generated from mwc/project_argument
367 lines
30 KiB
Plaintext
367 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"Argument Project Research"
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"## Overarching Question: \n",
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"I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.).\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"sns.set_theme()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [],
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"source": [
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"file_name = \"brfss_2020_cleaned.csv\"\n",
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"dataset_path = \"data/brfss_2020_cleaned.csv\"\n",
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"people = pd.read_csv(\"data/brfss_2020_cleaned.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: xlabel='age', ylabel='weight'>"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sns.barplot(data=people, x=\"age\", y=\"weight\", hue=\"age\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "continental-franklin",
|
|
"metadata": {},
|
|
"source": [
|
|
"I'm trying to find out at what age, on average, do people experience a dramatic\n",
|
|
"weight gain or loss, if at all.\n",
|
|
"I'm curious to find out if such a dramatic increase or decrease in weight can\n",
|
|
"be captured in a one-time snapshot database, where individuals are NOT tracked \n",
|
|
"over a period of time, but ONLY once."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "infinite-instrument",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Methods and Results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "recognized-positive",
|
|
"metadata": {
|
|
"jp-MarkdownHeadingCollapsed": true
|
|
},
|
|
"source": [
|
|
"Since this chart of this dataset shows that an age 65+ person, on average, is closer in weight to an age 18 to 30 person, does that mean he or she is as healthy?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "graduate-palmer",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Methods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "endless-variation",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Explain how you will approach this research question below. Consider the following:* \n",
|
|
" - *Which aspects of the dataset will you use?* \n",
|
|
" - *How will you reorganize/store the data?* \n",
|
|
" - *What data science tools/functions will you use and why?* \n",
|
|
" \n",
|
|
"✏️ *Write your answer below:*\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "portuguese-japan",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Results "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "negative-highlight",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#######################################################################\n",
|
|
"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
|
|
"### \n",
|
|
"### Your data analysis may include a statistic and/or a data visualization\n",
|
|
"#######################################################################"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "victorian-burning",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "collectible-puppy",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "demographic-future",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Methods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "incorporate-roller",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Explain how you will approach this research question below. Consider the following:* \n",
|
|
" - *Which aspects of the dataset will you use?* \n",
|
|
" - *How will you reorganize/store the data?* \n",
|
|
" - *What data science tools/functions will you use and why?* \n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "juvenile-creation",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Results "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "pursuant-surrey",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#######################################################################\n",
|
|
"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
|
|
"###\n",
|
|
"### Your data analysis may include a statistic and/or a data visualization\n",
|
|
"#######################################################################"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "located-night",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "infectious-symbol",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Discussion"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "furnished-camping",
|
|
"metadata": {
|
|
"code_folding": []
|
|
},
|
|
"source": [
|
|
"## Considerations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bearing-stadium",
|
|
"metadata": {},
|
|
"source": [
|
|
"*It's important to recognize the limitations of our research.\n",
|
|
"Consider the following:*\n",
|
|
"\n",
|
|
"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
|
|
"- *What were limitations of your datset?*\n",
|
|
"- *Are there any known biases in the data?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
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"id": "beneficial-invasion",
|
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"metadata": {},
|
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"source": [
|
|
"## Summary"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "about-raise",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Summarize what you discovered through the research. Consider the following:*\n",
|
|
"\n",
|
|
"- *What did you learn about your media consumption/digital habits?*\n",
|
|
"- *Did the results make sense?*\n",
|
|
"- *What was most surprising?*\n",
|
|
"- *How will this project impact you going forward?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
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"jupytext": {
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"cell_metadata_json": true,
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"text_representation": {
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"format_name": "rmarkdown",
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"format_version": "1.2",
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"jupytext_version": "1.9.1"
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}
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": false,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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"varInspector": {
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"cols": {
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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