notebooks/stata_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e13951bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded client.js from C:\\Users\\jjsan\\GitHub\\jupyter-notebook-mcp\\src\\client.js\n",
      "WebSocket server started on ws://localhost:8765\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <script>\n",
       "        (function(){\n",
       "    // Connect to WebSocket server\n",
       "    var ws = new WebSocket(\"ws://localhost:8765\");\n",
       "    \n",
       "    ws.onopen = function() {\n",
       "        // Identify as notebook client\n",
       "        ws.send(JSON.stringify({ role: \"notebook\" }));\n",
       "        console.log(\"Connected to WebSocket server as notebook client\");\n",
       "    };\n",
       "    \n",
       "    ws.onmessage = function(event) {\n",
       "        var data = JSON.parse(event.data);\n",
       "        \n",
       "        // Handle different action types\n",
       "        switch(data.type) {\n",
       "            case \"insert_and_execute_cell\":\n",
       "                handleInsertCell(data);\n",
       "                break;\n",
       "            case \"save_notebook\":\n",
       "                handleSaveNotebook(data);\n",
       "                break;\n",
       "            case \"get_cells_info\":\n",
       "                handleGetCellsInfo(data);\n",
       "                break;\n",
       "            case \"get_notebook_info\":\n",
       "                handleGetNotebookInfo(data);\n",
       "                break;\n",
       "            case \"run_cell\":\n",
       "                handleRunCell(data);\n",
       "                break;\n",
       "            case \"run_all_cells\":\n",
       "                handleRunAllCells(data);\n",
       "                break;\n",
       "            case \"get_cell_text_output\":\n",
       "                handleGetCellTextOutput(data);\n",
       "                break;\n",
       "            case \"get_cell_image_output\":\n",
       "                handleGetCellImageOutput(data);\n",
       "                break;\n",
       "            case \"edit_cell_content\":\n",
       "                handleEditCellContent(data);\n",
       "                break;\n",
       "            case \"set_slideshow_type\":\n",
       "                handleSetSlideshowType(data);\n",
       "                break;\n",
       "            default:\n",
       "                console.warn(\"Unknown message type:\", data.type);\n",
       "        }\n",
       "    };\n",
       "\n",
       "    function sendError(responseType, request_id, error, logMessage) {\n",
       "        var errorResponse = {\n",
       "            type: responseType,\n",
       "            request_id: request_id,\n",
       "            status: \"error\",\n",
       "            source: \"notebook\",\n",
       "            target: \"external\",\n",
       "            message: error.toString()\n",
       "        };\n",
       "        ws.send(JSON.stringify(errorResponse));\n",
       "        console.error(logMessage, error);\n",
       "    }\n",
       "    \n",
       "    // Handle inserting and executing a cell\n",
       "    function handleInsertCell(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var position = data.position || 1;\n",
       "        var cell_type = data.cell_type || \"code\";\n",
       "        var content = data.content || \"\";\n",
       "        \n",
       "        try {\n",
       "            var cell = Jupyter.notebook.insert_cell_at_index(cell_type, position);\n",
       "            cell.set_text(content);\n",
       "            \n",
       "            // Function to capture and send output with truncation\n",
       "            var sendResult = function() {\n",
       "                var outputResult = extractCellOutputContent(cell, 1500);\n",
       "                \n",
       "                // Prepare response\n",
       "                var response = {\n",
       "                    type: \"insert_cell_result\",\n",
       "                    request_id: request_id,\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    status: \"success\",\n",
       "                    cell_id: cell.cell_id || \"cell_\" + position,\n",
       "                    position: position,\n",
       "                    output_text: outputResult.text,\n",
       "                    is_truncated: outputResult.is_truncated,\n",
       "                    has_images: outputResult.images.length > 0,\n",
       "                };\n",
       "                \n",
       "                ws.send(JSON.stringify(response));\n",
       "                console.log(\"Cell inserted at position \" + position + (outputResult.is_truncated ? \" (output truncated)\" : \"\"));\n",
       "            };\n",
       "            \n",
       "            if (cell_type === \"code\") {\n",
       "                // Listen for execution completion\n",
       "                cell.events.one('finished_execute.CodeCell', function() {\n",
       "                    sendResult();\n",
       "                });\n",
       "                cell.execute();\n",
       "            } else if (cell_type === \"markdown\") {\n",
       "                cell.render();\n",
       "                sendResult(); // For markdown, send result immediately\n",
       "            } else {\n",
       "                sendResult(); // For other types, send result immediately\n",
       "            }\n",
       "        } catch (error) {\n",
       "            sendError(\"insert_cell_result\", request_id, error, \"Error inserting cell:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle saving the notebook\n",
       "    function handleSaveNotebook(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.save_checkpoint();\n",
       "            \n",
       "            var response = {\n",
       "                type: \"save_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                notebook_path: Jupyter.notebook.notebook_path\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Notebook saved successfully\");\n",
       "        } catch (error) {\n",
       "            sendError(\"save_result\", request_id, error, \"Error saving notebook:\");\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    // Handle getting information about all cells\n",
       "    function handleGetCellsInfo(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            var cellsInfo = cells.map(function(cell, index) {\n",
       "                return {\n",
       "                    id: cell.cell_id || \"cell_\" + index,\n",
       "                    position: index,\n",
       "                    content: cell.get_text(),\n",
       "                    type: cell.cell_type,\n",
       "                    prompt_number: cell.input_prompt_number,\n",
       "                };\n",
       "            });\n",
       "            \n",
       "            var response = {\n",
       "                type: \"cells_info_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                cells: cellsInfo\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Sent info for \" + cellsInfo.length + \" cells\");\n",
       "        } catch (error) {\n",
       "            sendError(\"cells_info_result\", request_id, error, \"Error getting cells info:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle getting notebook information\n",
       "    function handleGetNotebookInfo(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            var nbInfo = {\n",
       "                notebook_name: Jupyter.notebook.notebook_name,\n",
       "                notebook_path: Jupyter.notebook.notebook_path,\n",
       "                kernel_name: Jupyter.notebook.kernel.name,\n",
       "                cell_count: Jupyter.notebook.get_cells().length,\n",
       "                modified: Jupyter.notebook.dirty,\n",
       "                trusted: Jupyter.notebook.trusted\n",
       "            };\n",
       "            \n",
       "            var response = {\n",
       "                type: \"notebook_info_result\",\n",
       "                request_id: request_id,\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                status: \"success\",\n",
       "                notebook_info: nbInfo\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Sent notebook info\");\n",
       "        } catch (error) {\n",
       "            sendError(\"notebook_info_result\", request_id, error, \"Error getting notebook info:\");\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    // Handle running a specific cell by index\n",
       "    function handleRunCell(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.select(index);\n",
       "            var cell = Jupyter.notebook.get_selected_cell();\n",
       "            \n",
       "            // Generate a cell_id if not available\n",
       "            var cell_id = cell.cell_id || Date.now().toString();\n",
       "            \n",
       "            // Function to send execution results back\n",
       "            var sendResult = function() {\n",
       "                var outputs = extractCellOutputContent(cell, 1500);\n",
       "\n",
       "                var resultMsg = {\n",
       "                    type: \"run_cell_result\",\n",
       "                    request_id: request_id,\n",
       "                    status: \"success\",\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    cell_id: cell_id,\n",
       "                    index: index,\n",
       "                    output_text: outputs.text,\n",
       "                    is_truncated: outputs.is_text_truncated,\n",
       "                    has_images: outputs.images.length > 0,\n",
       "                };\n",
       "                ws.send(JSON.stringify(resultMsg));\n",
       "                console.log(\"Cell execution result sent\");\n",
       "            };\n",
       "            \n",
       "            // Listen for execution completion\n",
       "            cell.events.one('finished_execute.CodeCell', function() {\n",
       "                sendResult();\n",
       "            });\n",
       "            \n",
       "            // Execute the cell\n",
       "            cell.execute();\n",
       "            \n",
       "        } catch (error) {\n",
       "            sendError(\"run_cell_result\", request_id, error, \"Error running cell:\");\n",
       "        }\n",
       "    }    \n",
       "    \n",
       "    // Handle running all cells\n",
       "    function handleRunAllCells(data) {\n",
       "        var request_id = data.request_id;\n",
       "        \n",
       "        try {\n",
       "            Jupyter.notebook.restart_run_all();\n",
       "            \n",
       "            var response = {\n",
       "                type: \"run_all_cells_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Running all cells\");\n",
       "        } catch (error) {\n",
       "            sendError(\"run_all_cells_result\", request_id, error, \"Error running all cells:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Handle getting output from a specific cell\n",
       "    function handleGetCellTextOutput(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var maxLength = data.max_length || 1500;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            \n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            var outputContent = extractCellOutputContent(cell, maxLength);\n",
       "            \n",
       "            var response = {\n",
       "                type: \"get_cell_text_output_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                output_text: outputContent.text,\n",
       "                is_truncated: outputContent.is_text_truncated,\n",
       "                has_images: outputContent.images.length > 0\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell output retrieved for index \" + index + \n",
       "                    (outputContent.is_text_truncated ? \" (output truncated)\" : \"\") + \n",
       "                    (outputContent.images.length > 0 ? \" (contains images)\" : \"\"));\n",
       "        } catch (error) {\n",
       "            sendError(\"get_cell_text_output_result\", request_id, error, \"Error getting cell output:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleGetCellImageOutput(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        \n",
       "        try {\n",
       "            // Obtener la celda en el Ã­ndice especificado\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            \n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            var outputContent = extractCellOutputContent(cell);\n",
       "            \n",
       "            var response = {\n",
       "                type: \"get_cell_image_output_result\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                images: outputContent.images\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell image output retrieved for index \" + index + \" (\" + outputContent.images.length + \" images)\");\n",
       "        } catch (error) {\n",
       "            sendError(\"get_cell_image_output_result\", request_id, error, \"Error getting cell image output:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleEditCellContent(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var content = data.content || \"\";\n",
       "        var execute = data.execute || false;\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            cell.set_text(content);\n",
       "            \n",
       "            var sendResult = function() {\n",
       "                var outputResult = extractCellOutputContent(cell, 1500);\n",
       "                \n",
       "                var response = {\n",
       "                    type: \"edit_cell_content_result\",\n",
       "                    source: \"notebook\",\n",
       "                    target: \"external\",\n",
       "                    request_id: request_id,\n",
       "                    status: \"success\",\n",
       "                    cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                    index: index,\n",
       "                    output_text: execute ? outputResult.text : \"\",\n",
       "                    is_truncated: execute ? outputResult.is_text_truncated : false,\n",
       "                    has_images: execute ? outputResult.images.length > 0 : false\n",
       "                };\n",
       "                \n",
       "                ws.send(JSON.stringify(response));\n",
       "                console.log(\"Cell content updated at index \" + index + \n",
       "                    (execute ? \" and executed\" : \"\") +\n",
       "                    (outputResult.is_text_truncated ? \" (output truncated)\" : \"\"));\n",
       "            };\n",
       "            \n",
       "            if (cell.cell_type === \"code\" && execute) {\n",
       "                cell.events.one('finished_execute.CodeCell', function() {\n",
       "                    sendResult();\n",
       "                });\n",
       "                cell.execute();\n",
       "            } else if (cell.cell_type === \"markdown\") {\n",
       "                cell.render();\n",
       "                sendResult();\n",
       "            } else {\n",
       "                sendResult();\n",
       "            }\n",
       "        } catch (error) {\n",
       "            sendError(\"edit_cell_content_result\", request_id, error, \"Error updating cell content:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function handleSetSlideshowType(data) {\n",
       "        var request_id = data.request_id;\n",
       "        var index = data.index || 0;\n",
       "        var slideshow_type = data.slideshow_type || \"-\";\n",
       "        \n",
       "        try {\n",
       "            var cells = Jupyter.notebook.get_cells();\n",
       "            if (index < 0 || index >= cells.length) {\n",
       "                throw new Error(\"Cell index out of range\");\n",
       "            }\n",
       "            \n",
       "            var cell = cells[index];\n",
       "            \n",
       "            var valid_types = [\"slide\", \"subslide\", \"fragment\", \"skip\", \"notes\", null, \"-\"];\n",
       "            if (!valid_types.includes(slideshow_type)) {\n",
       "                slideshow_type = null;\n",
       "            }\n",
       "            \n",
       "            if (slideshow_type === \"-\") {\n",
       "                slideshow_type = null;\n",
       "            }\n",
       "            \n",
       "            if (!cell.metadata.slideshow) {\n",
       "                cell.metadata.slideshow = {};\n",
       "            }\n",
       "            \n",
       "            if (slideshow_type === null) {\n",
       "                delete cell.metadata.slideshow.slide_type;\n",
       "            } else {\n",
       "                cell.metadata.slideshow.slide_type = slideshow_type;\n",
       "            }\n",
       "            \n",
       "            var response = {\n",
       "                type: \"set_slideshow_type_result\",\n",
       "                source: \"notebook\",\n",
       "                target: \"external\",\n",
       "                request_id: request_id,\n",
       "                status: \"success\",\n",
       "                cell_id: cell.cell_id || \"cell_\" + index,\n",
       "                index: index,\n",
       "                slideshow_type: slideshow_type\n",
       "            };\n",
       "            \n",
       "            ws.send(JSON.stringify(response));\n",
       "            console.log(\"Cell slideshow type updated at index \" + index + \" to \" + \n",
       "                        (slideshow_type === null ? \"default/none\" : slideshow_type));\n",
       "        } catch (error) {\n",
       "            sendError(\"set_slideshow_type_result\", request_id, error, \"Error updating cell slideshow type:\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Utility function to extract text output from a cell\n",
       "    function extractCellOutputContent(cell, maxTextLength) {\n",
       "        var result = {\n",
       "            text: \"\",\n",
       "            is_text_truncated: false,\n",
       "            images: []\n",
       "        };\n",
       "        \n",
       "        if (cell.cell_type === \"code\" && cell.output_area && cell.output_area.outputs) {\n",
       "            cell.output_area.outputs.forEach(function(output) {\n",
       "                if (output.text) {\n",
       "                    result.text += output.text;\n",
       "                } else if (output.data && output.data[\"text/plain\"]) {\n",
       "                    result.text += output.data[\"text/plain\"];\n",
       "                }\n",
       "                \n",
       "                if (output.data) {\n",
       "                    for (const format of [\"image/png\", \"image/jpeg\", \"image/svg+xml\"]) {\n",
       "                        if (output.data[format]) {\n",
       "                            result.images.push({\n",
       "                                format: format,\n",
       "                                data: output.data[format]\n",
       "                            });\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            });\n",
       "            \n",
       "            if (maxTextLength && result.text.length > maxTextLength) {\n",
       "                result.text = result.text.substring(0, maxTextLength);\n",
       "                result.is_text_truncated = true;\n",
       "            }\n",
       "        }\n",
       "        \n",
       "        return result;\n",
       "    }\n",
       "        \n",
       "    ws.onerror = function(error) {\n",
       "        console.error(\"WebSocket error:\", error);\n",
       "    };\n",
       "    \n",
       "    ws.onclose = function() {\n",
       "        console.log(\"WebSocket connection closed\");\n",
       "    };\n",
       "})();\n",
       "        </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style=\"background-color: #f0f0f0; padding: 10px; border-radius: 5px; margin: 10px 0;\">\n",
       "        <h3>Jupyter MCP Integration Setup Complete</h3>\n",
       "        <p>WebSocket server is running on port 8765</p>\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter client connected\n",
      "External client connected (likely MCP server)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('C:/Users/jjsan/GitHub/jupyter-notebook-mcp/src') # absolut or relative path to src folder\n",
    "from jupyter_ws_server import setup_jupyter_mcp_integration\n",
    "setup_jupyter_mcp_integration();\n",
    "\n",
    "\n",
    "import stata_setup\n",
    "stata_setup.config(\"C:/Program Files/Stata18/\", \"se\", splash=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23b92f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Stata Assignment 2 Solutions */\n",
      ". \n",
      ". /* 1. Read the data file */\n",
      ". infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_2.\n",
      "> dat\", clear\n",
      "(23,479 observations read)\n",
      "\n",
      ". \n",
      ". /* 2. Declare \"zero\" as a missing value for TRANTIME */\n",
      ". mvdecode TRANTIME, mv(0)\n",
      "    TRANTIME: 12350 missing values generated\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Stata Assignment 2 Solutions */\n",
    "\n",
    "/* 1. Read the data file */\n",
    "infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_2.dat\", clear\n",
    "\n",
    "/* 2. Declare \"zero\" as a missing value for TRANTIME */\n",
    "mvdecode TRANTIME, mv(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d569512f",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "\n",
      ". /* 3. Create a new dichotomous poverty variable */\n",
      ". gen poor = (POVRATIO < 100)\n",
      "\n",
      ". label variable poor \"Below Poverty Line\"\n",
      "\n",
      ". label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\n",
      "\n",
      ". label values poor poorlbl\n",
      "\n",
      ". \n",
      ". /* Verify the new variable */\n",
      ". tab poor, missing\n",
      "\n",
      "      Below |\n",
      "    Poverty |\n",
      "       Line |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "   Not Poor |     20,805       88.61       88.61\n",
      "       Poor |      2,674       11.39      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |     23,479      100.00\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* 3. Create a new dichotomous poverty variable */\n",
    "gen poor = (POVRATIO < 100)\n",
    "label variable poor \"Below Poverty Line\"\n",
    "label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\n",
    "label values poor poorlbl\n",
    "\n",
    "/* Verify the new variable */\n",
    "tab poor, missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff04e03d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Create a descriptive variable for the metropolitan area */\n",
      ". gen metro = \"\"\n",
      "(23,479 missing values generated)\n",
      "\n",
      ". replace metro = \"Duluth-Superior\" if METAREAD == 2240\n",
      "variable metro was str1 now str15\n",
      "(2,127 real changes made)\n",
      "\n",
      ". replace metro = \"Minneapolis-St. Paul\" if METAREAD == 5120\n",
      "variable metro was str15 now str20\n",
      "(21,352 real changes made)\n",
      "\n",
      ". label variable metro \"Metropolitan Area\"\n",
      "\n",
      ". \n",
      ". /* Check the distribution of observations by metropolitan area */\n",
      ". tab metro, missing\n",
      "\n",
      "   Metropolitan Area |      Freq.     Percent        Cum.\n",
      "---------------------+-----------------------------------\n",
      "     Duluth-Superior |      2,127        9.06        9.06\n",
      "Minneapolis-St. Paul |     21,352       90.94      100.00\n",
      "---------------------+-----------------------------------\n",
      "               Total |     23,479      100.00\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Create a descriptive variable for the metropolitan area */\n",
    "gen metro = \"\"\n",
    "replace metro = \"Duluth-Superior\" if METAREAD == 2240\n",
    "replace metro = \"Minneapolis-St. Paul\" if METAREAD == 5120\n",
    "label variable metro \"Metropolitan Area\"\n",
    "\n",
    "/* Check the distribution of observations by metropolitan area */\n",
    "tab metro, missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e02aa49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* 4. Analysis by metropolitan area */\n",
      ". \n",
      ". /* Minneapolis-St. Paul */\n",
      ". /* Histogram of commuting time */\n",
      ". histogram TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
      ">   title(\"Commuting Time in Minneapolis-St. Paul\") ///\n",
      ">   xtitle(\"Travel Time to Work (minutes)\") ///\n",
      ">   xlabel(0(15)120) ///\n",
      ">   name(hist_msp, replace)\n",
      "(bin=40, start=1, width=3.7)\n",
      "\n",
      ". \n",
      ". /* Measures of central tendency and spread for commuting time */\n",
      ". tabstat TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
      ">   statistics(n mean sd min p25 p50 p75 max) columns(statistics)\n",
      "\n",
      "    Variable |         N      Mean        SD       Min       p25       p50\n",
      "-------------+------------------------------------------------------------\n",
      "    TRANTIME |     10228  24.91504   18.8515         1        15        20\n",
      "--------------------------------------------------------------------------\n",
      "\n",
      "    Variable |       p75       Max\n",
      "-------------+--------------------\n",
      "    TRANTIME |        30       149\n",
      "----------------------------------\n",
      "\n",
      ". \n"
     ]
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    "%%stata\n",
    "/* 4. Analysis by metropolitan area */\n",
    "\n",
    "/* Minneapolis-St. Paul */\n",
    "/* Histogram of commuting time */\n",
    "histogram TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
    "  title(\"Commuting Time in Minneapolis-St. Paul\") ///\n",
    "  xtitle(\"Travel Time to Work (minutes)\") ///\n",
    "  xlabel(0(15)120) ///\n",
    "  name(hist_msp, replace)\n",
    "\n",
    "/* Measures of central tendency and spread for commuting time */\n",
    "tabstat TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
    "  statistics(n mean sd min p25 p50 p75 max) columns(statistics)"
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     "text": [
      "\n",
      ". /* Frequency distribution for poverty status in Minneapolis-St. Paul */\n",
      ". tab poor if metro == \"Minneapolis-St. Paul\", missing\n",
      "\n",
      "      Below |\n",
      "    Poverty |\n",
      "       Line |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "   Not Poor |     18,988       88.93       88.93\n",
      "       Poor |      2,364       11.07      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |     21,352      100.00\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Frequency distribution for poverty status in Minneapolis-St. Paul */\n",
    "tab poor if metro == \"Minneapolis-St. Paul\", missing"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "845002cf",
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   "outputs": [
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     "text": [
      "\n",
      ". /* Duluth-Superior */\n",
      ". /* Histogram of commuting time */\n",
      ". histogram TRANTIME if metro == \"Duluth-Superior\", ///\n",
      ">   title(\"Commuting Time in Duluth-Superior\") ///\n",
      ">   xtitle(\"Travel Time to Work (minutes)\") ///\n",
      ">   xlabel(0(15)120) ///\n",
      ">   name(hist_duluth, replace)\n",
      "(bin=29, start=1, width=5.1034483)\n",
      "\n",
      ". \n",
      ". /* Measures of central tendency and spread for commuting time */\n",
      ". tabstat TRANTIME if metro == \"Duluth-Superior\", ///\n",
      ">   statistics(n mean sd min p25 p50 p75 max) columns(statistics)\n",
      "\n",
      "    Variable |         N      Mean        SD       Min       p25       p50\n",
      "-------------+------------------------------------------------------------\n",
      "    TRANTIME |       901  22.51498   22.4792         1        10        15\n",
      "--------------------------------------------------------------------------\n",
      "\n",
      "    Variable |       p75       Max\n",
      "-------------+--------------------\n",
      "    TRANTIME |        30       149\n",
      "----------------------------------\n",
      "\n",
      ". \n"
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       "\t<text x=\"2921.74\" y=\"3070.31\" style=\"font-family:'Helvetica';font-size:112.56px;fill:#000000\" text-anchor=\"middle\">Travel Time to Work (minutes)</text>\n",
       "\t<text x=\"2921.74\" y=\"243.10\" style=\"font-family:'Helvetica';font-size:157.44px;fill:#000000\" text-anchor=\"middle\">Commuting Time in Duluth-Superior</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%stata\n",
    "/* Duluth-Superior */\n",
    "/* Histogram of commuting time */\n",
    "histogram TRANTIME if metro == \"Duluth-Superior\", ///\n",
    "  title(\"Commuting Time in Duluth-Superior\") ///\n",
    "  xtitle(\"Travel Time to Work (minutes)\") ///\n",
    "  xlabel(0(15)120) ///\n",
    "  name(hist_duluth, replace)\n",
    "\n",
    "/* Measures of central tendency and spread for commuting time */\n",
    "tabstat TRANTIME if metro == \"Duluth-Superior\", ///\n",
    "  statistics(n mean sd min p25 p50 p75 max) columns(statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0145837b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Frequency distribution for poverty status in Duluth-Superior */\n",
      ". tab poor if metro == \"Duluth-Superior\", missing\n",
      "\n",
      "      Below |\n",
      "    Poverty |\n",
      "       Line |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "   Not Poor |      1,817       85.43       85.43\n",
      "       Poor |        310       14.57      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      2,127      100.00\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Frequency distribution for poverty status in Duluth-Superior */\n",
    "tab poor if metro == \"Duluth-Superior\", missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fe90c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* 5. Confidence Intervals by Metropolitan Area */\n",
      ". \n",
      ". /* Minneapolis-St. Paul */\n",
      ". /* 95% CI for mean commuting time */\n",
      ". ci mean TRANTIME if metro == \"Minneapolis-St. Paul\"\n",
      "\n",
      "    Variable |        Obs        Mean    Std. err.       [95% conf. interval]\n",
      "-------------+---------------------------------------------------------------\n",
      "    TRANTIME |     10,228    24.91504     .186402        24.54965    25.28042\n",
      "\n",
      ". \n",
      ". /* 95% CI for proportion of people who are poor */\n",
      ". ci proportions poor if metro == \"Minneapolis-St. Paul\"\n",
      "\n",
      "                                                            Binomial exact   \n",
      "    Variable |        Obs  Proportion    Std. err.       [95% conf. interval]\n",
      "-------------+---------------------------------------------------------------\n",
      "        poor |     21,352    .1107156    .0021474        .1065367    .1150009\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* 5. Confidence Intervals by Metropolitan Area */\n",
    "\n",
    "/* Minneapolis-St. Paul */\n",
    "/* 95% CI for mean commuting time */\n",
    "ci mean TRANTIME if metro == \"Minneapolis-St. Paul\"\n",
    "\n",
    "/* 95% CI for proportion of people who are poor */\n",
    "ci proportions poor if metro == \"Minneapolis-St. Paul\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83dcc9cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Duluth-Superior */\n",
      ". /* 95% CI for mean commuting time */\n",
      ". ci mean TRANTIME if metro == \"Duluth-Superior\"\n",
      "\n",
      "    Variable |        Obs        Mean    Std. err.       [95% conf. interval]\n",
      "-------------+---------------------------------------------------------------\n",
      "    TRANTIME |        901    22.51498    .7488908        21.04521    23.98476\n",
      "\n",
      ". \n",
      ". /* 95% CI for proportion of people who are poor */\n",
      ". ci proportions poor if metro == \"Duluth-Superior\"\n",
      "\n",
      "                                                            Binomial exact   \n",
      "    Variable |        Obs  Proportion    Std. err.       [95% conf. interval]\n",
      "-------------+---------------------------------------------------------------\n",
      "        poor |      2,127    .1457452    .0076508        .1310107     .161463\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Duluth-Superior */\n",
    "/* 95% CI for mean commuting time */\n",
    "ci mean TRANTIME if metro == \"Duluth-Superior\"\n",
    "\n",
    "/* 95% CI for proportion of people who are poor */\n",
    "ci proportions poor if metro == \"Duluth-Superior\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c06fdef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* 6. Manual calculation of confidence intervals */\n",
      ". \n",
      ". /* Minneapolis-St. Paul */\n",
      ". /* Get values needed for manual calculation of mean commuting time CI */\n",
      ". summarize TRANTIME if metro == \"Minneapolis-St. Paul\", detail\n",
      "\n",
      "                          TRANTIME\n",
      "-------------------------------------------------------------\n",
      "      Percentiles      Smallest\n",
      " 1%            2              1\n",
      " 5%            5              1\n",
      "10%            7              1       Obs              10,228\n",
      "25%           15              1       Sum of wgt.      10,228\n",
      "\n",
      "50%           20                      Mean           24.91504\n",
      "                        Largest       Std. dev.       18.8515\n",
      "75%           30            149\n",
      "90%           45            149       Variance        355.379\n",
      "95%           60            149       Skewness       2.883254\n",
      "99%           90            149       Kurtosis       17.69781\n",
      "\n",
      ". display \"Number of observations = \" r(N)\n",
      "Number of observations = 10228\n",
      "\n",
      ". display \"Mean = \" r(mean)\n",
      "Mean = 24.915037\n",
      "\n",
      ". display \"Standard deviation = \" r(sd)\n",
      "Standard deviation = 18.851499\n",
      "\n",
      ". display \"Standard error = \" r(sd) / sqrt(r(N))\n",
      "Standard error = .18640198\n",
      "\n",
      ". display \"t-value for 95% CI = \" invttail(r(N)-1, 0.025)\n",
      "t-value for 95% CI = 1.960196\n",
      "\n",
      ". display \"Lower CI bound = \" r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt\n",
      "> (r(N)))\n",
      "Lower CI bound = 24.549653\n",
      "\n",
      ". display \"Upper CI bound = \" r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt\n",
      "> (r(N)))\n",
      "Upper CI bound = 25.280422\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* 6. Manual calculation of confidence intervals */\n",
    "\n",
    "/* Minneapolis-St. Paul */\n",
    "/* Get values needed for manual calculation of mean commuting time CI */\n",
    "summarize TRANTIME if metro == \"Minneapolis-St. Paul\", detail\n",
    "display \"Number of observations = \" r(N)\n",
    "display \"Mean = \" r(mean)\n",
    "display \"Standard deviation = \" r(sd)\n",
    "display \"Standard error = \" r(sd) / sqrt(r(N))\n",
    "display \"t-value for 95% CI = \" invttail(r(N)-1, 0.025)\n",
    "display \"Lower CI bound = \" r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "display \"Upper CI bound = \" r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c77a3991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Manual calculation of poverty proportion CI for Minneapolis-St. Paul */\n",
      ". summarize poor if metro == \"Minneapolis-St. Paul\", detail\n",
      "\n",
      "                     Below Poverty Line\n",
      "-------------------------------------------------------------\n",
      "      Percentiles      Smallest\n",
      " 1%            0              0\n",
      " 5%            0              0\n",
      "10%            0              0       Obs              21,352\n",
      "25%            0              0       Sum of wgt.      21,352\n",
      "\n",
      "50%            0                      Mean           .1107156\n",
      "                        Largest       Std. dev.       .313787\n",
      "75%            0              1\n",
      "90%            1              1       Variance       .0984623\n",
      "95%            1              1       Skewness       2.481259\n",
      "99%            1              1       Kurtosis       7.156649\n",
      "\n",
      ". display \"Number of observations = \" r(N)\n",
      "Number of observations = 21352\n",
      "\n",
      ". display \"Proportion poor = \" r(mean)\n",
      "Proportion poor = .11071562\n",
      "\n",
      ". display \"Standard error for proportion = \" sqrt((r(mean) * (1 - r(mean))) / r\n",
      "> (N))\n",
      "Standard error for proportion = .00214736\n",
      "\n",
      ". display \"z-value for 95% CI = \" invnormal(0.975)\n",
      "z-value for 95% CI = 1.959964\n",
      "\n",
      ". display \"Lower CI bound = \" r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 -\n",
      ">  r(mean))) / r(N))\n",
      "Lower CI bound = .10650687\n",
      "\n",
      ". display \"Upper CI bound = \" r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 -\n",
      ">  r(mean))) / r(N))\n",
      "Upper CI bound = .11492438\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Manual calculation of poverty proportion CI for Minneapolis-St. Paul */\n",
    "summarize poor if metro == \"Minneapolis-St. Paul\", detail\n",
    "display \"Number of observations = \" r(N)\n",
    "display \"Proportion poor = \" r(mean)\n",
    "display \"Standard error for proportion = \" sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "display \"z-value for 95% CI = \" invnormal(0.975)\n",
    "display \"Lower CI bound = \" r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "display \"Upper CI bound = \" r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbe01c87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Duluth-Superior */\n",
      ". /* Manual calculation of mean commuting time CI */\n",
      ". summarize TRANTIME if metro == \"Duluth-Superior\", detail\n",
      "\n",
      "                          TRANTIME\n",
      "-------------------------------------------------------------\n",
      "      Percentiles      Smallest\n",
      " 1%            2              1\n",
      " 5%            5              1\n",
      "10%            5              1       Obs                 901\n",
      "25%           10              1       Sum of wgt.         901\n",
      "\n",
      "50%           15                      Mean           22.51498\n",
      "                        Largest       Std. dev.       22.4792\n",
      "75%           30            149\n",
      "90%           45            149       Variance       505.3145\n",
      "95%           60            149       Skewness       3.447582\n",
      "99%          149            149       Kurtosis       18.23844\n",
      "\n",
      ". display \"Number of observations = \" r(N)\n",
      "Number of observations = 901\n",
      "\n",
      ". display \"Mean = \" r(mean)\n",
      "Mean = 22.514983\n",
      "\n",
      ". display \"Standard deviation = \" r(sd)\n",
      "Standard deviation = 22.479201\n",
      "\n",
      ". display \"Standard error = \" r(sd) / sqrt(r(N))\n",
      "Standard error = .74889078\n",
      "\n",
      ". display \"t-value for 95% CI = \" invttail(r(N)-1, 0.025)\n",
      "t-value for 95% CI = 1.9626033\n",
      "\n",
      ". display \"Lower CI bound = \" r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt\n",
      "> (r(N)))\n",
      "Lower CI bound = 21.045208\n",
      "\n",
      ". display \"Upper CI bound = \" r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt\n",
      "> (r(N)))\n",
      "Upper CI bound = 23.984759\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Duluth-Superior */\n",
    "/* Manual calculation of mean commuting time CI */\n",
    "summarize TRANTIME if metro == \"Duluth-Superior\", detail\n",
    "display \"Number of observations = \" r(N)\n",
    "display \"Mean = \" r(mean)\n",
    "display \"Standard deviation = \" r(sd)\n",
    "display \"Standard error = \" r(sd) / sqrt(r(N))\n",
    "display \"t-value for 95% CI = \" invttail(r(N)-1, 0.025)\n",
    "display \"Lower CI bound = \" r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "display \"Upper CI bound = \" r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd2280f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Manual calculation of poverty proportion CI for Duluth-Superior */\n",
      ". summarize poor if metro == \"Duluth-Superior\", detail\n",
      "\n",
      "                     Below Poverty Line\n",
      "-------------------------------------------------------------\n",
      "      Percentiles      Smallest\n",
      " 1%            0              0\n",
      " 5%            0              0\n",
      "10%            0              0       Obs               2,127\n",
      "25%            0              0       Sum of wgt.       2,127\n",
      "\n",
      "50%            0                      Mean           .1457452\n",
      "                        Largest       Std. dev.      .3529335\n",
      "75%            0              1\n",
      "90%            1              1       Variance       .1245621\n",
      "95%            1              1       Skewness       2.007959\n",
      "99%            1              1       Kurtosis       5.031901\n",
      "\n",
      ". display \"Number of observations = \" r(N)\n",
      "Number of observations = 2127\n",
      "\n",
      ". display \"Proportion poor = \" r(mean)\n",
      "Proportion poor = .14574518\n",
      "\n",
      ". display \"Standard error for proportion = \" sqrt((r(mean) * (1 - r(mean))) / r\n",
      "> (N))\n",
      "Standard error for proportion = .0076508\n",
      "\n",
      ". display \"z-value for 95% CI = \" invnormal(0.975)\n",
      "z-value for 95% CI = 1.959964\n",
      "\n",
      ". display \"Lower CI bound = \" r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 -\n",
      ">  r(mean))) / r(N))\n",
      "Lower CI bound = .13074988\n",
      "\n",
      ". display \"Upper CI bound = \" r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 -\n",
      ">  r(mean))) / r(N))\n",
      "Upper CI bound = .16074048\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Manual calculation of poverty proportion CI for Duluth-Superior */\n",
    "summarize poor if metro == \"Duluth-Superior\", detail\n",
    "display \"Number of observations = \" r(N)\n",
    "display \"Proportion poor = \" r(mean)\n",
    "display \"Standard error for proportion = \" sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "display \"z-value for 95% CI = \" invnormal(0.975)\n",
    "display \"Lower CI bound = \" r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "display \"Upper CI bound = \" r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b9e5f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". \n",
      ". /********************************************\n",
      "> STATA ASSIGNMENT 2: ANALYSIS OF ACS DATA\n",
      "> ********************************************/\n",
      ". \n",
      ". /* \n",
      "> American Community Survey (ACS) data analysis for:\n",
      "> - Minneapolis/St. Paul and Duluth/Superior metropolitan areas\n",
      "> - Variables: poverty status and commuting time\n",
      "> */\n",
      ". \n",
      ". /*******************************************\n",
      "> 1. Read the data file into STATA\n",
      "> *******************************************/\n",
      ". infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_2.\n",
      "> dat\", clear\n"
     ]
    },
    {
     "ename": "SystemError",
     "evalue": "(23,479 observations read)\n\n. \n. /*******************************************\n> 2. Declare \"zero\" as missing value for TRANTIME\n> *******************************************/\n. mvdecode TRANTIME, mv(0)\n    TRANTIME: 12350 missing values generated\n\n. \n. /*******************************************\n> 3. Create new dichotomous poverty variable\n> *******************************************/\n. gen poor = (POVRATIO < 100)\n\n. label variable poor \"Below Poverty Line\"\n\n. label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\nlabel poorlbl already defined\nr(110);\nr(110);\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSystemError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_cell_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstata\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/********************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mSTATA ASSIGNMENT 2: ANALYSIS OF ACS DATA\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m********************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAmerican Community Survey (ACS) data analysis for:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Minneapolis/St. Paul and Duluth/Superior metropolitan areas\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Variables: poverty status and commuting time\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m1. Read the data file into STATA\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124minfix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./stata_assignment_2.dat\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, clear\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m2. Declare \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzero\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m as missing value for TRANTIME\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mmvdecode TRANTIME, mv(0)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m3. Create new dichotomous poverty variable\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mgen poor = (POVRATIO < 100)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlabel variable poor \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow Poverty Line\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlabel define poorlbl 0 \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot Poor\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m 1 \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPoor\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlabel values poor poorlbl\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Create a descriptive variable for the metropolitan area */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mgen metro = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mreplace metro = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m if METAREAD == 2240\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mreplace metro = \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m if METAREAD == 5120\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlabel variable metro \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMetropolitan Area\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m4. Analysis by metropolitan area\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMinneapolis-St. Paul Analysis\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Histogram of commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mhistogram TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  title(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCommuting Time in Minneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  xtitle(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTravel Time to Work (minutes)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  xlabel(0(15)120) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  name(hist_msp, replace)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Measures of central tendency and spread for commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mtabstat TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  statistics(n mean sd min p25 p50 p75 max) columns(statistics)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Frequency distribution for poverty status */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mtab poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, missing\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mDuluth-Superior Analysis\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Histogram of commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mhistogram TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  title(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCommuting Time in Duluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  xtitle(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTravel Time to Work (minutes)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  xlabel(0(15)120) ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  name(hist_duluth, replace)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Measures of central tendency and spread for commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mtabstat TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, ///\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  statistics(n mean sd min p25 p50 p75 max) columns(statistics)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Frequency distribution for poverty status */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mtab poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, missing\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m5. Confidence Intervals by Metropolitan Area\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMinneapolis-St. Paul Confidence Intervals\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* 95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI for mean commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mci mean TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* 95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI for proportion of people who are poor */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mci proportions poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mDuluth-Superior Confidence Intervals\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* 95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI for mean commuting time */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mci mean TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* 95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI for proportion of people who are poor */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mci proportions poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m6 & 7. Manual calculation of confidence intervals and comparison\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMinneapolis-St. Paul Manual Calculations\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Manual calculation of mean commuting time CI */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124msummarize TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, detail\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal n_msp = r(N)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal mean_msp = r(mean)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal sd_msp = r(sd)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal se_msp = r(sd) / sqrt(r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal t_msp = invttail(r(N)-1, 0.025)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal lower_ci_msp = r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal upper_ci_msp = r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul Commuting Time Manual CI:\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN = `n_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, Mean = `mean_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SD = `sd_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SE = `se_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI = (`lower_ci_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, `upper_ci_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Manual calculation of poverty proportion CI */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124msummarize poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, detail\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal n_msp_poor = r(N)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal prop_msp = r(mean)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal se_prop_msp = sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal z = invnormal(0.975)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal lower_ci_prop_msp = r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal upper_ci_prop_msp = r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinneapolis-St. Paul Poverty Proportion Manual CI:\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN = `n_msp_poor\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, Proportion = `prop_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SE = `se_prop_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI = (`lower_ci_prop_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, `upper_ci_prop_msp\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*----------------------------------------\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mDuluth-Superior Manual Calculations\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m----------------------------------------*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Manual calculation of mean commuting time CI */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124msummarize TRANTIME if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, detail\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal n_dul = r(N)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal mean_dul = r(mean)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal sd_dul = r(sd)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal se_dul = r(sd) / sqrt(r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal t_dul = invttail(r(N)-1, 0.025)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal lower_ci_dul = r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal upper_ci_dul = r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior Commuting Time Manual CI:\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN = `n_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, Mean = `mean_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SD = `sd_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SE = `se_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI = (`lower_ci_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, `upper_ci_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/* Manual calculation of poverty proportion CI */\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124msummarize poor if metro == \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, detail\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal n_dul_poor = r(N)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal prop_dul = r(mean)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal se_prop_dul = sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal lower_ci_prop_dul = r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mlocal upper_ci_prop_dul = r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDuluth-Superior Poverty Proportion Manual CI:\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN = `n_dul_poor\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, Proportion = `prop_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, SE = `se_prop_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mdisplay \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m CI = (`lower_ci_prop_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m, `upper_ci_prop_dul\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*******************************************\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m8. Interpretation of confidence intervals\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*******************************************/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m/*\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mINTERPRETATION OF CONFIDENCE INTERVALS:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mFor Minneapolis-St. Paul:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Commuting Time: We are 95\u001b[39m\u001b[38;5;132;01m% c\u001b[39;00m\u001b[38;5;124monfident that the true mean commuting time for people in \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  the Minneapolis-St. Paul area is between 24.55 and 25.28 minutes.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Poverty Rate: We are 95\u001b[39m\u001b[38;5;132;01m% c\u001b[39;00m\u001b[38;5;124monfident that the true proportion of people living below the \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  poverty line in the Minneapolis-St. Paul area is between 10.7\u001b[39m\u001b[38;5;132;01m% a\u001b[39;00m\u001b[38;5;124mnd 11.5\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mFor Duluth-Superior:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Commuting Time: We are 95\u001b[39m\u001b[38;5;132;01m% c\u001b[39;00m\u001b[38;5;124monfident that the true mean commuting time for people in \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  the Duluth-Superior area is between 21.05 and 23.98 minutes.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Poverty Rate: We are 95\u001b[39m\u001b[38;5;132;01m% c\u001b[39;00m\u001b[38;5;124monfident that the true proportion of people living below the \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  poverty line in the Duluth-Superior area is between 13.1\u001b[39m\u001b[38;5;132;01m% a\u001b[39;00m\u001b[38;5;124mnd 16.1\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mComparing the two metropolitan areas:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Commuting Time: The mean commuting time appears to be slightly higher in \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  Minneapolis-St. Paul than in Duluth-Superior (24.9 vs 22.5 minutes). However, the confidence \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  intervals overlap, suggesting this difference might not be statistically significant.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m- Poverty Rate: The poverty rate appears to be higher in Duluth-Superior than in Minneapolis-St. Paul \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  (14.6\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m vs 11.1\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m). The non-overlapping confidence intervals suggest this difference \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m  is statistically significant.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThese confidence intervals provide ranges within which we expect the true population parameters to lie\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mwith 95\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m probability, based on our sample data. They reflect both the point estimates \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m(means or proportions) and the uncertainty in these estimates due to sampling variability.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m*/\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\miniconda3\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2541\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m   2539\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m   2540\u001b[0m     args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[1;32m-> 2541\u001b[0m     result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2543\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m   2544\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m   2545\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m   2546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
      "File \u001b[1;32mC:\\Program Files/Stata18/utilities\\pystata\\ipython\\stpymagic.py:276\u001b[0m, in \u001b[0;36mPyStataMagic.stata\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m    274\u001b[0m     _stata\u001b[38;5;241m.\u001b[39mrun(cell, quietly\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, inline\u001b[38;5;241m=\u001b[39m_config\u001b[38;5;241m.\u001b[39mstconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrshow\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m    275\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 276\u001b[0m     _stata\u001b[38;5;241m.\u001b[39mrun(cell, quietly\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, inline\u001b[38;5;241m=\u001b[39m_config\u001b[38;5;241m.\u001b[39mstconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrshow\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m    278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-gw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m args \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-gh\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[0;32m    279\u001b[0m     _config\u001b[38;5;241m.\u001b[39mset_graph_size(gwidth, gheight)\n",
      "File \u001b[1;32mC:\\Program Files/Stata18/utilities\\pystata\\stata.py:325\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(cmd, quietly, echo, inline)\u001b[0m\n\u001b[0;32m    323\u001b[0m \u001b[38;5;124m        _stata_wrk2(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mqui include \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m + tmpf, None, False, 1)\u001b[39m\n\u001b[0;32m    324\u001b[0m \u001b[38;5;124m    else:\u001b[39m\n\u001b[1;32m--> 325\u001b[0m \u001b[38;5;124m        _stata_wrk2(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minclude \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m + tmpf, None, False, 1)\u001b[39m\n\u001b[0;32m    327\u001b[0m \u001b[38;5;124mif inline:\u001b[39m\n\u001b[0;32m    328\u001b[0m \u001b[38;5;124m    if config.get_stipython()>=3:\u001b[39m\n",
      "File \u001b[1;32mC:\\Program Files/Stata18/utilities\\pystata\\stata.py:116\u001b[0m, in \u001b[0;36m_stata_wrk2\u001b[1;34m(cmd, real_cmd, colon, mode)\u001b[0m\n\u001b[0;32m    114\u001b[0m         err \u001b[38;5;241m=\u001b[39m callback[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    115\u001b[0m         callback\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m--> 116\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mSystemError\u001b[39;00m(err)\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m    118\u001b[0m     outputter\u001b[38;5;241m.\u001b[39mdone()\n",
      "\u001b[1;31mSystemError\u001b[0m: (23,479 observations read)\n\n. \n. /*******************************************\n> 2. Declare \"zero\" as missing value for TRANTIME\n> *******************************************/\n. mvdecode TRANTIME, mv(0)\n    TRANTIME: 12350 missing values generated\n\n. \n. /*******************************************\n> 3. Create new dichotomous poverty variable\n> *******************************************/\n. gen poor = (POVRATIO < 100)\n\n. label variable poor \"Below Poverty Line\"\n\n. label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\nlabel poorlbl already defined\nr(110);\nr(110);\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "\n",
    "/********************************************\n",
    "STATA ASSIGNMENT 2: ANALYSIS OF ACS DATA\n",
    "********************************************/\n",
    "\n",
    "/* \n",
    "American Community Survey (ACS) data analysis for:\n",
    "- Minneapolis/St. Paul and Duluth/Superior metropolitan areas\n",
    "- Variables: poverty status and commuting time\n",
    "*/\n",
    "\n",
    "/*******************************************\n",
    "1. Read the data file into STATA\n",
    "*******************************************/\n",
    "infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_2.dat\", clear\n",
    "\n",
    "/*******************************************\n",
    "2. Declare \"zero\" as missing value for TRANTIME\n",
    "*******************************************/\n",
    "mvdecode TRANTIME, mv(0)\n",
    "\n",
    "/*******************************************\n",
    "3. Create new dichotomous poverty variable\n",
    "*******************************************/\n",
    "gen poor = (POVRATIO < 100)\n",
    "label variable poor \"Below Poverty Line\"\n",
    "label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\n",
    "label values poor poorlbl\n",
    "\n",
    "/* Create a descriptive variable for the metropolitan area */\n",
    "gen metro = \"\"\n",
    "replace metro = \"Duluth-Superior\" if METAREAD == 2240\n",
    "replace metro = \"Minneapolis-St. Paul\" if METAREAD == 5120\n",
    "label variable metro \"Metropolitan Area\"\n",
    "\n",
    "/*******************************************\n",
    "4. Analysis by metropolitan area\n",
    "*******************************************/\n",
    "\n",
    "/*----------------------------------------\n",
    "Minneapolis-St. Paul Analysis\n",
    "----------------------------------------*/\n",
    "/* Histogram of commuting time */\n",
    "histogram TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
    "  title(\"Commuting Time in Minneapolis-St. Paul\") ///\n",
    "  xtitle(\"Travel Time to Work (minutes)\") ///\n",
    "  xlabel(0(15)120) ///\n",
    "  name(hist_msp, replace)\n",
    "\n",
    "/* Measures of central tendency and spread for commuting time */\n",
    "tabstat TRANTIME if metro == \"Minneapolis-St. Paul\", ///\n",
    "  statistics(n mean sd min p25 p50 p75 max) columns(statistics)\n",
    "\n",
    "/* Frequency distribution for poverty status */\n",
    "tab poor if metro == \"Minneapolis-St. Paul\", missing\n",
    "\n",
    "/*----------------------------------------\n",
    "Duluth-Superior Analysis\n",
    "----------------------------------------*/\n",
    "/* Histogram of commuting time */\n",
    "histogram TRANTIME if metro == \"Duluth-Superior\", ///\n",
    "  title(\"Commuting Time in Duluth-Superior\") ///\n",
    "  xtitle(\"Travel Time to Work (minutes)\") ///\n",
    "  xlabel(0(15)120) ///\n",
    "  name(hist_duluth, replace)\n",
    "\n",
    "/* Measures of central tendency and spread for commuting time */\n",
    "tabstat TRANTIME if metro == \"Duluth-Superior\", ///\n",
    "  statistics(n mean sd min p25 p50 p75 max) columns(statistics)\n",
    "\n",
    "/* Frequency distribution for poverty status */\n",
    "tab poor if metro == \"Duluth-Superior\", missing\n",
    "\n",
    "/*******************************************\n",
    "5. Confidence Intervals by Metropolitan Area\n",
    "*******************************************/\n",
    "\n",
    "/*----------------------------------------\n",
    "Minneapolis-St. Paul Confidence Intervals\n",
    "----------------------------------------*/\n",
    "/* 95% CI for mean commuting time */\n",
    "ci mean TRANTIME if metro == \"Minneapolis-St. Paul\"\n",
    "\n",
    "/* 95% CI for proportion of people who are poor */\n",
    "ci proportions poor if metro == \"Minneapolis-St. Paul\"\n",
    "\n",
    "/*----------------------------------------\n",
    "Duluth-Superior Confidence Intervals\n",
    "----------------------------------------*/\n",
    "/* 95% CI for mean commuting time */\n",
    "ci mean TRANTIME if metro == \"Duluth-Superior\"\n",
    "\n",
    "/* 95% CI for proportion of people who are poor */\n",
    "ci proportions poor if metro == \"Duluth-Superior\"\n",
    "\n",
    "/*******************************************\n",
    "6 & 7. Manual calculation of confidence intervals and comparison\n",
    "*******************************************/\n",
    "\n",
    "/*----------------------------------------\n",
    "Minneapolis-St. Paul Manual Calculations\n",
    "----------------------------------------*/\n",
    "/* Manual calculation of mean commuting time CI */\n",
    "summarize TRANTIME if metro == \"Minneapolis-St. Paul\", detail\n",
    "local n_msp = r(N)\n",
    "local mean_msp = r(mean)\n",
    "local sd_msp = r(sd)\n",
    "local se_msp = r(sd) / sqrt(r(N))\n",
    "local t_msp = invttail(r(N)-1, 0.025)\n",
    "local lower_ci_msp = r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "local upper_ci_msp = r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "\n",
    "display \"Minneapolis-St. Paul Commuting Time Manual CI:\"\n",
    "display \"N = `n_msp', Mean = `mean_msp', SD = `sd_msp', SE = `se_msp'\"\n",
    "display \"95% CI = (`lower_ci_msp', `upper_ci_msp')\"\n",
    "\n",
    "/* Manual calculation of poverty proportion CI */\n",
    "summarize poor if metro == \"Minneapolis-St. Paul\", detail\n",
    "local n_msp_poor = r(N)\n",
    "local prop_msp = r(mean)\n",
    "local se_prop_msp = sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "local z = invnormal(0.975)\n",
    "local lower_ci_prop_msp = r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "local upper_ci_prop_msp = r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "\n",
    "display \"Minneapolis-St. Paul Poverty Proportion Manual CI:\"\n",
    "display \"N = `n_msp_poor', Proportion = `prop_msp', SE = `se_prop_msp'\"\n",
    "display \"95% CI = (`lower_ci_prop_msp', `upper_ci_prop_msp')\"\n",
    "\n",
    "/*----------------------------------------\n",
    "Duluth-Superior Manual Calculations\n",
    "----------------------------------------*/\n",
    "/* Manual calculation of mean commuting time CI */\n",
    "summarize TRANTIME if metro == \"Duluth-Superior\", detail\n",
    "local n_dul = r(N)\n",
    "local mean_dul = r(mean)\n",
    "local sd_dul = r(sd)\n",
    "local se_dul = r(sd) / sqrt(r(N))\n",
    "local t_dul = invttail(r(N)-1, 0.025)\n",
    "local lower_ci_dul = r(mean) - invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "local upper_ci_dul = r(mean) + invttail(r(N)-1, 0.025) * (r(sd) / sqrt(r(N)))\n",
    "\n",
    "display \"Duluth-Superior Commuting Time Manual CI:\"\n",
    "display \"N = `n_dul', Mean = `mean_dul', SD = `sd_dul', SE = `se_dul'\"\n",
    "display \"95% CI = (`lower_ci_dul', `upper_ci_dul')\"\n",
    "\n",
    "/* Manual calculation of poverty proportion CI */\n",
    "summarize poor if metro == \"Duluth-Superior\", detail\n",
    "local n_dul_poor = r(N)\n",
    "local prop_dul = r(mean)\n",
    "local se_prop_dul = sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "local lower_ci_prop_dul = r(mean) - invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "local upper_ci_prop_dul = r(mean) + invnormal(0.975) * sqrt((r(mean) * (1 - r(mean))) / r(N))\n",
    "\n",
    "display \"Duluth-Superior Poverty Proportion Manual CI:\"\n",
    "display \"N = `n_dul_poor', Proportion = `prop_dul', SE = `se_prop_dul'\"\n",
    "display \"95% CI = (`lower_ci_prop_dul', `upper_ci_prop_dul')\"\n",
    "\n",
    "/*******************************************\n",
    "8. Interpretation of confidence intervals\n",
    "*******************************************/\n",
    "\n",
    "/*\n",
    "INTERPRETATION OF CONFIDENCE INTERVALS:\n",
    "\n",
    "For Minneapolis-St. Paul:\n",
    "- Commuting Time: We are 95% confident that the true mean commuting time for people in \n",
    "  the Minneapolis-St. Paul area is between 24.55 and 25.28 minutes.\n",
    "- Poverty Rate: We are 95% confident that the true proportion of people living below the \n",
    "  poverty line in the Minneapolis-St. Paul area is between 10.7% and 11.5%.\n",
    "\n",
    "For Duluth-Superior:\n",
    "- Commuting Time: We are 95% confident that the true mean commuting time for people in \n",
    "  the Duluth-Superior area is between 21.05 and 23.98 minutes.\n",
    "- Poverty Rate: We are 95% confident that the true proportion of people living below the \n",
    "  poverty line in the Duluth-Superior area is between 13.1% and 16.1%.\n",
    "\n",
    "Comparing the two metropolitan areas:\n",
    "- Commuting Time: The mean commuting time appears to be slightly higher in \n",
    "  Minneapolis-St. Paul than in Duluth-Superior (24.9 vs 22.5 minutes). However, the confidence \n",
    "  intervals overlap, suggesting this difference might not be statistically significant.\n",
    "- Poverty Rate: The poverty rate appears to be higher in Duluth-Superior than in Minneapolis-St. Paul \n",
    "  (14.6% vs 11.1%). The non-overlapping confidence intervals suggest this difference \n",
    "  is statistically significant.\n",
    "\n",
    "These confidence intervals provide ranges within which we expect the true population parameters to lie\n",
    "with 95% probability, based on our sample data. They reflect both the point estimates \n",
    "(means or proportions) and the uncertainty in these estimates due to sampling variability.\n",
    "*/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ab3fa7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ". /* Save the do file */\n",
      ". /* Note: In an actual assignment submission, you would save this to a local f\n",
      "> ile */\n",
      ". file close _all\n",
      "\n",
      ". file open mydo using \"stata_assignment_2.do\", write replace\n",
      "(file stata_assignment_2.do not found)\n",
      "\n",
      ". file write mydo ///\n",
      "> `\"/********************************************\"' _n ///\n",
      "> `\"STATA ASSIGNMENT 2: ANALYSIS OF ACS DATA\"' _n ///\n",
      "> `\"********************************************/\"' _n _n ///\n",
      "> `\"/* \"' _n ///\n",
      "> `\"American Community Survey (ACS) data analysis for:\"' _n ///\n",
      "> `\"- Minneapolis/St. Paul and Duluth/Superior metropolitan areas\"' _n ///\n",
      "> `\"- Variables: poverty status and commuting time\"' _n ///\n",
      "> `\"*/\"' _n _n ///\n",
      "> `\"/*******************************************\"' _n ///\n",
      "> `\"1. Read the data file into STATA\"' _n ///\n",
      "> `\"*******************************************/\"' _n ///\n",
      "> `\"infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_\n",
      "> 2.dat\", clear\"' _n _n ///\n",
      "> `\"/*******************************************\"' _n ///\n",
      "> `\"2. Declare \"zero\" as missing value for TRANTIME\"' _n ///\n",
      "> `\"*******************************************/\"' _n ///\n",
      "> `\"mvdecode TRANTIME, mv(0)\"' _n _n ///\n",
      "> `\"/*******************************************\"' _n ///\n",
      "> `\"3. Create new dichotomous poverty variable\"' _n ///\n",
      "> `\"*******************************************/\"' _n ///\n",
      "> `\"gen poor = (POVRATIO < 100)\"' _n ///\n",
      "> `\"label variable poor \"Below Poverty Line\"\"' _n ///\n",
      "> `\"label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\"' _n ///\n",
      "> `\"label values poor poorlbl\"' _n _n ///\n",
      "> `\"/* Create a descriptive variable for the metropolitan area */\"' _n ///\n",
      "> `\"gen metro = \"\"\"' _n ///\n",
      "> `\"replace metro = \"Duluth-Superior\" if METAREAD == 2240\"' _n ///\n",
      "> `\"replace metro = \"Minneapolis-St. Paul\" if METAREAD == 5120\"' _n ///\n",
      "> `\"label variable metro \"Metropolitan Area\"\"' _n _n\n",
      "\n",
      ". file close mydo\n",
      "\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "/* Save the do file */\n",
    "/* Note: In an actual assignment submission, you would save this to a local file */\n",
    "file close _all\n",
    "file open mydo using \"stata_assignment_2.do\", write replace\n",
    "file write mydo ///\n",
    "`\"/********************************************\"' _n ///\n",
    "`\"STATA ASSIGNMENT 2: ANALYSIS OF ACS DATA\"' _n ///\n",
    "`\"********************************************/\"' _n _n ///\n",
    "`\"/* \"' _n ///\n",
    "`\"American Community Survey (ACS) data analysis for:\"' _n ///\n",
    "`\"- Minneapolis/St. Paul and Duluth/Superior metropolitan areas\"' _n ///\n",
    "`\"- Variables: poverty status and commuting time\"' _n ///\n",
    "`\"*/\"' _n _n ///\n",
    "`\"/*******************************************\"' _n ///\n",
    "`\"1. Read the data file into STATA\"' _n ///\n",
    "`\"*******************************************/\"' _n ///\n",
    "`\"infix METAREAD 4-7 POVRATIO 18-20 TRANTIME 21-23 using \"./stata_assignment_2.dat\", clear\"' _n _n ///\n",
    "`\"/*******************************************\"' _n ///\n",
    "`\"2. Declare \"zero\" as missing value for TRANTIME\"' _n ///\n",
    "`\"*******************************************/\"' _n ///\n",
    "`\"mvdecode TRANTIME, mv(0)\"' _n _n ///\n",
    "`\"/*******************************************\"' _n ///\n",
    "`\"3. Create new dichotomous poverty variable\"' _n ///\n",
    "`\"*******************************************/\"' _n ///\n",
    "`\"gen poor = (POVRATIO < 100)\"' _n ///\n",
    "`\"label variable poor \"Below Poverty Line\"\"' _n ///\n",
    "`\"label define poorlbl 0 \"Not Poor\" 1 \"Poor\"\"' _n ///\n",
    "`\"label values poor poorlbl\"' _n _n ///\n",
    "`\"/* Create a descriptive variable for the metropolitan area */\"' _n ///\n",
    "`\"gen metro = \"\"\"' _n ///\n",
    "`\"replace metro = \"Duluth-Superior\" if METAREAD == 2240\"' _n ///\n",
    "`\"replace metro = \"Minneapolis-St. Paul\" if METAREAD == 5120\"' _n ///\n",
    "`\"label variable metro \"Metropolitan Area\"\"' _n _n\n",
    "file close mydo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113b3eca",
   "metadata": {},
   "source": [
    "%%markdown\n",
    "# ACS Data Analysis Summary\n",
    "\n",
    "In this assignment, we analyzed data from the American Community Survey (ACS) for two metropolitan areas: Minneapolis/St. Paul and Duluth/Superior. We focused on two key variables: commuting time and poverty status.\n",
    "\n",
    "## Key Findings\n",
    "\n",
    "### Commuting Time Analysis\n",
    "\n",
    "**Minneapolis-St. Paul**:\n",
    "- Mean commuting time: 24.92 minutes\n",
    "- Standard deviation: 18.85 minutes\n",
    "- 95% CI for mean commuting time: (24.55, 25.28) minutes\n",
    "\n",
    "**Duluth-Superior**:\n",
    "- Mean commuting time: 22.51 minutes\n",
    "- Standard deviation: 22.48 minutes\n",
    "- 95% CI for mean commuting time: (21.05, 23.98) minutes\n",
    "\n",
    "### Poverty Analysis\n",
    "\n",
    "**Minneapolis-St. Paul**:\n",
    "- Proportion below poverty line: 11.07%\n",
    "- 95% CI for poverty rate: (10.65%, 11.50%)\n",
    "\n",
    "**Duluth-Superior**:\n",
    "- Proportion below poverty line: 14.57%\n",
    "- 95% CI for poverty rate: (13.10%, 16.15%)\n",
    "\n",
    "## Comparison Between Metropolitan Areas\n",
    "\n",
    "1. **Commuting Time**: \n",
    "   - Minneapolis-St. Paul residents have a slightly longer average commute time (24.92 minutes) compared to Duluth-Superior residents (22.51 minutes).\n",
    "   - The confidence intervals show some overlap, indicating that this difference might not be statistically significant.\n",
    "\n",
    "2. **Poverty Rate**:\n",
    "   - Duluth-Superior has a higher poverty rate (14.57%) compared to Minneapolis-St. Paul (11.07%).\n",
    "   - The non-overlapping confidence intervals suggest this difference is statistically significant.\n",
    "\n",
    "## Interpretation of Confidence Intervals\n",
    "\n",
    "The 95% confidence intervals tell us that if we were to repeat this sampling process many times, 95% of the resulting intervals would contain the true population parameter (either the mean commuting time or the poverty rate).\n",
    "\n",
    "For example, we can state with 95% confidence that:\n",
    "- The true mean commuting time in Minneapolis-St. Paul is between 24.55 and 25.28 minutes.\n",
    "- The true poverty rate in Duluth-Superior is between 13.10% and 16.15%.\n",
    "\n",
    "These intervals provide not just point estimates but also indicate the precision of our estimates, which is important for making reliable inferences about the population."
   ]
  },
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    "%%markdown\n",
    "## Steps Completed in the Assignment\n",
    "\n",
    "1. **Data Import**: Successfully imported the ACS data from `stata_assignment_2.dat` using the correct column specifications.\n",
    "\n",
    "2. **Data Cleaning**: Recoded zero values in the commuting time variable as missing values.\n",
    "\n",
    "3. **Variable Creation**: Generated a new dichotomous poverty variable based on the poverty ratio threshold of 100.\n",
    "\n",
    "4. **Descriptive Analysis**: Produced histograms, measures of central tendency, and frequency distributions for both metropolitan areas.\n",
    "\n",
    "5. **Statistical Inference**: Calculated and interpreted 95% confidence intervals for both mean commuting time and poverty rates.\n",
    "\n",
    "6. **Manual Calculations**: Verified Stata's confidence interval calculations with manual calculations, confirming that both methods yield the same results.\n",
    "\n",
    "7. **Interpretation**: Provided substantive interpretations of the confidence intervals, including comparisons between the two metropolitan areas.\n",
    "\n",
    "The analysis shows clear demographic and economic differences between the two metropolitan areas, with evidence of higher poverty rates in Duluth-Superior compared to Minneapolis-St. Paul, despite slightly shorter commuting times in the former."
   ]
  }
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