{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb917ecc",
   "metadata": {},
   "source": [
    "# The Bootstrap\n",
    "A data scientist uses a **random sample** of data to estimate an **unknown parameter**. She uses the sample to calculate the value of a statistic that she will use as her estimate of the population value. \n",
    "\n",
    "Once she has calculated the observed value of her statistic, she could simply present it as her estimate and proceed. But she's a data scientist. She knows that her random sample is just **one of numerous** possible random samples, and thus her estimate is just one of numerous plausible estimates. \n",
    "\n",
    "By how much could those estimates vary? To answer this, it appears that she needs to draw **another sample** from the population and compute a new estimate based on the new sample. However, she doesn't have the resources to return to the population and draw another sample.\n",
    "\n",
    "It appears that the data scientist is at an impasse.\n",
    "\n",
    "Fortunately, a brilliant idea called the **bootstrap** can help her out. Since it is not feasible to generate new samples from the population, the bootstrap generates new random samples using a method called **resampling**: the new samples are drawn at random from the original sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ae269c",
   "metadata": {},
   "source": [
    "In this section, we will see how and why the bootstrap works. In the rest of the chapter, we will use the bootstrap for inference.\n",
    "\n",
    "A bootstrap estimate of sampling variability uses the original sample as a stand-in for an unknown population. It does not collect new independent observations. Keep these three distributions separate:\n",
    "\n",
    "| Distribution | What varies? | What it describes |\n",
    "|---|---|---|\n",
    "| Individual observations | The value for each employee. | Variation in employee compensation. |\n",
    "| Sampling distribution | A statistic from each new population sample. | How an estimate varies across repeated samples. |\n",
    "| Bootstrap distribution | A statistic from each resample of one fixed sample. | An approximation to that sampling variability. |\n",
    "\n",
    "The original sample size is `n`; the number of bootstrap resamples is `B`. Increasing `n` can improve precision. Increasing `B` stabilizes the numerical approximation and does not add population information. Run the demonstration cells in order; exercises include their own setup.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84eceec6",
   "metadata": {
    "execution": {
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    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
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   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6327ce4f",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908b4dac",
   "metadata": {},
   "source": [
    "## Employee Compensation in the City of San Francisco\n",
    "[SF OpenData](https://data.sfgov.org) is a website where the City and County of San Francisco make some of their data publicly available. One of the data sets contains compensation data for employees of the City. These include medical professionals at City-run hospitals, police officers, fire fighters, transportation workers, elected officials, and all other employees of the City. \n",
    "\n",
    "Compensation data for the calendar year 2019 are in the table `sf2019`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c07030b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:25.988803Z",
     "iopub.status.busy": "2026-08-31T06:01:25.988693Z",
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     "shell.execute_reply": "2026-08-31T06:01:26.012080Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sf2019_all = pd.read_csv(path_data + 'san_francisco_2019.csv')\n",
    "sf2019 = sf2019_all.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20f59b95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.013599Z",
     "iopub.status.busy": "2026-08-31T06:01:26.013529Z",
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     "shell.execute_reply": "2026-08-31T06:01:26.018508Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "                     Organization Group               Department  \\\n0                     Public Protection          Adult Probation   \n1                     Public Protection          Adult Probation   \n2                     Public Protection          Adult Probation   \n3                     Public Protection          Adult Probation   \n4                     Public Protection          Adult Probation   \n...                                 ...                      ...   \n44520  General Administration & Finance  Treasurer/Tax Collector   \n44521  General Administration & Finance  Treasurer/Tax Collector   \n44522  General Administration & Finance  Treasurer/Tax Collector   \n44523  General Administration & Finance  Treasurer/Tax Collector   \n44524  General Administration & Finance  Treasurer/Tax Collector   \n\n                Job Family                             Job    Salary  \\\n0      Information Systems              IS Trainer-Journey   91332.0   \n1      Information Systems           IS Engineer-Assistant  123241.0   \n2      Information Systems      IS Business Analyst-Senior  115715.0   \n3      Information Systems   IS Business Analyst-Principal  159394.0   \n4      Information Systems           IS Programmer Analyst   70035.0   \n...                    ...                             ...       ...   \n44520              Revenue     Investigator, Tax Collector   44961.0   \n44521              Revenue     Investigator, Tax Collector   85706.0   \n44522              Revenue     Investigator, Tax Collector  101403.0   \n44523              Revenue  Pr Investigator, Tax Collector  113738.0   \n44524              Revenue  Pr Investigator, Tax Collector  113237.0   \n\n       Overtime  Benefits  Total Compensation  \n0           0.0   40059.0            131391.0  \n1           0.0   49279.0            172520.0  \n2           0.0   46752.0            162468.0  \n3           0.0   57312.0            216706.0  \n4           0.0   28671.0             98706.0  \n...         ...       ...                 ...  \n44520      42.0   19546.0             64549.0  \n44521     107.0   38924.0            124737.0  \n44522       0.0   43185.0            144587.0  \n44523     397.0   47104.0            161240.0  \n44524       0.0   49749.0            162986.0  \n\n[44525 rows x 8 columns]",
      "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>Organization Group</th>\n      <th>Department</th>\n      <th>Job Family</th>\n      <th>Job</th>\n      <th>Salary</th>\n      <th>Overtime</th>\n      <th>Benefits</th>\n      <th>Total Compensation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Public Protection</td>\n      <td>Adult Probation</td>\n      <td>Information Systems</td>\n      <td>IS Trainer-Journey</td>\n      <td>91332.0</td>\n      <td>0.0</td>\n      <td>40059.0</td>\n      <td>131391.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Public Protection</td>\n      <td>Adult Probation</td>\n      <td>Information Systems</td>\n      <td>IS Engineer-Assistant</td>\n      <td>123241.0</td>\n      <td>0.0</td>\n      <td>49279.0</td>\n      <td>172520.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Public Protection</td>\n      <td>Adult Probation</td>\n      <td>Information Systems</td>\n      <td>IS Business Analyst-Senior</td>\n      <td>115715.0</td>\n      <td>0.0</td>\n      <td>46752.0</td>\n      <td>162468.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Public Protection</td>\n      <td>Adult Probation</td>\n      <td>Information Systems</td>\n      <td>IS Business Analyst-Principal</td>\n      <td>159394.0</td>\n      <td>0.0</td>\n      <td>57312.0</td>\n      <td>216706.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Public Protection</td>\n      <td>Adult Probation</td>\n      <td>Information Systems</td>\n      <td>IS Programmer Analyst</td>\n      <td>70035.0</td>\n      <td>0.0</td>\n      <td>28671.0</td>\n      <td>98706.0</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    </tr>\n    <tr>\n      <th>44520</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Treasurer/Tax Collector</td>\n      <td>Revenue</td>\n      <td>Investigator, Tax Collector</td>\n      <td>44961.0</td>\n      <td>42.0</td>\n      <td>19546.0</td>\n      <td>64549.0</td>\n    </tr>\n    <tr>\n      <th>44521</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Treasurer/Tax Collector</td>\n      <td>Revenue</td>\n      <td>Investigator, Tax Collector</td>\n      <td>85706.0</td>\n      <td>107.0</td>\n      <td>38924.0</td>\n      <td>124737.0</td>\n    </tr>\n    <tr>\n      <th>44522</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Treasurer/Tax Collector</td>\n      <td>Revenue</td>\n      <td>Investigator, Tax Collector</td>\n      <td>101403.0</td>\n      <td>0.0</td>\n      <td>43185.0</td>\n      <td>144587.0</td>\n    </tr>\n    <tr>\n      <th>44523</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Treasurer/Tax Collector</td>\n      <td>Revenue</td>\n      <td>Pr Investigator, Tax Collector</td>\n      <td>113738.0</td>\n      <td>397.0</td>\n      <td>47104.0</td>\n      <td>161240.0</td>\n    </tr>\n    <tr>\n      <th>44524</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Treasurer/Tax Collector</td>\n      <td>Revenue</td>\n      <td>Pr Investigator, Tax Collector</td>\n      <td>113237.0</td>\n      <td>0.0</td>\n      <td>49749.0</td>\n      <td>162986.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>44525 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 3
    }
   ],
   "source": [
    "sf2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487d36a6",
   "metadata": {},
   "source": [
    "There is one row for each of over 44,500 employees. There are numerous columns containing information about City departmental affiliation and details of the different parts of the employee's compensation package. Here is the row corresponding to London Breed, the Mayor of San Francisco in 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a68f4621",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.019693Z",
     "iopub.status.busy": "2026-08-31T06:01:26.019638Z",
     "iopub.status.idle": "2026-08-31T06:01:26.023302Z",
     "shell.execute_reply": "2026-08-31T06:01:26.023164Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "                     Organization Group Department  \\\n43382  General Administration & Finance      Mayor   \n\n                          Job Family    Job    Salary  Overtime  Benefits  \\\n43382  Administrative & Mgmt (Unrep)  Mayor  342974.0       0.0   98012.0   \n\n       Total Compensation  \n43382            440987.0  ",
      "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>Organization Group</th>\n      <th>Department</th>\n      <th>Job Family</th>\n      <th>Job</th>\n      <th>Salary</th>\n      <th>Overtime</th>\n      <th>Benefits</th>\n      <th>Total Compensation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>43382</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Mayor</td>\n      <td>Administrative &amp; Mgmt (Unrep)</td>\n      <td>Mayor</td>\n      <td>342974.0</td>\n      <td>0.0</td>\n      <td>98012.0</td>\n      <td>440987.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4
    }
   ],
   "source": [
    "sf2019[sf2019['Job'] == 'Mayor']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f07b0c",
   "metadata": {},
   "source": [
    "We are going to study the final column, `Total Compensation`. That's the employee's salary plus the City's contribution towards their retirement and benefit plans.\n",
    "\n",
    "Financial packages in a calendar year can sometimes be hard to understand as they depend on the date of hire, whether the employee is changing jobs within the City, and so on. For example, the lowest values in the `Total Compensation` column look a little strange."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245eb576",
   "metadata": {},
   "source": [
    ":::{note} `sort_values()` Key Parameters\n",
    "\n",
    "`sort_values()` is a pandas DataFrame/Series method that sorts by one or more columns. `sort_values()` sorts a DataFrame by one or more columns. The `by` parameter specifies which column to sort on, and `ascending=False` reverses the order (largest first). Combined with `.head()`, it lets you quickly inspect the \n",
    "extreme values in a dataset.\n",
    "\n",
    "```python\n",
    "# Ascending (smallest first) — the default\n",
    "df.sort_values(by='column')\n",
    "\n",
    "# Descending (largest first)\n",
    "df.sort_values(by='column', ascending=False)\n",
    "```\n",
    "\n",
    "| Parameter | Default | Meaning |\n",
    "|-----------|---------|---------|\n",
    "| `by` | required | column name or list of columns |\n",
    "| `ascending` | `True` | `False` to sort descending |\n",
    "| `inplace` | `False` | modify df directly if `True` |\n",
    "| `na_position` | `'last'` | put NaNs first or last |\n",
    "\n",
    ":::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39367613",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.024229Z",
     "iopub.status.busy": "2026-08-31T06:01:26.024175Z",
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     "shell.execute_reply": "2026-08-31T06:01:26.030320Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "                            Organization Group  \\\n18526  Public Works, Transportation & Commerce   \n43234         General Administration & Finance   \n22329  Public Works, Transportation & Commerce   \n32865                         Community Health   \n38732                     Culture & Recreation   \n\n                           Department                     Job Family  \\\n18526   Municipal Transportation Agcy  Clerical, Secretarial & Steno   \n43234                 Human Resources            Public Service Aide   \n22329     Public Utilities Commission  Clerical, Secretarial & Steno   \n32865                   Public Health            Public Service Aide   \n38732  Recreation And Park Commission                     Recreation   \n\n                           Job  Salary  Overtime  Benefits  Total Compensation  \n18526      Senior Clerk Typist     0.0       0.0       0.0                 0.0  \n43234   Public Service Trainee     0.0       0.0       0.0                 0.0  \n22329             Senior Clerk     0.0       0.0       0.0                 0.0  \n32865  PS Aide Health Services     0.0       0.0       0.0                 0.0  \n38732           Pool Lifeguard     0.0       0.0       0.0                 0.0  ",
      "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>Organization Group</th>\n      <th>Department</th>\n      <th>Job Family</th>\n      <th>Job</th>\n      <th>Salary</th>\n      <th>Overtime</th>\n      <th>Benefits</th>\n      <th>Total Compensation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18526</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Municipal Transportation Agcy</td>\n      <td>Clerical, Secretarial &amp; Steno</td>\n      <td>Senior Clerk Typist</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>43234</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Human Resources</td>\n      <td>Public Service Aide</td>\n      <td>Public Service Trainee</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>22329</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Public Utilities Commission</td>\n      <td>Clerical, Secretarial &amp; Steno</td>\n      <td>Senior Clerk</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>32865</th>\n      <td>Community Health</td>\n      <td>Public Health</td>\n      <td>Public Service Aide</td>\n      <td>PS Aide Health Services</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>38732</th>\n      <td>Culture &amp; Recreation</td>\n      <td>Recreation And Park Commission</td>\n      <td>Recreation</td>\n      <td>Pool Lifeguard</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5
    }
   ],
   "source": [
    "sf2019.sort_values(by='Total Compensation').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33df4710",
   "metadata": {},
   "source": [
    "To define a more comparable analysis population, keep records with annual salary over $15,000. Salary is a screening variable, not a direct measure of hours or full-year employment. This filter changes the population to which the estimate refers; it excludes some city employees.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1a04472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.031348Z",
     "iopub.status.busy": "2026-08-31T06:01:26.031294Z",
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     "shell.execute_reply": "2026-08-31T06:01:26.033481Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sf2019 = sf2019[sf2019['Salary'] > 15000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "670704d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.034566Z",
     "iopub.status.busy": "2026-08-31T06:01:26.034515Z",
     "iopub.status.idle": "2026-08-31T06:01:26.035940Z",
     "shell.execute_reply": "2026-08-31T06:01:26.035791Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "37103"
     },
     "execution_count": 7
    }
   ],
   "source": [
    "len(sf2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8429ff",
   "metadata": {},
   "source": [
    "## Population and Parameter\n",
    "\n",
    "Our analysis population is the **37,103 records with salary over $15,000**, not all 44,525 employee records in the file. The parameter is the median total compensation in that filtered group. The histogram below describes the individual compensation values in that group.\n",
    "\n",
    "```{index} Population\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bd5558e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.036820Z",
     "iopub.status.busy": "2026-08-31T06:01:26.036777Z",
     "iopub.status.idle": "2026-08-31T06:01:26.090213Z",
     "shell.execute_reply": "2026-08-31T06:01:26.090001Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Filtered Population Compensation')"
     },
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x400 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "sf_bins = np.arange(0, 775000, 25000)\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.hist(sf2019['Total Compensation'], bins=sf_bins, density=True, edgecolor='white')\n",
    "ax.set_xlabel('Annual Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.set_title('Filtered Population Compensation')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e078dd",
   "metadata": {},
   "source": [
    "Most compensation values are below $300,000, but some are higher. The largest is $724,031, so the bins extend beyond $700,000 and include the full recorded range.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7abfbae0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.091352Z",
     "iopub.status.busy": "2026-08-31T06:01:26.091282Z",
     "iopub.status.idle": "2026-08-31T06:01:26.097731Z",
     "shell.execute_reply": "2026-08-31T06:01:26.097522Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "                     Organization Group           Department  \\\n43936  General Administration & Finance  Retirement Services   \n43937  General Administration & Finance  Retirement Services   \n43938  General Administration & Finance  Retirement Services   \n\n                          Job Family                       Job    Salary  \\\n43936  Administrative & Mgmt (Unrep)  Chief Investment Officer  577633.0   \n43937                     Unassigned         Managing Director  483072.0   \n43938                     Unassigned         Managing Director  482649.0   \n\n       Overtime  Benefits  Total Compensation  \n43936       0.0  146398.0            724031.0  \n43937       0.0  134879.0            617951.0  \n43938       0.0  134905.0            617554.0  ",
      "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>Organization Group</th>\n      <th>Department</th>\n      <th>Job Family</th>\n      <th>Job</th>\n      <th>Salary</th>\n      <th>Overtime</th>\n      <th>Benefits</th>\n      <th>Total Compensation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>43936</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Retirement Services</td>\n      <td>Administrative &amp; Mgmt (Unrep)</td>\n      <td>Chief Investment Officer</td>\n      <td>577633.0</td>\n      <td>0.0</td>\n      <td>146398.0</td>\n      <td>724031.0</td>\n    </tr>\n    <tr>\n      <th>43937</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Retirement Services</td>\n      <td>Unassigned</td>\n      <td>Managing Director</td>\n      <td>483072.0</td>\n      <td>0.0</td>\n      <td>134879.0</td>\n      <td>617951.0</td>\n    </tr>\n    <tr>\n      <th>43938</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Retirement Services</td>\n      <td>Unassigned</td>\n      <td>Managing Director</td>\n      <td>482649.0</td>\n      <td>0.0</td>\n      <td>134905.0</td>\n      <td>617554.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9
    }
   ],
   "source": [
    "sf2019.sort_values(by='Total Compensation', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a619b035",
   "metadata": {},
   "source": [
    "Suppose the parameter in which we are interested is the **median** of the total compensations.\n",
    "\n",
    "Since we have the luxury of having all of the data from the population, we can simply calculate the parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc9c3a65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.098856Z",
     "iopub.status.busy": "2026-08-31T06:01:26.098787Z",
     "iopub.status.idle": "2026-08-31T06:01:26.101077Z",
     "shell.execute_reply": "2026-08-31T06:01:26.100889Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "135747.0"
     },
     "execution_count": 10
    }
   ],
   "source": [
    "pop_median = np.median(sf2019['Total Compensation']).item()\n",
    "pop_median\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ee214e",
   "metadata": {},
   "source": [
    "The median total compensation in the **filtered population** is $135,747. The median across all 44,525 records is $121,630. These are different parameters because they describe different groups.\n",
    "\n",
    "We know the filtered population here so we can evaluate the estimation procedure. For the exercise in inference, pretend we cannot inspect it after drawing the sample. Neither the population median nor the other population records enter the bootstrap calculation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a36f631",
   "metadata": {},
   "source": [
    "## A Random Sample and an Estimate\n",
    "Let us draw a **sample of 500** employees at random **without replacement**, and let the median total compensation of the sampled employees serve as our estimate of the parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2f6591c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.102135Z",
     "iopub.status.busy": "2026-08-31T06:01:26.102070Z",
     "iopub.status.idle": "2026-08-31T06:01:26.103882Z",
     "shell.execute_reply": "2026-08-31T06:01:26.103707Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "our_sample = sf2019.sample(500, replace=False, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "175fcb60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.104913Z",
     "iopub.status.busy": "2026-08-31T06:01:26.104857Z",
     "iopub.status.idle": "2026-08-31T06:01:26.141017Z",
     "shell.execute_reply": "2026-08-31T06:01:26.140800Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Compensation in the Original Sample')"
     },
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x400 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "sf_bins = np.arange(0, 775000, 25000)\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.hist(our_sample['Total Compensation'], bins=sf_bins, density=True, edgecolor='white')\n",
    "ax.set_xlabel('Annual Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.set_title('Compensation in the Original Sample')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "752e8e17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.142073Z",
     "iopub.status.busy": "2026-08-31T06:01:26.142005Z",
     "iopub.status.idle": "2026-08-31T06:01:26.143907Z",
     "shell.execute_reply": "2026-08-31T06:01:26.143730Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "139515.5"
     },
     "execution_count": 13
    }
   ],
   "source": [
    "est_median = np.median(our_sample['Total Compensation']).item()\n",
    "est_median\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd1a374",
   "metadata": {},
   "source": [
    "Because the sample was drawn randomly and consists of 500 observations, its distribution tends to resemble that of the population — a consequence of the **law of large numbers**. The sample median is therefore a reasonable estimate of the population median, though not identical to it.\n",
    "\n",
    "So now we have one estimate of the parameter. But had the sample come out differently, the estimate would have had a different value. We would like to be able to quantify the amount by which the estimate could vary across samples. That measure of variability will help us measure how accurately we can estimate the parameter.\n",
    "\n",
    "To see how different the estimate would be if the sample had come out differently, we could just draw another sample from the population. But that would be cheating. We are trying to mimic real life, in which we won't have all the population data at hand.\n",
    "\n",
    "Somehow, we have to get another random sample *without sampling again from the population*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d161c9c",
   "metadata": {},
   "source": [
    "## The Bootstrap: Resampling from the Sample\n",
    "\n",
    "What we do have is a large random sample from the population. As we know, a large random sample is likely to resemble the population from which it is drawn. This observation allows data scientists to *lift themselves up by their own bootstraps*: the sampling procedure can be replicated by *sampling from the sample*. \n",
    "\n",
    "Here are the steps of *the bootstrap method* for generating another random sample that resembles the population:\n",
    "\n",
    "- **Treat the original sample as if it were the population.**\n",
    "- **Draw from the sample, at random with replacement, the same number of times as the original sample size**. \n",
    "\n",
    "It is important to resample the same number of times as the original sample size. The reason is that the variability of an estimate depends on the size of the sample. Since our original sample consisted of 500 employees, our sample median was based on 500 values. To see how different the sample could have been, we have to compare it to the median of other samples of size 500.\n",
    "\n",
    "If we drew 500 times at random *without* replacement from our sample of size 500, we would just get the same sample back. By drawing *with* replacement, we create the possibility for the new samples to be different from the original, because some employees might be drawn more than once and others not at all.\n",
    "\n",
    "The same-size rule preserves the sample size associated with the statistic's variability. The **independent unit** here is one employee record. Ordinary row resampling assumes approximately independent observations from the population of interest; repeated measurements or clustered observations may need a different resampling unit.\n",
    "\n",
    "```{index} Bootstrap\n",
    "```\n",
    "\n",
    "```{index} Resample\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1e285f",
   "metadata": {},
   "source": [
    "## Why the Bootstrap Works\n",
    "\n",
    "Why is this a good idea? By the law of averages, the distribution of the original sample is likely to resemble the population, and the distributions of all the \"resamples\" are likely to resemble the original sample. So the distributions of all the resamples are likely to resemble the population as well.\n",
    "\n",
    "Resampling makes the empirical sample distribution play the role of the unknown population distribution. For statistics such as a median in a sufficiently large, representative sample, this can approximate repeated-sample variability. It is an approximation under conditions, not a guarantee for every statistic or sample.\n",
    "\n",
    "The sample is about 1.35% of the filtered population, so the difference between the original without-replacement sampling and the ordinary with-replacement bootstrap is small here. If the sampling fraction were large, that distinction would need attention.\n",
    "\n",
    "A bootstrap cannot repair nonresponse bias, restore excluded groups, or discover an unseen population maximum. More bootstrap repetitions do not fix those problems.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1d80e1",
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "source": [
    "```{figure} ../../figures/bootstrap_pic.png\n",
    "---\n",
    "height: 750px\n",
    "name: bootstrap_pic\n",
    "---\n",
    "Bootstrap\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a053a4",
   "metadata": {},
   "source": [
    "## A Resampled Median\n",
    "The pandas `sample` method draws one row without replacement by default, so for the bootstrap we set both arguments explicitly: `sample(len(our_sample), replace=True)` draws as many rows as the original sample, with replacement. Here is one new sample drawn from the original sample, and the corresponding sample median."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73db95c",
   "metadata": {},
   "source": [
    "### Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c38260f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.145061Z",
     "iopub.status.busy": "2026-08-31T06:01:26.145004Z",
     "iopub.status.idle": "2026-08-31T06:01:26.147627Z",
     "shell.execute_reply": "2026-08-31T06:01:26.147419Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "resample_1 = our_sample.sample(len(our_sample), replace=True, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2dad487",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.148637Z",
     "iopub.status.busy": "2026-08-31T06:01:26.148565Z",
     "iopub.status.idle": "2026-08-31T06:01:26.152499Z",
     "shell.execute_reply": "2026-08-31T06:01:26.152351Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "                             Organization Group  \\\n12985   Public Works, Transportation & Commerce   \n16538   Public Works, Transportation & Commerce   \n21664   Public Works, Transportation & Commerce   \n10542   Public Works, Transportation & Commerce   \n26693                          Community Health   \n...                                         ...   \n23679  Human Welfare & Neighborhood Development   \n34182                          Community Health   \n40968          General Administration & Finance   \n25841  Human Welfare & Neighborhood Development   \n41838          General Administration & Finance   \n\n                          Department                     Job Family  \\\n12985  Municipal Transportation Agcy               Journeyman Trade   \n16538  Municipal Transportation Agcy                 Street Transit   \n21664    Public Utilities Commission               Journeyman Trade   \n10542            Building Inspection       Professional Engineering   \n26693                  Public Health            Information Systems   \n...                              ...                            ...   \n23679                 Human Services            Public Service Aide   \n34182                  Public Health                        Nursing   \n40968        Administrative Services                     Management   \n25841                 Human Services  Clerical, Secretarial & Steno   \n41838           Board Of Supervisors            Information Systems   \n\n                                  Job    Salary  Overtime  Benefits  \\\n12985       Transit Power Line Worker  116545.0   64340.0   25075.0   \n16538                Transit Operator   38795.0   13149.0   28440.0   \n21664  Sr Statnry Eng, Wtr Treat Plnt  132790.0   30471.0   53760.0   \n10542                  Assoc Engineer  115963.0       0.0   47307.0   \n26693      IS Business Analyst-Senior  127852.0       0.0   49398.0   \n...                               ...       ...       ...       ...   \n23679    Public Svc Aide-Public Works   15913.0       0.0    9701.0   \n34182                Registered Nurse   20730.0    1047.0    8599.0   \n40968                     Dep Dir III  197386.0       0.0   68032.0   \n25841             Senior Clerk Typist   70798.0      10.0   34862.0   \n41838  IT Operations Support Admin IV  121411.0       0.0   49022.0   \n\n       Total Compensation  \n12985            205959.0  \n16538             80384.0  \n21664            217022.0  \n10542            163271.0  \n26693            177250.0  \n...                   ...  \n23679             25614.0  \n34182             30375.0  \n40968            265418.0  \n25841            105670.0  \n41838            170434.0  \n\n[500 rows x 8 columns]",
      "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>Organization Group</th>\n      <th>Department</th>\n      <th>Job Family</th>\n      <th>Job</th>\n      <th>Salary</th>\n      <th>Overtime</th>\n      <th>Benefits</th>\n      <th>Total Compensation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12985</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Municipal Transportation Agcy</td>\n      <td>Journeyman Trade</td>\n      <td>Transit Power Line Worker</td>\n      <td>116545.0</td>\n      <td>64340.0</td>\n      <td>25075.0</td>\n      <td>205959.0</td>\n    </tr>\n    <tr>\n      <th>16538</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Municipal Transportation Agcy</td>\n      <td>Street Transit</td>\n      <td>Transit Operator</td>\n      <td>38795.0</td>\n      <td>13149.0</td>\n      <td>28440.0</td>\n      <td>80384.0</td>\n    </tr>\n    <tr>\n      <th>21664</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Public Utilities Commission</td>\n      <td>Journeyman Trade</td>\n      <td>Sr Statnry Eng, Wtr Treat Plnt</td>\n      <td>132790.0</td>\n      <td>30471.0</td>\n      <td>53760.0</td>\n      <td>217022.0</td>\n    </tr>\n    <tr>\n      <th>10542</th>\n      <td>Public Works, Transportation &amp; Commerce</td>\n      <td>Building Inspection</td>\n      <td>Professional Engineering</td>\n      <td>Assoc Engineer</td>\n      <td>115963.0</td>\n      <td>0.0</td>\n      <td>47307.0</td>\n      <td>163271.0</td>\n    </tr>\n    <tr>\n      <th>26693</th>\n      <td>Community Health</td>\n      <td>Public Health</td>\n      <td>Information Systems</td>\n      <td>IS Business Analyst-Senior</td>\n      <td>127852.0</td>\n      <td>0.0</td>\n      <td>49398.0</td>\n      <td>177250.0</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    </tr>\n    <tr>\n      <th>23679</th>\n      <td>Human Welfare &amp; Neighborhood Development</td>\n      <td>Human Services</td>\n      <td>Public Service Aide</td>\n      <td>Public Svc Aide-Public Works</td>\n      <td>15913.0</td>\n      <td>0.0</td>\n      <td>9701.0</td>\n      <td>25614.0</td>\n    </tr>\n    <tr>\n      <th>34182</th>\n      <td>Community Health</td>\n      <td>Public Health</td>\n      <td>Nursing</td>\n      <td>Registered Nurse</td>\n      <td>20730.0</td>\n      <td>1047.0</td>\n      <td>8599.0</td>\n      <td>30375.0</td>\n    </tr>\n    <tr>\n      <th>40968</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Administrative Services</td>\n      <td>Management</td>\n      <td>Dep Dir III</td>\n      <td>197386.0</td>\n      <td>0.0</td>\n      <td>68032.0</td>\n      <td>265418.0</td>\n    </tr>\n    <tr>\n      <th>25841</th>\n      <td>Human Welfare &amp; Neighborhood Development</td>\n      <td>Human Services</td>\n      <td>Clerical, Secretarial &amp; Steno</td>\n      <td>Senior Clerk Typist</td>\n      <td>70798.0</td>\n      <td>10.0</td>\n      <td>34862.0</td>\n      <td>105670.0</td>\n    </tr>\n    <tr>\n      <th>41838</th>\n      <td>General Administration &amp; Finance</td>\n      <td>Board Of Supervisors</td>\n      <td>Information Systems</td>\n      <td>IT Operations Support Admin IV</td>\n      <td>121411.0</td>\n      <td>0.0</td>\n      <td>49022.0</td>\n      <td>170434.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 15
    }
   ],
   "source": [
    "resample_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b82e2fc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.153340Z",
     "iopub.status.busy": "2026-08-31T06:01:26.153276Z",
     "iopub.status.idle": "2026-08-31T06:01:26.191325Z",
     "shell.execute_reply": "2026-08-31T06:01:26.191103Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Compensation in One Bootstrap Resample')"
     },
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x400 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "sf_bins = np.arange(0, 775000, 25000)\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.hist(resample_1['Total Compensation'], bins=sf_bins, density=True, edgecolor='white')\n",
    "ax.set_xlabel('Annual Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.set_title('Compensation in One Bootstrap Resample')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Resample the Same Size\n",
    "# Generate a bootstrap resample with replacement and the same size as salaries.\n",
    "# Print the original and resample sizes, then the number of distinct resampled values.\n",
    "import pandas as pd\n",
    "salaries = pd.Series([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "### Your code starts here.\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Original size: 10\nResample size: 10\nDistinct resampled values: 7\n"
    }
   ],
   "source": [
    "### Solution\n",
    "import pandas as pd\n",
    "salaries = pd.Series([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "resample = salaries.sample(len(salaries), replace=True, random_state=1)\n",
    "print('Original size:', len(salaries))\n",
    "print('Resample size:', len(resample))\n",
    "print('Distinct resampled values:', resample.nunique())\n",
    "# Sampling without replacement at the same size would only reorder the sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8b4984",
   "metadata": {},
   "source": [
    "### Get Median(s) from Percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89fa9f24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.192506Z",
     "iopub.status.busy": "2026-08-31T06:01:26.192414Z",
     "iopub.status.idle": "2026-08-31T06:01:26.194410Z",
     "shell.execute_reply": "2026-08-31T06:01:26.194225Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "132903.0"
     },
     "execution_count": 19
    }
   ],
   "source": [
    "resampled_median_1 = np.median(resample_1['Total Compensation']).item()\n",
    "resampled_median_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bc760a8",
   "metadata": {},
   "source": [
    "This value is an estimate of the population median.\n",
    "\n",
    "By resampling again and again, we can get many such estimates, and hence an empirical distribution of the estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f41349f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.195358Z",
     "iopub.status.busy": "2026-08-31T06:01:26.195298Z",
     "iopub.status.idle": "2026-08-31T06:01:26.197254Z",
     "shell.execute_reply": "2026-08-31T06:01:26.197067Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "138088.0"
     },
     "execution_count": 20
    }
   ],
   "source": [
    "resample_2 = our_sample.sample(len(our_sample), replace=True)\n",
    "resampled_median_2 = np.median(resample_2['Total Compensation']).item()\n",
    "resampled_median_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15db8761",
   "metadata": {},
   "source": [
    "Let us collect this code and define a function `one_bootstrap_median` that returns one bootstrapped median of total compensation, based on bootstrapping the original random sample that we called `our_sample`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85573f79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.198334Z",
     "iopub.status.busy": "2026-08-31T06:01:26.198270Z",
     "iopub.status.idle": "2026-08-31T06:01:26.199746Z",
     "shell.execute_reply": "2026-08-31T06:01:26.199560Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def one_bootstrap_median():\n",
    "    resampled_table = our_sample.sample(len(our_sample), replace=True)\n",
    "    bootstrapped_median = np.median(resampled_table['Total Compensation']).item()\n",
    "    return bootstrapped_median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb50d63",
   "metadata": {},
   "source": [
    "Run the cell below a few times to see how the bootstrapped medians vary. Remember that each of them is an estimate of the population median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02f85970",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.200610Z",
     "iopub.status.busy": "2026-08-31T06:01:26.200553Z",
     "iopub.status.idle": "2026-08-31T06:01:26.202388Z",
     "shell.execute_reply": "2026-08-31T06:01:26.202222Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "147936.0"
     },
     "execution_count": 22
    }
   ],
   "source": [
    "one_bootstrap_median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Collect Bootstrap Statistics\n",
    "# Use a single seeded generator to make 100 resamples and collect their medians.\n",
    "# Print how many statistics were collected. Each resample must have size len(values).\n",
    "import numpy as np\n",
    "rng = np.random.default_rng(42)\n",
    "values = np.array([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "### Your code starts here.\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Bootstrap statistics: 100\n"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "rng = np.random.default_rng(42)\n",
    "values = np.array([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "medians = []\n",
    "for _ in range(100):\n",
    "    resample = rng.choice(values, size=len(values), replace=True)\n",
    "    medians.append(np.median(resample))\n",
    "print('Bootstrap statistics:', len(medians))\n",
    "# There are still only ten original observations, despite the 100 resamples."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6b036f",
   "metadata": {},
   "source": [
    "## Bootstrap Empirical Distribution of the Sample Median\n",
    "\n",
    "We can now repeat the bootstrap process multiple times by running a `for` loop as usual. In each iteration, we will call the function `one_bootstrap_median` to generate one value of the bootstrapped median based on our original sample `our_sample`. Then we will append the bootstrapped median to the collection array `bstrap_medians`.\n",
    "\n",
    "Since we are asking for 5000 repetitions, the code might take a while to run. It has a lot of resampling to do!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e050a672",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.203238Z",
     "iopub.status.busy": "2026-08-31T06:01:26.203178Z",
     "iopub.status.idle": "2026-08-31T06:01:26.463225Z",
     "shell.execute_reply": "2026-08-31T06:01:26.462919Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "num_repetitions = 5000\n",
    "bstrap_medians = np.array([])\n",
    "for _ in range(num_repetitions):\n",
    "    bstrap_medians = np.append(bstrap_medians, one_bootstrap_median())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242615a2",
   "metadata": {},
   "source": [
    "Here is an empirical histogram of the 5000 bootstrapped medians. The green line is the population parameter: it is the median of the entire population, which is what we are trying to estimate. In this example we happen to know its value, but we did not use it in the bootstrap process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee5614af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.464469Z",
     "iopub.status.busy": "2026-08-31T06:01:26.464403Z",
     "iopub.status.idle": "2026-08-31T06:01:26.504967Z",
     "shell.execute_reply": "2026-08-31T06:01:26.504746Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x115f69be0>"
     },
     "execution_count": 26
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x400 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "resampled_medians = pd.DataFrame({'Bootstrap Sample Median': bstrap_medians})\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.hist(bstrap_medians, bins=20, density=True, edgecolor='white')\n",
    "ax.axvline(pop_median, color='green', label='Known filtered-population median')\n",
    "ax.axvline(est_median, color='black', linestyle='--', label='Original sample median')\n",
    "ax.set_xlabel('Bootstrap Median Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.set_title('Statistics from Resampling One Fixed Sample')\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.legend(fontsize=9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46e83fd",
   "metadata": {},
   "source": [
    "The population median is fixed at $135,747. The original sample median is one point estimate. The bootstrap medians vary because the resamples vary, and their distribution is conditional on this one original sample.\n",
    "\n",
    "The point estimates do not have to equal the parameter. The interval procedure is designed to capture the parameter frequently across new original samples, not to guarantee a correct interval every time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d839d45",
   "metadata": {},
   "source": [
    "## Do the Estimates Capture the Parameter?\n",
    "\n",
    "Does the middle 95% of the bootstrap medians contain the known population median? The endpoints below define that interval. We can check it here because the population is available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4bdfcfa0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.506067Z",
     "iopub.status.busy": "2026-08-31T06:01:26.506004Z",
     "iopub.status.idle": "2026-08-31T06:01:26.507815Z",
     "shell.execute_reply": "2026-08-31T06:01:26.507652Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "132935.0"
     },
     "execution_count": 27
    }
   ],
   "source": [
    "left = np.percentile(bstrap_medians, 2.5, method='nearest').item()\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "82d1329f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.508711Z",
     "iopub.status.busy": "2026-08-31T06:01:26.508653Z",
     "iopub.status.idle": "2026-08-31T06:01:26.510357Z",
     "shell.execute_reply": "2026-08-31T06:01:26.510188Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "147936.0"
     },
     "execution_count": 28
    }
   ],
   "source": [
    "right = np.percentile(bstrap_medians, 97.5, method='nearest').item()\n",
    "right"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b924ec0",
   "metadata": {},
   "source": [
    "Compare the fixed population median with the two endpoints printed above. The following plot displays whether this particular interval captures it. An interval's nominal level does not guarantee its success for every sample.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a51ca15e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.511308Z",
     "iopub.status.busy": "2026-08-31T06:01:26.511255Z",
     "iopub.status.idle": "2026-08-31T06:01:26.552256Z",
     "shell.execute_reply": "2026-08-31T06:01:26.552052Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1164ace10>"
     },
     "execution_count": 29
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x400 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "resampled_medians = pd.DataFrame({'Bootstrap Sample Median': bstrap_medians})\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.hist(bstrap_medians, bins=20, density=True, edgecolor='white')\n",
    "ax.axvline(pop_median, color='green', label='Known filtered-population median')\n",
    "ax.axvline(est_median, color='black', linestyle='--', label='Original sample median')\n",
    "ax.axvspan(left, right, color='gold', alpha=0.3, label='95% percentile interval')\n",
    "ax.set_xlabel('Bootstrap Median Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.set_title('Statistics from Resampling One Fixed Sample')\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.legend(fontsize=9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a713081",
   "metadata": {},
   "source": [
    "One interval cannot establish a long-run coverage rate. To examine coverage, repeat the **whole experiment**, drawing a new original sample from the known population each time, then bootstrapping that sample and constructing its interval.\n",
    "\n",
    "The demonstration below constructs 100 intervals. Each uses an original sample of 500 employees and 1,000 bootstrap resamples. These counts describe different operations: 100 new population samples, each with 1,000 resamples from its own fixed sample. Coverage should be near the nominal level when the method's assumptions are adequate, but the observed count is random and the bootstrap is approximate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b521e79",
   "metadata": {},
   "source": [
    "The function `bootstrap_median(original_sample, num_repetitions)` returns one ordinary median per bootstrap resample. The array pattern `np.random.choice(values, size=(B, n), replace=True)` makes a table with `B` rows of `n` draws. Each row is one resample; `np.median(..., axis=1)` computes its median. This performs the same steps as a loop, using array operations to keep the coverage demonstration fast.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb8a4ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.553354Z",
     "iopub.status.busy": "2026-08-31T06:01:26.553292Z",
     "iopub.status.idle": "2026-08-31T06:01:26.554871Z",
     "shell.execute_reply": "2026-08-31T06:01:26.554675Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bootstrap_median(original_sample, num_repetitions):\n",
    "    values = original_sample['Total Compensation'].to_numpy()\n",
    "    resamples = np.random.choice(values, size=(num_repetitions, len(values)), replace=True)\n",
    "    return np.median(resamples, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a81bc2",
   "metadata": {},
   "source": [
    "For each of 100 new samples, calculate a bootstrap interval and store its endpoints. The seed makes the demonstration reproducible. Increase the number of outer experiments to study coverage more precisely; increase the inner bootstrap count to stabilize each interval's numerical endpoints.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "742ec776",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:26.555895Z",
     "iopub.status.busy": "2026-08-31T06:01:26.555837Z",
     "iopub.status.idle": "2026-08-31T06:01:59.613728Z",
     "shell.execute_reply": "2026-08-31T06:01:59.613173Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "coverage_repetitions = 100\n",
    "bootstrap_repetitions = 1000\n",
    "left_ends = []\n",
    "right_ends = []\n",
    "for _ in range(coverage_repetitions):\n",
    "    original_sample = sf2019.sample(500, replace=False)\n",
    "    medians = bootstrap_median(original_sample, bootstrap_repetitions)\n",
    "    left, right = np.percentile(medians, [2.5, 97.5], method='nearest')\n",
    "    left_ends.append(left)\n",
    "    right_ends.append(right)\n",
    "intervals = pd.DataFrame({'Left': left_ends, 'Right': right_ends})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a904d7",
   "metadata": {},
   "source": [
    "For each of the 100 replications of the entire process, we get one interval of estimates of the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f3be953d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.616191Z",
     "iopub.status.busy": "2026-08-31T06:01:59.616090Z",
     "iopub.status.idle": "2026-08-31T06:01:59.620753Z",
     "shell.execute_reply": "2026-08-31T06:01:59.620517Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "        Left     Right\n0   132994.0  147981.0\n1   125636.0  139337.0\n2   132310.5  142158.5\n3   131303.5  146577.0\n4   131202.0  142432.0\n..       ...       ...\n95  132362.5  145871.5\n96  129444.0  141121.5\n97  128336.5  145259.0\n98  128859.0  141235.5\n99  127795.0  139925.5\n\n[100 rows x 2 columns]",
      "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>Left</th>\n      <th>Right</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>132994.0</td>\n      <td>147981.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>125636.0</td>\n      <td>139337.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>132310.5</td>\n      <td>142158.5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>131303.5</td>\n      <td>146577.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>131202.0</td>\n      <td>142432.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>132362.5</td>\n      <td>145871.5</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>129444.0</td>\n      <td>141121.5</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>128336.5</td>\n      <td>145259.0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>128859.0</td>\n      <td>141235.5</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>127795.0</td>\n      <td>139925.5</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 32
    }
   ],
   "source": [
    "intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0b4d07",
   "metadata": {},
   "source": [
    "The good intervals are those that contain the parameter we are trying to estimate. Typically the parameter is unknown, but in this section we happen to know what the parameter is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "044f125c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.624370Z",
     "iopub.status.busy": "2026-08-31T06:01:59.624145Z",
     "iopub.status.idle": "2026-08-31T06:01:59.627173Z",
     "shell.execute_reply": "2026-08-31T06:01:59.626963Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "135747.0"
     },
     "execution_count": 33
    }
   ],
   "source": [
    "pop_median"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd848e5",
   "metadata": {},
   "source": [
    "An interval covers the parameter when `Left <= parameter <= Right`. Include equality at either endpoint, which matters for discrete data and rounded values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "57233113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.628263Z",
     "iopub.status.busy": "2026-08-31T06:01:59.628197Z",
     "iopub.status.idle": "2026-08-31T06:01:59.630616Z",
     "shell.execute_reply": "2026-08-31T06:01:59.630297Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Intervals containing the parameter: 97 of 100\nObserved coverage: 0.97\n"
    }
   ],
   "source": [
    "covered = (intervals['Left'] <= pop_median) & (pop_median <= intervals['Right'])\n",
    "print('Intervals containing the parameter:', int(covered.sum()), 'of', len(intervals))\n",
    "print('Observed coverage:', round(covered.mean(), 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Include the Interval Endpoints\n",
    "# Count the intervals that include parameter, including equality at either endpoint.\n",
    "import numpy as np\n",
    "parameter = 10\n",
    "left = np.array([8, 10, 11])\n",
    "right = np.array([10, 12, 14])\n",
    "### Your code starts here.\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Intervals covering the parameter: 2\n"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "parameter = 10\n",
    "left = np.array([8, 10, 11])\n",
    "right = np.array([10, 12, 14])\n",
    "covered_practice = (left <= parameter) & (parameter <= right)\n",
    "print('Intervals covering the parameter:', np.count_nonzero(covered_practice))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77f5bbe",
   "metadata": {},
   "source": [
    "The printed fraction is the observed coverage in this finite demonstration. It need not be exactly 95%. The nominal 95% level is an intended property of the interval procedure under suitable conditions, not a promise about every set of 100 intervals.\n",
    "\n",
    "In the next graph, each horizontal segment is one interval. Green is the fixed population median; blue intervals cover it and red intervals miss it. The vertical axis identifies the outer experiment, not an employee or a bootstrap resample.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ceb572c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.631609Z",
     "iopub.status.busy": "2026-08-31T06:01:59.631548Z",
     "iopub.status.idle": "2026-08-31T06:01:59.705011Z",
     "shell.execute_reply": "2026-08-31T06:01:59.704779Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "metadata": {},
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x116608550>"
     },
     "execution_count": 37
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "<Figure size 700x600 with 1 Axes>",
      "image/png": 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"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "for i in range(len(intervals)):\n",
    "    ends = intervals.iloc[i]\n",
    "    color = 'tab:blue' if covered.iloc[i] else 'tab:red'\n",
    "    ax.plot([ends['Left'], ends['Right']], [i + 1, i + 1], color=color, linewidth=1)\n",
    "ax.axvline(pop_median, color='green', label='Population median')\n",
    "ax.set_xlabel('Median Total Compensation (Dollars)')\n",
    "ax.set_ylabel('Repeated Original Sample')\n",
    "ax.set_title('Coverage Across New Original Samples')\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a251ca",
   "metadata": {},
   "source": [
    "One bootstrap percentile interval follows this workflow: draw a representative original sample, resample it with replacement at the same size, calculate the same statistic for every resample, and take the chosen percentile endpoints of those statistics.\n\nConfidence concerns what happens when the **entire sampling-and-interval process** is repeated. For a reliable approximate 95% method, about 95% of intervals cover the fixed parameter in the long run. After calculating one interval, we generally do not know whether it covers the parameter.\n\nChanging the confidence level from 95% to 80% selects a central range that is no wider from the same bootstrap statistics. It reduces the intended long-run coverage. Increasing bootstrap repetitions alone does not produce narrower intervals or more independent data.\n\n```{index} Confidence level\n```\n\n```{index} Bootstrap distribution\n```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "917b6018",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cell-628bdc8d",
   "metadata": {},
   "source": [
    "\n",
    "## Section Practice\n",
    "\n",
    "Use this short exercise to check the main workflow from this section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cell-805c9682",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.706238Z",
     "iopub.status.busy": "2026-08-31T06:01:59.706169Z",
     "iopub.status.idle": "2026-08-31T06:01:59.707801Z",
     "shell.execute_reply": "2026-08-31T06:01:59.707489Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Practice The Bootstrap\n",
    "# HR has a random sample of 10 salaries (in $1,000s).\n",
    "#   1. Draw one bootstrap resample with sample.sample(len(sample), replace=True, random_state=1)\n",
    "#      and compute its median.\n",
    "#   2. Repeat with 1,000 resamples (random_state=i for i in range(1000)) and\n",
    "#      collect the medians.\n",
    "#   3. Print the sample median, the first resample's median, and the range\n",
    "#      (min and max) of the 1,000 bootstrap medians.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "sample = pd.Series([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cell-9e1ce82c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:01:59.709454Z",
     "iopub.status.busy": "2026-08-31T06:01:59.709376Z",
     "iopub.status.idle": "2026-08-31T06:01:59.711260Z",
     "shell.execute_reply": "2026-08-31T06:01:59.711089Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Sample median: 71.0\nFirst resample median: 73.5\nBootstrap medians range from 60.0 to 88.0\n"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "sample = pd.Series([58, 62, 64, 66, 70, 72, 75, 81, 88, 120])\n",
    "\n",
    "first = sample.sample(len(sample), replace=True, random_state=1)\n",
    "medians = np.array([sample.sample(len(sample), replace=True, random_state=i).median() for i in range(1000)])\n",
    "\n",
    "print('Sample median:', sample.median())\n",
    "print('First resample median:', first.median())\n",
    "print('Bootstrap medians range from', medians.min(), 'to', medians.max())\n"
   ]
  }
 ],
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