFStWeP8sAQAAfB2AatSoIXv27DFhZ9euXaZLbMiQIdK/f3+3QdEAAADFJgApDTpPPfWUeQAAABT7ALR8+fJrvj5w4MC8ng8AAIB/BiBdByizy5cvy8WLF820+BtuuIEABAAAit8sMF0AMfNDZ4AdPHhQ7rvvPgZBAwCA4rsXWFaNGjWSGTNmZGsdAgAAKLYBSAUGBpoNUQEAAIrdGKCYmBi35w6HQ06ePClz586VNm3aeOvcAAAA/CcARUREuD3XlaCrVKkiDzzwgLz55pveOjcAAAD/CUC68CEAAEBR5dUxQAAAAMW2BWjUqFE5rjtr1qy8/AoAAAD/CkC7d+82e4Clp6dLkyZNTNmhQ4fMLLCwsDC3sUEAAADFIgD17NlTypUrJ8uWLZOKFSuaMl0QcfDgwdK2bVsZPXq0t88TAADAt2OAdKbX9OnTXeFH6Z+nTZvGLDAAAFA8A1BKSor8/PPP2cqTkpLk/Pnz3jgvAAAA/wpAjzzyiOnu+uCDD+TEiRPmoX8eMmSIPProo94/SwAAAF+PAYqKipIxY8bIE088YXaCNwcKCjIB6PXXX/fm+QEAAPhHALrhhhtk/vz5Juz8+OOPZiuMhg0bStmyZb1/hgAAAP60EKLu/6WPxo0bm/CjQQgAAKBYBqDTp09Lx44dTfDp3r27CUHq6aefZgo8AAAongFo5MiRUrJkSUlISDDdYU59+vSR9evXe/P8AAAA/GMMUGxsrHz66adSq1Ytt/JGjRrJsWPHvHVuAAAA/tMCdOHCBbeWH6fk5GQpVaqUN84LAADAvwLQ/fffL8uXL3fb8+vKlStmVliHDh28eX4AAAD+0QWmQad9+/ayY8cOSUtLk5deekn2798vv/76q3z11VfeP0sAAABftwA1a9ZM9u3bJ/fcc4906tTJdInpCtC6S3yDBg28eX4AAAC+bwHSlZ87d+4s77zzjkyePNn7ZwQAAOBvLUA6/f377783434AAACs6QIbOHCgLF682PtnAwAA4K+DoHXg86JFi2TDhg3SokWLbHuAzZo1y1vnBwAA4NsAdOTIEalbt67pAgsLCzNlhw4dcqtD1xgAAChWAUhXetZ9vzZu3Oja+mLOnDlStWrVgjo/AAAA344Byrrb+7p168wUeAAAgGI/CPpqgQgAAKDYBSAd35N1jE9+x/zMnz9f6tWrJ6VLl5bw8HDZsmXLNetv3rzZ1NP69evXl6ioqKvWXblypTm/iIiIfJ0jAACweAyQtvg8+eSTrg1PL126JEOHDs02Cyw6OjpHx1u1apWMGDHChKA2bdqYxRW7desmP/zwg9xyyy3Z6sfHx0v37t3lmWeekffee89su/H8889LlSpVpHfv3m51dVf6MWPGSNu2bXPzEQEAgAUCHLnoxxo8eHCO6i1dujRH9Vq2bGlmky1YsMBV1rRpU9NiM3369Gz1x44dKzExMXLgwAFXmQawvXv3yrZt21xlGRkZ0q5dO3O+2qJ09uxZ+eijjySnUlJSJCQkRM6dOyfly5fP8fuA/KobuYaLiGLj6Iwevj4FWCYlF9/fuWoBymmwyelaQjt37pTIyEi3ct1mIy4uzuN7NOTo65l16dLFLMqoW3ToKtVqypQpplVoyJAh1+1SU6mpqeaR+QICAIDiK1+DoPMjOTnZtNRknUKvz0+dOuXxPVruqX56ero5ntJuMQ1E7777bo7PRVubNDE6H7Vr187TZwIAAEWDzwLQ1QZRa4/ctQZWe6rvLD9//rw88cQTJvxUrlw5x+cwbtw401zmfBw/fjzXnwMAABTzrTC8QQNKYGBgttaepKSkqy6sWK1aNY/1g4KCpFKlSrJ//345evSo9OzZ0/X6lStXzE+tc/DgQWnQoEG24+qgbufAbgAAUPz5rAUoODjYTGfX/cQy0+etW7f2+J5WrVplqx8bG2v2I9PxP7feeqt89913smfPHtfj4Ycflg4dOpg/07UFAAB82gKkRo0aJQMGDDABRsPNwoULJSEhwczscnZNJSYmyvLly81zLZ87d655n06F10HROt5nxYoV5nVdGyg0NNTtd1SoUMH8zFoOAADs5dMApHuJnT592sza0j3GNKSsXbtW6tSpY17XMg1ETrpgor4+cuRImTdvntSoUcPsRZZ1DSAAAACvrQNkC9YBgq+wDhCKE9YBgj9/f/t8FhgAAEBhIwABAADrEIAAAIB1CEAAAMA6BCAAAGAdAhAAALAOAQgAAFiHAAQAAKxDAAIAANYhAAEAAOsQgAAAgHUIQAAAwDoEIAAAYB0CEAAAsA4BCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdQhAAADAOgQgAABgHQIQAACwDgEIAABYhwAEAACsQwACAADWIQABAADrEIAAAIB1CEAAAMA6BCAAAGAdAhAAALAOAQgAAFiHAAQAAKxDAAIAANYhAAEAAOsQgAAAgHUIQAAAwDoEIAAAYB0CEAAAsA4BCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdQhAAADAOgQgAABgHQIQAACwTpCvTwAAUDzVjVwjRdHRGT18fQooBLQAAQAA6xCAAACAdQhAAADAOgQgAABgHQIQAACwDgEIAABYhwAEAACsQwACAADWIQABAADrEIAAAIB1CEAAAMA6BCAAAGAdAhAAALAOAQgAAFgnyNcnABSUupFruLgAAP9sAZo/f77Uq1dPSpcuLeHh4bJly5Zr1t+8ebOpp/Xr168vUVFRbq+/++670rZtW6lYsaJ5PPjgg7J9+/YC/hQAAKAo8WkAWrVqlYwYMUImTJggu3fvNsGlW7dukpCQ4LF+fHy8dO/e3dTT+uPHj5dhw4bJ6tWrXXU2bdok/fr1k40bN8q2bdvklltukc6dO0tiYmIhfjIAAODPAhwOh8NXv7xly5YSFhYmCxYscJU1bdpUIiIiZPr06dnqjx07VmJiYuTAgQOusqFDh8revXtN2PEkIyPDtATNnTtXBg4cmKPzSklJkZCQEDl37pyUL18+T58NvkcXGIC8ODqjBxeuiMrN97fPWoDS0tJk586dpnUmM30eFxfn8T0acrLW79Kli+zYsUMuX77s8T0XL140r910001ePHsAAFCU+WwQdHJysmmdqVq1qlu5Pj916pTH92i5p/rp6enmeNWrV8/2nsjISKlZs6YZC3Q1qamp5pE5QQIAgOLL54OgAwIC3J5rj1zWsuvV91SuXnvtNVmxYoVER0ebQdNXo91t2mTmfNSuXTsPnwQAABQVPgtAlStXlsDAwGytPUlJSdlaeZyqVavmsX5QUJBUqlTJrfyNN96QV199VWJjY+WOO+645rmMGzfO9Bc6H8ePH8/z5wIAAP7PZwEoODjYTGffsGGDW7k+b926tcf3tGrVKlt9DTgtWrSQkiVLuspef/11mTp1qqxfv968dj2lSpUyg6UyPwAAQPHl0y6wUaNGyaJFi2TJkiVmZtfIkSPNFHid2eVsmck8c0vLjx07Zt6n9fV9ixcvljFjxrh1e02cONG8VrduXdNipI/ffvvNJ58RAAD4H5+uBN2nTx85ffq0TJkyRU6ePCmhoaGydu1aqVOnjnldyzKvCaQLJurrGpTmzZsnNWrUkDlz5kjv3r3dFlbUGWZ/+MMf3H7XpEmT5JVXXinETwcAAPyVT9cB8lesA1Q8sA4QgLxgHaCiq0isAwQAAOArBCAAAGAdAhAAALAOAQgAAFiHAAQAAKxDAAIAANYhAAEAAOsQgAAAgHUIQAAAwDoEIAAAYB0CEAAAsA4BCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdQhAAADAOgQgAABgHQIQAACwDgEIAABYhwAEAACsQwACAADWIQABAADrEIAAAIB1CEAAAMA6BCAAAGAdAhAAALAOAQgAAFiHAAQAAKxDAAIAANYhAAEAAOsQgAAAgHUIQAAAwDoEIAAAYB0CEAAAsA4BCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdQhAAADAOgQgAABgHQIQAACwDgEIAABYhwAEAACsE+TrEwAAwJ/UjVwjRc3RGT18fQpFDi1AAADAOgQgAABgHQIQAACwDgEIAABYhwAEAACsQwACAADWIQABAADrEIAAAIB1CEAAAMA6BCAAAGAdtsJAsV0aHgCAq6EFCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdZgFBgBAEVcUZ+oendHD7hag+fPnS7169aR06dISHh4uW7ZsuWb9zZs3m3pav379+hIVFZWtzurVq6VZs2ZSqlQp8/PDDz8swE8AAACKGp8GoFWrVsmIESNkwoQJsnv3bmnbtq1069ZNEhISPNaPj4+X7t27m3paf/z48TJs2DATeJy2bdsmffr0kQEDBsjevXvNz8cee0y++eabQvxkAADAnwU4HA6Hr355y5YtJSwsTBYsWOAqa9q0qURERMj06dOz1R87dqzExMTIgQMHXGVDhw41QUeDj9Lwk5KSIuvWrXPV6dq1q1SsWFFWrFiRo/PS94eEhMi5c+ekfPny4m1FsakSAAB/7wLLzfe3z8YApaWlyc6dOyUyMtKtvHPnzhIXF+fxPRpy9PXMunTpIosXL5bLly9LyZIlTZ2RI0dmqzN79uyrnktqaqp5OOmFc17IgnAl9WKBHBcAgKIipQC+Y53HzEnbjs8CUHJysmRkZEjVqlXdyvX5qVOnPL5Hyz3VT09PN8erXr36Vetc7ZhKW5smT56crbx27dq5/FQAACAnQq7eLpFv58+fNy1Bfj0LLCAgwO25prasZdern7U8t8ccN26cjBo1yvX8ypUr8uuvv0qlSpWu+b68plMNVsePHy+Q7jVbcB25jv6E+5Hr6E9svh8dDocJPzVq1LhuXZ8FoMqVK0tgYGC2lpmkpKRsLThO1apV81g/KCjIhJVr1bnaMZXOFtNHZhUqVJCCpDelbTdmQeA6ch39Cfcj19Gf2Ho/hlyn5cfns8CCg4PNdPYNGza4levz1q1be3xPq1atstWPjY2VFi1amPE/16pztWMCAAD7+LQLTLuddJq6BhgNLgsXLjRT4HVml7NrKjExUZYvX26ea/ncuXPN+5555hkz4FkHQGee3TV8+HC5//77ZebMmdKrVy/5+OOP5bPPPpOtW7f67HMCAAD/4tMApFPWT58+LVOmTJGTJ09KaGiorF27VurUqWNe17LMawLpgon6us7ymjdvnunjmzNnjvTu3dtVR1t6Vq5cKRMnTpSXX35ZGjRoYNYb0in3/kC72iZNmpStyw1cR1/gfuQ6+hPuR66jNesAAQAAWLkVBgAAQGEjAAEAAOsQgAAAgHUIQAAAwDoEoAKgW2vcfffdUq5cObn55pvN5q4HDx50q/Pkk0+aVaYzP+69996COJ0iSzfJveOOO1yLeelSCZk3udXx+6+88oqZDVimTBlp37697N+/36fnXBSvI/di3v8/1/9vR4wY4SrjnvTOdeSevD79uy/rd4guBMy9mHMEoAKwefNmeeGFF+Trr782izLqXmW6ieuFCxfc6uku9TrV3/nQKf74P7Vq1ZIZM2bIjh07zOOBBx4wazs5Q85rr70ms2bNMmtDffvtt+Z//k6dOpll0JHz68i9mHt6v+m6ZRosM+Oe9M515J7Mmdtuu83tO+S7777jXswNnQaPgpWUlKRLDTg2b97sKhs0aJCjV69eXPpcqlixomPRokWOK1euOKpVq+aYMWOG67VLly45QkJCHFFRUVzXHF5H7sXcO3/+vKNRo0aODRs2ONq1a+cYPny4Keee9M515J7MmUmTJjmaN2/u8TXuxZyhBagQnDt3zvy86aab3Mo3bdpkusgaN25sVrbWPcvgWUZGhlngUlvRtAsnPj7e7PmmLWuZF1Fr166dxMXFcRlzeB25F3NPW3d79OghDz74oFs596R3riP3ZM4dPnzYDAHQRYL79u0rR44c4V7MBZ/vBl/c6ZgA3brjvvvuMytdO3Xr1k3++Mc/mlWv9S9OXbVauyZ27tzJKtGZaJOuflFfunRJbrzxRvnwww+lWbNmrpCTdZNbfX7s2LHC+s9b5K+j4l7MOQ2Pu3btMl03WTk3YeaezN915J7MGd3dQLeJ0n9A//zzzzJt2jSzE4J2bXMv5gwBqIC9+OKLsm/fvmx7kek2IE4ajHQ/NA1Da9askUcffbSgT6vIaNKkiezZs0fOnj0rq1evlkGDBpkxVk468C9r4MxahqtfRw1B3Is5c/z4cbPXoG6uXLp06avW457M/3Xknrw+/YeL0+23327+gaNbPy1btsw1oYZ78droAitAf/7znyUmJkY2btxoBqJeS/Xq1U0A0iZN/J/g4GBp2LChCYg6W6R58+by9ttvu2Y7OP+l46TdiFn/BY6rX0fuxZzT1lm9v8LDwyUoKMg8NETqfoT6Z+d9xz2Zv+uo3bT8/Zh7ZcuWNUFIv0P4+zFnCEAFQFshtOUnOjpavvjiC9M/ez26Kaz+y0iDEK59bVNTU8011f/JdZadU1pamvmLVJuBkbPryL2Ycx07djRdidqS5nxooOzfv7/5c/369bknvXAdAwMDs72Hvx+vT/9/PnDggPkO4e/HHMrhYGnkwnPPPWdmI23atMlx8uRJ1+PixYuu2Q+jR492xMXFOeLj4x0bN250tGrVylGzZk1HSkoK1/r/GzdunOPLL78012jfvn2O8ePHO0qUKOGIjY01r+sMML3O0dHRju+++87Rr18/R/Xq1bmGubiO3Iv5k3X2Evdk/q8j92TO6HeIfsccOXLE8fXXXzseeughR7ly5RxHjx7lXswhxgAV0MJzShfmy2zp0qVmgS/9F47+C0gHsOmYDE3sHTp0kFWrVpnFE/H/6MC+AQMGmPUtQkJCzFoh69evN2v9qJdeekl+//13ef755+XMmTNmUKCOK+Aa5vw66vXjXvQe7sn84+/HnDlx4oT069dPkpOTpUqVKmbcj649p0MpuBdzJkBTUA7rAgAAFAuMAQIAANYhAAEAAOsQgAAAgHUIQAAAwDoEIAAAYB0CEAAAsA4BCAAAWIcABAAArEMAAgAA1iEAAQAA6xCAAACAdQhAAABAbPO/hy50GoWmmM8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Leg\"].plot.hist(legend=True, label=\"P(Leg)\", density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14707789",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 530,
     "status": "ok",
     "timestamp": 1674067830043,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "14707789",
    "outputId": "4b54b6c3-afa4-429f-81e4-38cdd5c55c41"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for height in [140, 160, 180]:\n",
    "    mask = (df[\"Height\"] >= height) & (df[\"Height\"] < height+20)\n",
    "    label = f\"P(Leg | {height} <= Ht < {height+20})\"\n",
    "    df[mask][\"Leg\"].plot.hist(legend=True, label=label,  density=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8cc161",
   "metadata": {
    "id": "4f8cc161"
   },
   "source": [
    "These columns are decidedly **not** independent! There is a strong **correlation** between them (we'll come back to that word and use it more formally later on). In other words, $P(Leg | Height) \\ne P(Leg)$. That means that if we know Height, we have a better idea of what to expect from Leg. This forms the basis of our ability to make **predictions** from data!\n",
    "\n",
    "The key insight I want you to take away from this is that the presence of correlations - aka a lack of independence - in the data yield **predictive power**. Two implications:\n",
    "\n",
    "* If there are no correlations, we're unlikely to be able to make predictions.\n",
    "* The correlation between leg length and height is clearly due to a real, underlying relationship between height and leg length. Not all correlations are, though! It's important to keep in mind that when we find a correlation, we have only failed to rule out the possibility a causal relationship between the two variables; but we have not confirmed its existence. In other words: correlation does not imply causation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a8ebfc-ffc2-4022-90be-39c475012741",
   "metadata": {
    "id": "83acc936"
   },
   "source": [
    "## Asking interesting data questions\n",
    "\n",
    "* Consider a couple example datasets:\n",
    " * IMDB: All things movies. <https://developer.imdb.com/non-commercial-datasets/>\n",
    "   * Films: title, duration, genre tags, date, cast, crew, user ratings, critic ratings, ...\n",
    "   * People (actors, directors, writers, producers, crew): appearances/credits, birth/death dates, height, awards, ...\n",
    " * Boston Bike Share data: https://www.bluebikes.com/system-data\n",
    "   * Trips: Trip Duration, Start Time and Date, Stop Time and Date, Start Station Name & ID, End Station Name & ID, Bike ID, User Type (Casual = Single Trip or Day Pass user; Member = Annual or Monthly Member), Birth Year, Gender (self-reported by member)\n",
    "   * Stations: ID, Name, GPS coordinates, # docks\n",
    "\n",
    "* **Exercise, as a table:** Come up with at least one interesting question you might want to answer with each of these datasets.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc08d83-a2b0-4659-871b-fb28145d40d0",
   "metadata": {
    "id": "Cp_HMTKT1S_W"
   },
   "source": [
    "### Ideas from the class:\n",
    "IMDB\n",
    "\n",
    "\n",
    "Bikes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f943691f-c1df-4113-982f-355eef18a7d8",
   "metadata": {
    "id": "3b8abda8"
   },
   "source": [
    "**Insight**: Datasets can often answer questions that the data isn't directly about.\n",
    " * Example: baseball stats (http://www.baseball-reference.com) has details about ~20,000 major league baseball players over the last 150 years. This data includes handedness and birth/death dates, so we can study a possible link between handedness and lifespan."
   ]
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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