{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Applied Asymptotics Project\n\nLearning goal: Plan a complete large-sample inference workflow.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Dataset check\nThis cell confirms the dataset file is available. Simulation notebooks mount a harmless CSV so public file delivery is still validated.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "DATASET = \"WAGE1.csv\"\n",
        "VARIABLES = [\"wage\",\"educ\",\"exper\",\"tenure\"]\n",
        "if not os.path.exists(DATASET):\n",
        "    raise FileNotFoundError(\n",
        "        \"Dataset file not installed yet\\n\"\n",
        "        \"Dataset: WAGE1\\n\"\n",
        "        \"Variables needed: wage, educ, exper, tenure\\n\"\n",
        "        \"Course data folder: https://drive.google.com/drive/folders/1_STdcydIcst-opcbwOKRFzUXsgxQgBoS?usp=sharing\\n\"\n",
        "        \"Admin upload instruction: upload the dataset in Admin -> Datasets, publish it, and make the file available to the notebook runner.\"\n",
        "    )\n",
        "print(\"Ready:\", DATASET)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run the lab\nRun the code, inspect the table or graph, and connect the result to the formula in the lesson.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "checklist = pd.DataFrame({\n",
        "    \"step\": [\"choose datasets\", \"fit baseline model\", \"inspect histograms\", \"simulate sampling\", \"compare standard errors\", \"compute LM test\", \"write limits\"],\n",
        "    \"done\": [False, False, False, False, False, False, False]\n",
        "})\n",
        "print(checklist)\n",
        "print(\"Use the Module 5 notebooks to fill this checklist with real outputs and interpretation.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Formula explanation\nExplain the probability limit, asymptotic approximation, standard-error pattern, or LM statistic in words. Do not treat output as automatic causal evidence.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Short exercise\nChange one sample size, regressor, or restriction. Write two sentences: what changed mechanically, and what assumption still matters?\n\n## Check your understanding\nDoes increasing n fix nonnormality, endogeneity, heteroskedasticity, or omitted variables? Explain.\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}