project (phase 1) final-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Does degree you obtained affect on number of kids you have?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1)what's your research question?\n",
    "\n",
    "does the number of degrees you obtained correspond to the number of childeren you have"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2) Include a citation for your data, and if your data set is online, provide a link to the source\n",
    "\n",
    "I will work on data collected from GSS dataset which is a sociological survey made in US on demographic characterstics of residents in US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3)how the data were collected?\n",
    "as we use the GSS dataset which is a survey used to collect data on demographic characteristics  of residents in USA, there are 57.061 cases and 114 varaibles as data surveyed from  1972-2012, data collected by asking participants some questions and collecting thier answers, questions like:- what's the degree you have obtained? and the answers vary between(\"less than high school\",\"high school\",\"junior college\",\"Bachelor\",\"Graduate\") and asking again about the number of children they have,The target population of the GSS is adults (18+) living in households in the United States. The GSS sample is drawn using an area probability design that randomly selects respondents in households across the United States to take part in the survey. Respondents that become part of the GSS sample are from a mix of urban, suburban, and rural geographic areas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q4)What are the cases? \n",
    "the cases are the people(participants) who have been surveyed by GSS organization which is 57,061 but for more specified(accurate) analysis we will work on data with specific year (year 1980)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q5)What are the two variables you will be studying to answer the research you stated earlier? State the type of each variable\n",
    "1- degrees they have obtained (categorical variable)[expanatory variable]\n",
    "\n",
    "2- number of childern they have (numerical variable)[Response variable]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q6) What is the type of study? Is it an observational study or an experiment? Explain how you've arrived at your conclusion using information on the sampling and/or experimental design.\n",
    "\n",
    "it's observitonal study as the survey is done by asking questions and collecting answers(Data) only we didn't divide the sample into two groups or applying any treatment or method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q7)Identify the population of interest, and whether the findings from this analysis can be generalized to that population, or, if not, a subsection of that population. Explain why or why not. Also discuss any potential sources of bias that might prevent generalizability.\n",
    "\n",
    "population of intereset are the people in USA(married people).\n",
    "\n",
    "As the data collected from random sample of USA population, it could be generalized to the whole population\n",
    "\n",
    "potential sources of bias:    \n",
    "\n",
    "1-as refered in GSS survery not all cases respondent to the survey in all years.\n",
    "\n",
    "2- some of data is missing as some participants are not willing to take the survey which may cause biasing. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q8)Can these data be used to establish causal links between the variables of interest? Explain why or why not\n",
    "\n",
    "As we said that it's observtional study then there's a correlation between the variables not causation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expolratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1)import our dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "data=pd.read_csv(\"F:/biostatistics project/dataset/gss123.csv\",low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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       "       caseid  year   age     sex   race  \\\n",
       "0           1  1972  23.0  Female  White   \n",
       "1           2  1972  70.0    Male  White   \n",
       "2           3  1972  48.0  Female  White   \n",
       "3           4  1972  27.0  Female  White   \n",
       "4           5  1972  61.0  Female  White   \n",
       "...       ...   ...   ...     ...    ...   \n",
       "57056   57057  2012  61.0  Female  Other   \n",
       "57057   57058  2012  53.0  Female  Other   \n",
       "57058   57059  2012  48.0    Male  White   \n",
       "57059   57060  2012  37.0  Female  Other   \n",
       "57060   57061  2012  22.0    Male  Other   \n",
       "\n",
       "                                   hispanic uscitzn  educ  paeduc  maeduc  \\\n",
       "0                                       NaN     NaN  16.0    10.0     NaN   \n",
       "1                                       NaN     NaN  10.0     8.0     8.0   \n",
       "2                                       NaN     NaN  12.0     8.0     8.0   \n",
       "3                                       NaN     NaN  17.0    16.0    12.0   \n",
       "4                                       NaN     NaN  12.0     8.0     8.0   \n",
       "...                                     ...     ...   ...     ...     ...   \n",
       "57056                          Not Hispanic     NaN  16.0    12.0    12.0   \n",
       "57057                          Not Hispanic     NaN  13.0     NaN    12.0   \n",
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       "57060                          Not Hispanic     NaN  12.0    10.0    12.0   \n",
       "\n",
       "       ...               news tvhours racdif1  racdif2 racdif3 racdif4  \\\n",
       "0      ...           Everyday     NaN     NaN      NaN     NaN     NaN   \n",
       "1      ...           Everyday     NaN     NaN      NaN     NaN     NaN   \n",
       "2      ...           Everyday     NaN     NaN      NaN     NaN     NaN   \n",
       "3      ...        Once A Week     NaN     NaN      NaN     NaN     NaN   \n",
       "4      ...           Everyday     NaN     NaN      NaN     NaN     NaN   \n",
       "...    ...                ...     ...     ...      ...     ...     ...   \n",
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       "57060  ...                NaN     NaN     NaN      NaN     NaN     NaN   \n",
       "\n",
       "              helppoor             helpnot          helpsick  \\\n",
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       "3                  NaN                 NaN               NaN   \n",
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       "...                ...                 ...               ...   \n",
       "57056  Agree With Both  Govt Does Too Much  Govt Should Help   \n",
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       "\n",
       "                    helpblk  \n",
       "0                       NaN  \n",
       "1                       NaN  \n",
       "2                       NaN  \n",
       "3                       NaN  \n",
       "4                       NaN  \n",
       "...                     ...  \n",
       "57056  No Special Treatment  \n",
       "57057  No Special Treatment  \n",
       "57058                   NaN  \n",
       "57059                   NaN  \n",
       "57060                   NaN  \n",
       "\n",
       "[57061 rows x 114 columns]"
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data[data.year==1980] ##working on data with certain year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ourdata=data[[\"degree\",\"childs\"]]"
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       "      <th>12116</th>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12117</th>\n",
       "      <td>High School</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12118</th>\n",
       "      <td>Junior College</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12119</th>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               degree  childs\n",
       "10652  Lt High School     0.0\n",
       "10653  Lt High School     4.0\n",
       "10654  Lt High School     5.0\n",
       "10655     High School     1.0\n",
       "10656     High School     2.0\n",
       "...               ...     ...\n",
       "12115     High School     1.0\n",
       "12116     High School     0.0\n",
       "12117     High School     3.0\n",
       "12118  Junior College     0.0\n",
       "12119     High School     0.0\n",
       "\n",
       "[1468 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ourdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2)understand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>childs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.030717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.878411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            childs\n",
       "count  1465.000000\n",
       "mean      2.030717\n",
       "std       1.878411\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       2.000000\n",
       "75%       3.000000\n",
       "max       8.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ourdata.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as we see number of children vary from 0 child to 3 childs with median 2 childs and we have 8 childs as oultier and from these numbers we can conclude that the data is right skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) cleaninng data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "isnull=pd.isnull(ourdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degree</th>\n",
       "      <th>childs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10652</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10653</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10654</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10655</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10656</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12115</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12116</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12117</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12118</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12119</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       degree  childs\n",
       "10652   False   False\n",
       "10653   False   False\n",
       "10654   False   False\n",
       "10655   False   False\n",
       "10656   False   False\n",
       "...       ...     ...\n",
       "12115   False   False\n",
       "12116   False   False\n",
       "12117   False   False\n",
       "12118   False   False\n",
       "12119   False   False\n",
       "\n",
       "[1468 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isnull\n",
    "##there is not NAN in our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4)visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ccc7883520>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"degree\",y=\"childs\",data=ourdata,order=[\"Lt High School\",\"High School\",\"Junior College\",\"Bachelor\",\"Graduate\"],showmeans=True,meanprops={\"marker\":\"+\",\"markeredgecolor\": \"black\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "by visualizing data :\n",
    "\n",
    "\n",
    "1-\"less than high school\" : data is right skewed with median value 2 childs, most of less than high school people has children more than 2 childs(Q3=4 , Q1=1 , IQR=3). \n",
    "\n",
    "2-\"High School\": data is left skewed (as mean before median),median value 2 childs, most of high school people(in our box we concern) has children less than 2, there is ouliers at 8 value.\n",
    "\n",
    "3- \"junior college\" : right skewed with median 1 childs.\n",
    "\n",
    "4-\"Bachelor\": same for \"junior college\" .\n",
    "\n",
    "5-\"Graduate\": data is right skewed with median=1 and IQR=2 and outlier at 7.\n",
    "\n",
    "there's overlapping between data in each degree.\n",
    "\n",
    "\n",
    "conclusion: as the degree of education increases the number of childrens obtained decreases but it satbilizes from college to graduate so i think there's relation between the degree and number of childs but i can't confirm that without making hypothesis test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ccc7940400>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"degree\",y=\"childs\",data=ourdata,order=[\"Lt High School\",\"High School\",\"Junior College\",\"Bachelor\",\"Graduate\"],ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bar plot shows that highest number of childrens belongs to \"lt high school\" and decreases and stabilizes to \"Graduate\" degree."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}