{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Abstract\n",
"This document tries to find any meaningful relationships between the level of education, the health status and the financial status of a sample of a survey of pensioner in Bulgaria. The data is provided by the World bank. One way, two way ANOVA as well as kruskal–wallis statistical non parametric test have been used."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"import statsmodels.api as sm\n",
"from statsmodels.formula.api import ols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Contents\n",
"* [1. Reading the files.](#first-bullet)\n",
" \n",
"* [2. Dataset of interest.](#second-bullet)\n",
" \n",
"* [3. Data prep.](#third-bullet)\n",
" \n",
"* [4. Exploratory Data Analysis.](#fourth-bullet)\n",
" \n",
"* [5. Modeling and Hypothesis Testing.](#fifth-bullet)\n",
" \n",
"* [6. Conclusion.](#sixth-bullet)\n",
" \n",
"* [7. References.](#seventh-bullet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Reading the files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"edu = pd.read_stata('data/Education_wb_section_5b.dta')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file is stored as edu. Below we see a preview of the top 5 rows. We have 55 columns."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" cluster \n",
" location \n",
" id_hh \n",
" id_hhm \n",
" s5b_q1 \n",
" s5b_q2 \n",
" ... \n",
" s5b_q23_8_1 \n",
" s5b_q23_8_2 \n",
" s5b_q23_9_1 \n",
" s5b_q23_9_2 \n",
" s5b_q23_10_1 \n",
" s5b_q23_10_2 \n",
" lp_type \n",
" lp_size \n",
" lp_eu_region \n",
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" 1 \n",
" 1 \n",
" turkish \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" municipality center \n",
" 20 000 - 49 999 \n",
" SW \n",
" 1.148 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 244 \n",
" blagoevgrad \n",
" 65334 \n",
" 25 \n",
" urban location \n",
" 4 \n",
" 2 \n",
" 1 \n",
" turkish \n",
" ... \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
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" 0.0 \n",
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" 1.148 \n",
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" 65334 \n",
" 25 \n",
" urban location \n",
" 4 \n",
" 3 \n",
" 1 \n",
" turkish \n",
" ... \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" municipality center \n",
" 20 000 - 49 999 \n",
" SW \n",
" 1.148 \n",
" \n",
" \n",
" 3 \n",
" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 1 \n",
" 1 \n",
" bulgarian \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
" 4 \n",
" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 2 \n",
" 1 \n",
" bulgarian \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
"
\n",
"
5 rows × 55 columns
\n",
"
"
],
"text/plain": [
" resp id district city cluster location id_hh id_hhm \\\n",
"0 1 244 blagoevgrad 65334 25 urban location 4 1 \n",
"1 1 244 blagoevgrad 65334 25 urban location 4 2 \n",
"2 1 244 blagoevgrad 65334 25 urban location 4 3 \n",
"3 2 8102 haskovo 77195 805 urban location 2 1 \n",
"4 2 8102 haskovo 77195 805 urban location 2 2 \n",
"\n",
" s5b_q1 s5b_q2 ... s5b_q23_8_1 s5b_q23_8_2 s5b_q23_9_1 s5b_q23_9_2 \\\n",
"0 1 turkish ... NaN NaN NaN NaN \n",
"1 1 turkish ... 0.0 0.0 0.0 0.0 \n",
"2 1 turkish ... 0.0 0.0 0.0 0.0 \n",
"3 1 bulgarian ... NaN NaN NaN NaN \n",
"4 1 bulgarian ... NaN NaN NaN NaN \n",
"\n",
" s5b_q23_10_1 s5b_q23_10_2 lp_type lp_size \\\n",
"0 NaN NaN municipality center 20 000 - 49 999 \n",
"1 0.0 0.0 municipality center 20 000 - 49 999 \n",
"2 0.0 0.0 municipality center 20 000 - 49 999 \n",
"3 NaN NaN regional center 50 000 - 99 999 \n",
"4 NaN NaN regional center 50 000 - 99 999 \n",
"\n",
" lp_eu_region weight \n",
"0 SW 1.148 \n",
"1 SW 1.148 \n",
"2 SW 1.148 \n",
"3 SC 0.956 \n",
"4 SC 0.956 \n",
"\n",
"[5 rows x 55 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The file has 11439 rows and 55 columns. The names of columns have been commented out with #."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11439, 55)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#list(edu.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We identify the columns of interest, later they are renamed. There is a preview of the first 5 rows in the new dataframe edu_use."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"edu_use = edu[['id','s5b_q4']]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"edu_use = edu_use.rename(index=str, columns={\"id\": \"id\", \"s5b_q4\": \"education\"})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" education \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
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" 1 \n",
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]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu_use.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code for the pension dataset is analogous of the one above so it will be discussed only briefly."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"pen = pd.read_stata('data/Pension_wb_section_8.dta')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" id_hh \n",
" id_hhm \n",
" s8_q1 \n",
" s8_q2 \n",
" ... \n",
" s8_q5_2_o \n",
" s8_q5_3 \n",
" s8_q5_3_o \n",
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" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 1 \n",
" 1 \n",
" yes \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 1980.0 \n",
" 208.0 \n",
" NaN \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
" 4 \n",
" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 2 \n",
" 1 \n",
" yes \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 1032.0 \n",
" NaN \n",
" NaN \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
"
\n",
"
5 rows × 29 columns
\n",
"
"
],
"text/plain": [
" resp id district city cluster location id_hh id_hhm \\\n",
"0 1 244 blagoevgrad 65334 25 urban location 4 1 \n",
"1 1 244 blagoevgrad 65334 25 urban location 4 2 \n",
"2 1 244 blagoevgrad 65334 25 urban location 4 3 \n",
"3 2 8102 haskovo 77195 805 urban location 2 1 \n",
"4 2 8102 haskovo 77195 805 urban location 2 2 \n",
"\n",
" s8_q1 s8_q2 ... s8_q5_2_o s8_q5_3 s8_q5_3_o s8_q6_1 s8_q6_2 s8_q6_3 \\\n",
"0 2 no ... NaN NaN NaN NaN NaN NaN \n",
"1 2 yes ... NaN NaN NaN 240.0 NaN NaN \n",
"2 2 yes ... NaN NaN NaN 240.0 NaN NaN \n",
"3 1 yes ... NaN NaN NaN 1980.0 208.0 NaN \n",
"4 1 yes ... NaN NaN NaN 1032.0 NaN NaN \n",
"\n",
" lp_type lp_size lp_eu_region weight \n",
"0 municipality center 20 000 - 49 999 SW 1.148 \n",
"1 municipality center 20 000 - 49 999 SW 1.148 \n",
"2 municipality center 20 000 - 49 999 SW 1.148 \n",
"3 regional center 50 000 - 99 999 SC 0.956 \n",
"4 regional center 50 000 - 99 999 SC 0.956 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pen.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(12115, 29)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pen.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#list(pen.columns)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"pen_use = pen[['id','s8_q6_1', 's8_q6_2', 's8_q6_3']]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pen_use = pen_use.rename(index=str, columns={\"id\": \"id\", \"s8_q6_1\": \"pention1\", 's8_q6_2':'pention2',\n",
" 's8_q6_3': 'pention3'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several variables have NaN or Not a number values."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" pention1 \n",
" pention2 \n",
" pention3 \n",
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" \n",
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" 244 \n",
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"pen_use.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"health = pd.read_stata('data/Health_wb_section_6.dta')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" SC \n",
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" \n",
" \n",
" 4 \n",
" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 2 \n",
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" 180 \n",
" 0 \n",
" 0 \n",
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" 0 \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
"
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"
5 rows × 88 columns
\n",
"
"
],
"text/plain": [
" resp id district city cluster location id_hh id_hhm \\\n",
"0 1 244 blagoevgrad 65334 25 urban location 4 1 \n",
"1 1 244 blagoevgrad 65334 25 urban location 4 2 \n",
"2 1 244 blagoevgrad 65334 25 urban location 4 3 \n",
"3 2 8102 haskovo 77195 805 urban location 2 1 \n",
"4 2 8102 haskovo 77195 805 urban location 2 2 \n",
"\n",
" s6_q1 s6_q2 ... s6_q39_6 s6_q39_7_1 s6_q39_7_2 s6_q39_8 s6_q39_9_1 \\\n",
"0 1 good ... 20 0 0 0 0 \n",
"1 1 good ... 30 0 0 0 0 \n",
"2 1 good ... 30 0 0 0 0 \n",
"3 1 poor ... 180 0 0 0 0 \n",
"4 1 poor ... 180 0 0 0 0 \n",
"\n",
" s6_q39_9_2 lp_type lp_size lp_eu_region weight \n",
"0 0 municipality center 20 000 - 49 999 SW 1.148 \n",
"1 0 municipality center 20 000 - 49 999 SW 1.148 \n",
"2 0 municipality center 20 000 - 49 999 SW 1.148 \n",
"3 0 regional center 50 000 - 99 999 SC 0.956 \n",
"4 0 regional center 50 000 - 99 999 SC 0.956 \n",
"\n",
"[5 rows x 88 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"health.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(12115, 88)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"health.shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"#list(health.columns)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"health_use = health[['id', 's6_q2']]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"health_use = health_use.rename(index=str, columns={\"id\": \"id\", \"s6_q2\": \"health\"})"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" health \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
" good \n",
" \n",
" \n",
" 1 \n",
" 244 \n",
" good \n",
" \n",
" \n",
" 2 \n",
" 244 \n",
" good \n",
" \n",
" \n",
" 3 \n",
" 8102 \n",
" poor \n",
" \n",
" \n",
" 4 \n",
" 8102 \n",
" poor \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id health\n",
"0 244 good\n",
"1 244 good\n",
"2 244 good\n",
"3 8102 poor\n",
"4 8102 poor"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"health_use.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"fin = pd.read_stata('data/Financial situation_wb_section_14.dta')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4300, 49)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" resp \n",
" id \n",
" district \n",
" city \n",
" cluster \n",
" location \n",
" id_hh \n",
" s14_id \n",
" s14_q1_1 \n",
" s14_q1_2 \n",
" ... \n",
" s14_q16 \n",
" s14_q17 \n",
" s14_q18 \n",
" s14_q19_1 \n",
" s14_q19_2 \n",
" s14_q19_3 \n",
" lp_type \n",
" lp_size \n",
" lp_eu_region \n",
" weight \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 244 \n",
" blagoevgrad \n",
" 65334 \n",
" 25 \n",
" urban location \n",
" 4 \n",
" 1.0 \n",
" strongly agree \n",
" strongly agree \n",
" ... \n",
" less than enough \n",
" improve somewhat \n",
" 40.0 \n",
" 500.0 \n",
" 300.0 \n",
" 100.0 \n",
" municipality center \n",
" 20 000 - 49 999 \n",
" SW \n",
" 1.148 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 8102 \n",
" haskovo \n",
" 77195 \n",
" 805 \n",
" urban location \n",
" 2 \n",
" 1.0 \n",
" agree \n",
" agree \n",
" ... \n",
" enough \n",
" remain the same \n",
" 270.0 \n",
" 5000.0 \n",
" 2500.0 \n",
" 200.0 \n",
" regional center \n",
" 50 000 - 99 999 \n",
" SC \n",
" 0.956 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 2985 \n",
" lovech \n",
" 15552 \n",
" 297 \n",
" rural location \n",
" 5 \n",
" 1.0 \n",
" strongly agree \n",
" disagree \n",
" ... \n",
" less than enough \n",
" remain the same \n",
" 90.0 \n",
" 400.0 \n",
" 250.0 \n",
" 90.0 \n",
" common \n",
" under 1000 \n",
" NC \n",
" 1.148 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 2984 \n",
" lovech \n",
" 15552 \n",
" 297 \n",
" rural location \n",
" 4 \n",
" 1.0 \n",
" neither agree nor disagree \n",
" neither agree nor disagree \n",
" ... \n",
" enough \n",
" remain the same \n",
" 160.0 \n",
" 500.0 \n",
" 300.0 \n",
" 100.0 \n",
" common \n",
" under 1000 \n",
" NC \n",
" 1.148 \n",
" \n",
" \n",
" 4 \n",
" 5 \n",
" 2665 \n",
" kjistendil \n",
" 41112 \n",
" 265 \n",
" urban location \n",
" 5 \n",
" 1.0 \n",
" strongly agree \n",
" agree \n",
" ... \n",
" less than enough \n",
" improve somewhat \n",
" 153.0 \n",
" 700.0 \n",
" 350.0 \n",
" 200.0 \n",
" regional center \n",
" 20 000 - 49 999 \n",
" SW \n",
" 1.148 \n",
" \n",
" \n",
"
\n",
"
5 rows × 49 columns
\n",
"
"
],
"text/plain": [
" resp id district city cluster location id_hh s14_id \\\n",
"0 1 244 blagoevgrad 65334 25 urban location 4 1.0 \n",
"1 2 8102 haskovo 77195 805 urban location 2 1.0 \n",
"2 3 2985 lovech 15552 297 rural location 5 1.0 \n",
"3 4 2984 lovech 15552 297 rural location 4 1.0 \n",
"4 5 2665 kjistendil 41112 265 urban location 5 1.0 \n",
"\n",
" s14_q1_1 s14_q1_2 ... \\\n",
"0 strongly agree strongly agree ... \n",
"1 agree agree ... \n",
"2 strongly agree disagree ... \n",
"3 neither agree nor disagree neither agree nor disagree ... \n",
"4 strongly agree agree ... \n",
"\n",
" s14_q16 s14_q17 s14_q18 s14_q19_1 s14_q19_2 s14_q19_3 \\\n",
"0 less than enough improve somewhat 40.0 500.0 300.0 100.0 \n",
"1 enough remain the same 270.0 5000.0 2500.0 200.0 \n",
"2 less than enough remain the same 90.0 400.0 250.0 90.0 \n",
"3 enough remain the same 160.0 500.0 300.0 100.0 \n",
"4 less than enough improve somewhat 153.0 700.0 350.0 200.0 \n",
"\n",
" lp_type lp_size lp_eu_region weight \n",
"0 municipality center 20 000 - 49 999 SW 1.148 \n",
"1 regional center 50 000 - 99 999 SC 0.956 \n",
"2 common under 1000 NC 1.148 \n",
"3 common under 1000 NC 1.148 \n",
"4 regional center 20 000 - 49 999 SW 1.148 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"fin_use = fin[['id','s14_q13']]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"fin_use = fin_use.rename(index=str, columns={\"id\": \"id\", \"s14_q13\": \"financial_situation\"})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" financial_situation \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
" poor \n",
" \n",
" \n",
" 1 \n",
" 8102 \n",
" poor \n",
" \n",
" \n",
" 2 \n",
" 2985 \n",
" poor \n",
" \n",
" \n",
" 3 \n",
" 2984 \n",
" poor \n",
" \n",
" \n",
" 4 \n",
" 2665 \n",
" poor \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id financial_situation\n",
"0 244 poor\n",
"1 8102 poor\n",
"2 2985 poor\n",
"3 2984 poor\n",
"4 2665 poor"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin_use.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Dataset of interest\n",
"We use pandas merge function to create a left join between the pen_use and the health_use dataset on the id \n",
"column as key."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"data_use = pd.merge(pen_use, health_use, on='id', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" pention1 \n",
" pention2 \n",
" pention3 \n",
" health \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" \n",
" \n",
" 1 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" \n",
" \n",
" 2 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" \n",
" \n",
" 3 \n",
" 244 \n",
" 240.0 \n",
" NaN \n",
" NaN \n",
" good \n",
" \n",
" \n",
" 4 \n",
" 244 \n",
" 240.0 \n",
" NaN \n",
" NaN \n",
" good \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id pention1 pention2 pention3 health\n",
"0 244 NaN NaN NaN good\n",
"1 244 NaN NaN NaN good\n",
"2 244 NaN NaN NaN good\n",
"3 244 240.0 NaN NaN good\n",
"4 244 240.0 NaN NaN good"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(43085, 5)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"data_use = pd.merge(data_use, edu_use, on='id', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" pention1 \n",
" pention2 \n",
" pention3 \n",
" health \n",
" education \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" initial \n",
" \n",
" \n",
" 1 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
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" not finished initial \n",
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" 2 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
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" not finished initial \n",
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" 3 \n",
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" NaN \n",
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" NaN \n",
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" initial \n",
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" 4 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" not finished initial \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id pention1 pention2 pention3 health education\n",
"0 244 NaN NaN NaN good initial \n",
"1 244 NaN NaN NaN good not finished initial\n",
"2 244 NaN NaN NaN good not finished initial\n",
"3 244 NaN NaN NaN good initial \n",
"4 244 NaN NaN NaN good not finished initial"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(168968, 6)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.shape"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"data_use = pd.merge(data_use, fin_use, on='id', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(168995, 7)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we see a preview of the final dataset."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" pention1 \n",
" pention2 \n",
" pention3 \n",
" health \n",
" education \n",
" financial_situation \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" initial \n",
" poor \n",
" \n",
" \n",
" 1 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" not finished initial \n",
" poor \n",
" \n",
" \n",
" 2 \n",
" 244 \n",
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" not finished initial \n",
" poor \n",
" \n",
" \n",
" 3 \n",
" 244 \n",
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" NaN \n",
" good \n",
" initial \n",
" poor \n",
" \n",
" \n",
" 4 \n",
" 244 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" good \n",
" not finished initial \n",
" poor \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id pention1 pention2 pention3 health education \\\n",
"0 244 NaN NaN NaN good initial \n",
"1 244 NaN NaN NaN good not finished initial \n",
"2 244 NaN NaN NaN good not finished initial \n",
"3 244 NaN NaN NaN good initial \n",
"4 244 NaN NaN NaN good not finished initial \n",
"\n",
" financial_situation \n",
"0 poor \n",
"1 poor \n",
"2 poor \n",
"3 poor \n",
"4 poor "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Data prep\n",
"Below we use the describe function to get the descriptive statistics of the numerical data."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" id \n",
" pention1 \n",
" pention2 \n",
" pention3 \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 168995.000000 \n",
" 52470.000000 \n",
" 10033.000000 \n",
" 1221.000000 \n",
" \n",
" \n",
" mean \n",
" 4339.347720 \n",
" 1213.292834 \n",
" 380.882288 \n",
" 258.638002 \n",
" \n",
" \n",
" std \n",
" 2497.386079 \n",
" 861.293243 \n",
" 422.236855 \n",
" 197.779505 \n",
" \n",
" \n",
" min \n",
" 1.000000 \n",
" 18.000000 \n",
" 12.000000 \n",
" 8.000000 \n",
" \n",
" \n",
" 25% \n",
" 2163.000000 \n",
" 456.000000 \n",
" 192.000000 \n",
" 170.000000 \n",
" \n",
" \n",
" 50% \n",
" 4352.000000 \n",
" 1150.000000 \n",
" 240.000000 \n",
" 200.000000 \n",
" \n",
" \n",
" 75% \n",
" 6505.000000 \n",
" 1600.000000 \n",
" 432.000000 \n",
" 250.000000 \n",
" \n",
" \n",
" max \n",
" 8941.000000 \n",
" 9999.000000 \n",
" 9999.000000 \n",
" 1800.000000 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id pention1 pention2 pention3\n",
"count 168995.000000 52470.000000 10033.000000 1221.000000\n",
"mean 4339.347720 1213.292834 380.882288 258.638002\n",
"std 2497.386079 861.293243 422.236855 197.779505\n",
"min 1.000000 18.000000 12.000000 8.000000\n",
"25% 2163.000000 456.000000 192.000000 170.000000\n",
"50% 4352.000000 1150.000000 240.000000 200.000000\n",
"75% 6505.000000 1600.000000 432.000000 250.000000\n",
"max 8941.000000 9999.000000 9999.000000 1800.000000"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the info function to get the number of non-null values of the columns. pention2 and pention3 have a lot of\n",
"null values and will be subsequently dropped."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 168995 entries, 0 to 168994\n",
"Data columns (total 7 columns):\n",
"id 168995 non-null int16\n",
"pention1 52470 non-null float64\n",
"pention2 10033 non-null float64\n",
"pention3 1221 non-null float64\n",
"health 168995 non-null category\n",
"education 168995 non-null category\n",
"financial_situation 168820 non-null category\n",
"dtypes: category(3), float64(3), int16(1)\n",
"memory usage: 6.0 MB\n"
]
}
],
"source": [
"data_use.info()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"data_use = data_use[['pention1', 'health','education', 'financial_situation']]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 168995 entries, 0 to 168994\n",
"Data columns (total 4 columns):\n",
"pention1 52470 non-null float64\n",
"health 168995 non-null category\n",
"education 168995 non-null category\n",
"financial_situation 168820 non-null category\n",
"dtypes: category(3), float64(1)\n",
"memory usage: 3.1 MB\n"
]
}
],
"source": [
"data_use.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All null values are dropped. We are left with 52392 rows and 4 columns."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"data_use = data_use.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(52392, 4)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.shape"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 52392 entries, 9 to 168993\n",
"Data columns (total 4 columns):\n",
"pention1 52392 non-null float64\n",
"health 52392 non-null category\n",
"education 52392 non-null category\n",
"financial_situation 52392 non-null category\n",
"dtypes: category(3), float64(1)\n",
"memory usage: 972.9 KB\n"
]
}
],
"source": [
"data_use.info()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" pention1 \n",
" health \n",
" education \n",
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" \n",
" \n",
" \n",
" \n",
" 9 \n",
" 240.0 \n",
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" poor \n",
" \n",
" \n",
" 10 \n",
" 240.0 \n",
" good \n",
" not finished initial \n",
" poor \n",
" \n",
" \n",
" 11 \n",
" 240.0 \n",
" good \n",
" not finished initial \n",
" poor \n",
" \n",
" \n",
" 12 \n",
" 240.0 \n",
" good \n",
" initial \n",
" poor \n",
" \n",
" \n",
" 13 \n",
" 240.0 \n",
" good \n",
" not finished initial \n",
" poor \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" pention1 health education financial_situation\n",
"9 240.0 good initial poor\n",
"10 240.0 good not finished initial poor\n",
"11 240.0 good not finished initial poor\n",
"12 240.0 good initial poor\n",
"13 240.0 good not finished initial poor"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below code is commented out. It is used to export the data to a csv file."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"#data_use.to_csv('data_use.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dummy variables are being created and the original columns have been dropped."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"data_final = pd.concat([data_use.drop(['health', 'education', 'financial_situation'], axis=1),\n",
" pd.get_dummies(data_use[['health', 'education', 'financial_situation']])], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" pention1 \n",
" health_excellent \n",
" health_good \n",
" health_satisfactory \n",
" health_poor \n",
" health_don't know \n",
" education_higher, ph.d. \n",
" education_higher, master \n",
" education_higher, bachelor \n",
" education_higher, specialist \n",
" ... \n",
" education_vocational-technical \n",
" education_primary \n",
" education_initial \n",
" education_not finished initial \n",
" education_never been to school \n",
" financial_situation_very poor \n",
" financial_situation_poor \n",
" financial_situation_good \n",
" financial_situation_very good \n",
" financial_situation_rich \n",
" \n",
" \n",
" \n",
" \n",
" 9 \n",
" 240.0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
" 10 \n",
" 240.0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
" 11 \n",
" 240.0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
" 12 \n",
" 240.0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
" 13 \n",
" 240.0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
"
\n",
"
5 rows × 23 columns
\n",
"
"
],
"text/plain": [
" pention1 health_excellent health_good health_satisfactory health_poor \\\n",
"9 240.0 0 1 0 0 \n",
"10 240.0 0 1 0 0 \n",
"11 240.0 0 1 0 0 \n",
"12 240.0 0 1 0 0 \n",
"13 240.0 0 1 0 0 \n",
"\n",
" health_don't know education_higher, ph.d. \\\n",
"9 0 0 \n",
"10 0 0 \n",
"11 0 0 \n",
"12 0 0 \n",
"13 0 0 \n",
"\n",
" education_higher, master \\\n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"\n",
" education_higher, bachelor \\\n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"\n",
" education_higher, specialist ... \\\n",
"9 0 ... \n",
"10 0 ... \n",
"11 0 ... \n",
"12 0 ... \n",
"13 0 ... \n",
"\n",
" education_vocational-technical \\\n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"\n",
" education_primary \\\n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"\n",
" education_initial \\\n",
"9 1 \n",
"10 0 \n",
"11 0 \n",
"12 1 \n",
"13 0 \n",
"\n",
" education_not finished initial education_never been to school \\\n",
"9 0 0 \n",
"10 1 0 \n",
"11 1 0 \n",
"12 0 0 \n",
"13 1 0 \n",
"\n",
" financial_situation_very poor financial_situation_poor \\\n",
"9 0 1 \n",
"10 0 1 \n",
"11 0 1 \n",
"12 0 1 \n",
"13 0 1 \n",
"\n",
" financial_situation_good financial_situation_very good \\\n",
"9 0 0 \n",
"10 0 0 \n",
"11 0 0 \n",
"12 0 0 \n",
"13 0 0 \n",
"\n",
" financial_situation_rich \n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_final.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#data_final.to_csv('data_final.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Exploratory Data Analysis (EDA)\n",
"Below we can see the unique values at the columns and their absolute frequencies as well as the data type."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[poor, very poor, good, very good, rich]\n",
"Categories (5, object): [very poor < poor < good < very good < rich]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use['financial_situation'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"poor 22475\n",
"good 19222\n",
"very poor 9858\n",
"very good 804\n",
"rich 33\n",
"Name: financial_situation, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use['financial_situation'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"primary 16432\n",
"secondary specialized 7404\n",
"initial 6487\n",
"secondary vocational-technical 5291\n",
"secondary 4808\n",
"not finished initial 3672\n",
"higher, master 2825\n",
"vocational-technical 1545\n",
"never been to school 1311\n",
"higher, specialist 1194\n",
"higher, bachelor 1193\n",
"higher, ph.d. 230\n",
"Name: education, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use['education'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"good 23970\n",
"excellent 11339\n",
"satisfactory 10175\n",
"poor 6888\n",
"don't know 20\n",
"Name: health, dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use['health'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 52392.000000\n",
"mean 1213.478718\n",
"std 861.250591\n",
"min 18.000000\n",
"25% 456.000000\n",
"50% 1150.000000\n",
"75% 1596.000000\n",
"max 9999.000000\n",
"Name: pention1, dtype: float64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_use['pention1'].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is time for data visualization for better understanding. Most of the people are poor or very poor."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,10))\n",
"plt.style.use('seaborn-ticks')\n",
"plt.bar(data_use['financial_situation'].unique(), data_use['financial_situation'].value_counts())\n",
"plt.xlabel('Financial_situation').set_fontsize(30)\n",
"plt.ylabel('Numbers').set_fontsize(30)\n",
"plt.title('Distribution of people based on their financial situation').set_fontsize(30)\n",
"plt.xticks(rotation=0, fontsize=20)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,10))\n",
"plt.style.use('seaborn-ticks')\n",
"plt.bar(data_use['education'].unique(), data_use['education'].value_counts())\n",
"plt.xlabel('Education level').set_fontsize(20)\n",
"plt.ylabel('Numbers').set_fontsize(30)\n",
"plt.title('Distribution of people based on their education level').set_fontsize(30)\n",
"plt.xticks(rotation=-60, fontsize=18)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Large number of people have very basic or average education levels."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,10))\n",
"plt.style.use('seaborn-ticks')\n",
"plt.bar(data_use['health'].unique(), data_use['health'].value_counts())\n",
"plt.xlabel('Health Status').set_fontsize(20)\n",
"plt.ylabel('Numbers').set_fontsize(30)\n",
"plt.title('Distribution of people based on their health status').set_fontsize(30)\n",
"plt.xticks(rotation=-60, fontsize=20)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most people consider their health status as good."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AAAAAKCTBFgAAAACFJNgCAAAAoJAEWwAAAAAUksnjAQA2YCb0b35+tFf79d0EANhgGLEFAAAAQCFt1CO2Lrzwwjz//PMplUo5++yzs+uuu67vJgEAAACwmjbaYOupp57KP/7xj1RXV2fatGkZNWpU7rnnnvXdLAAAAABW00YbbE2cODEHHXRQkmTHHXfM/Pnzs2DBgrRv//FzHtTV1SVJZsyY0eRtbCo1NTXp0KFDkmTJvFnruTV82FtvveU1aWZqFy30ujRDXpPmR19pnrwmzVNNzcLKvRiwah9+3wJ8vA2hv9TnLPW5y+raaIOt2bNnp3fv3pWft9hii8yaNatBsFVdXZ3q6uoG6y1cuDBJMnLkyHXTUDYqA/5nfbeAlRlwy/puAR+lrzRP+krzo680T8+s7wYAQDM2a9asbLfddqtdf6MNtsrl8go/l0qlBmXDhw/P8OHDG5QtWbIkL774Yrp06ZKqqqomb2dT+Pa3v53rrrtufTcDCkF/gdWjr8Dq019g9egrsPo2hP5SV1eXWbNmpU+fPmu03kYbbHXr1i2zZ8+u/Dxz5sx07tz5E9dr06ZN+vfv35RNa3KtW7fONttss76bAYWgv8Dq0Vdg9ekvsHr0FVh9G0p/WZORWvVaNEE7CmHffffN+PHjkyQvvfRSunbt+onzawEAAADQfGy0I7Z233339O7dO0cffXRKpVJ++tOfru8mAQAAALAGNtpgK0l+9KMfre8mAAAAALCWqs4777zz1ncjWPfWdDI22JjpL7B69BVYffoLrB59BVbfxtpfSuWPfjwgAAAAABTARjt5PAAAAADFJtgCAAAAoJAEWwAAAAAUkmBrIzFv3rz84he/yIEHHpg+ffpkv/32yznnnJOZM2eu76ZBo5k7d25+/vOfZ//990+fPn0yYMCA/OAHP8hrr722Qt0lS5bkyiuvzCGHHJI+ffrkS1/6Ur7//e/njTfeWKHu8uXLc9NNN+VrX/tadtlll/Tv3z8nnXRS/va3v620HWPGjMmwYcPSr1+/7LbbbjnuuOPyl7/8pdGPFxrTlVdemZ49e+ass85qUN6U5/8jjzySkSNHZrfddkvfvn1zxBFH5MEHH2z0Y4NP69FHH82xxx6b3XbbLXvssUe+/vWvZ+LEiSvUc22B5F//+ldGjRqV/fbbL717986XvvSlnHzyyXnmmWca1NNf2JjU1tbml7/8ZXbaaaccd9xxK63TXPpEEe/PTB6/EViyZEmOPPLIvPHGGxk5cmT69OmTf/zjH/ntb3+bTp065d57782mm266vpsJn8qcOXMydOjQzJs3L8ccc0x22mmnvPHGG7nttttSV1eXO++8MzvvvHOSpFwu54QTTsgTTzyRoUOH5ktf+lJmzpyZG2+8MXV1dbnnnnuy7bbbVrZ9zjnnZPTo0Tn44IMzYMCAvPfee7n11ltTU1OTW265Jbvttlul7n//93/nyiuvzF577ZXBgwenrq4u1dXVmTp1aq644ooccsgh6/x3A59k2rRpGTJkSJYuXZrDDz88F198cWVZU53/9913X84666z06tUrRx11VFq3bp3//d//zZNPPplRo0bl+OOPX5e/Alil0aNH55xzzskee+yRIUOGZOHChbnlllsyc+bM/Pa3v81ee+2VxLUFkuSll17KiBEj0qpVq4wcOTKf+9znMmPGjNx+++2ZPXt2rr322nzlK1/RX9iovP766/nRj36UN954I4sWLcqee+6Z2267rUGd5tInCnt/VmaDd91115V79OhR/t3vfteg/E9/+lO5R48e5QsvvHA9tQwaz7nnnlvu0aNHefz48Q3K68/z7373u5WyBx54oNyjR4/yJZdc0qDuiy++WO7Zs2f51FNPrZRNnjx5hfXL5XJ5xowZ5b59+5aHDBlSKZs+fXq5d+/e5aOOOqq8bNmySvl7771X3n///cv77LNPuba2tlGOFxrL8uXLy8OHDy8PGTKk3KNHj/KZZ55ZWdZU5/+iRYvKe+yxR/nAAw8sL1y4sFJ32bJl5cMPP7y8yy67lGfPnt1UhwyrbebMmeV+/fqVjz/++HJdXV2l/J///Gd57733Ll988cWVMtcWKJdPO+20co8ePcqPPfZYg/Jp06aVe/ToUf6P//iPcrmsv7DxmDdvXrlv377lwYMHl1977bVyjx49yscee+wK9ZpDnyjy/ZlHETcC9913XzbZZJMceeSRDcoHDBiQz372s7n//vtTNnCPguvatWsGDRqUgQMHNijff//9UyqV8sorr1TK7rvvviTJ17/+9QZ1e/fund122y2PPPJI5s+f/7F1u3XrloMOOigvvfRSXn311STJgw8+mKVLl2bkyJGpqqqq1G3fvn2GDBmS2bNn5/HHH2+kI4bGcccdd+Svf/1rzjjjjBWWNdX5P2HChLz77rs58sgjs8kmm1TqVlVV5Zhjjsn777+fcePGNfqxwpoaM2ZMFi1alNNOOy0tWvz/t83du3fPE088kTPPPLNS5toCyT//+c8kSf/+/RuU77DDDtliiy0yffr0JPoLG4+lS5dm8ODBufvuu/P5z39+lfWaQ58o8v2ZYGsDt2DBgrz++uvZeeed07p16wbLSqVSdt1118ydOzdvvfXWemohNI7vfOc7ueyyy1IqlRqUL1iwIOVyOZ07d66UvfDCC9lyyy3z2c9+doXt9O3bN0uXLs2UKVMqdauqqrLrrruutG6SPP/885W6SRoM/V1VXWgOZsyYkcsuuyyDBw/O3nvvvcLypjr/6+d/6Nev3wp16/elr9AcPPHEE2nXrl3lvK6rq0ttbe1K67q2QCpv3N98880G5e+9917mz5+fL3zhC0n0FzYenTt3zs9+9rN85jOf+dh6zaFPFPn+TLC1gav/X5GVdZAk2XLLLZN8MMkjbIhuvfXWJMlBBx2U5IOga968eZ/YJ+rD3unTp6dTp05p1arVKuvW95/6/tatW7cV6m611VYN6kJzcP7556dVq1YZNWrUSpc31fn/cdcmfYXm5PXXX8+2226bqVOn5thjj80uu+ySXXbZJYMGDcof/vCHSj3XFvjAySefnE033TRnnHFGnnnmmcydOzd///vfc/rpp6dUKuV73/ue/gIf0Vz6RJHvzwRbG7iFCxcmSdq2bbvS5fXl9fVgQzJhwoT8+te/Tu/evXPMMcck+eQ+UT/str7ewoULGwzF/aS6LVu2XGF05If3p6/RXIwbNy4PP/xw/uu//iudOnVaaZ2mOv/r/13ZtvUVmpN333038+fPz3/+539m9913z7XXXpsf//jHmT9/fn74wx/mj3/8YxLXFqjXo0eP3HXXXVm+fHlGjhyZvffeO4MHD87UqVMrH7agv0BDzaVPFPn+rOX6bgDNw0cf34Kiu++++3Luueeme/fuue6661b6x3xl1mS+ufq6q9N/1qQuNLX58+fnF7/4Rfbcc88MGzZsrbbR1Oe/vkJzsHTp0kyfPj2/+tWvMnjw4Er5AQcckMMOOywXXnhhDj744E/cjmsLG4vXX389J510UmprazNq1Kh8/vOfz9y5c3PjjTfm5JNPzlVXXZUdd9zxY7ehv0BDzalPNNf+I9jawLVv3z5JsmjRopUury9v167dOmsTNLVrr702V111Vfr06ZPrr78+W2yxRWXZmvaJdu3arbLu4sWLV6i7bNmy1NbWrhCkfbQurE+//OUvM2/evJx33nkfe4PSVOd/fT9c2f/6uS7RnGyyySZZvHhxg49CTz6YPH6vvfbKY489ltdeey1bb711EtcWOPfcc1NTU5OxY8eme/fulfJDDz00AwcOzKhRozJ27Ngk+gvUay7vT4p8f+ZRxA3cNttsk1KplBkzZqx0ef1ztNttt926bBY0mQsuuCBXXXVVvvKVr+R3v/tdg1Ar+eCP8RZbbPGJfeJzn/tckg/evMyZM2elkwXXP+f+4bpJVrptfY3m4umnn87o0aMzYsSItGvXLjNmzKh8JR/c5MyYMSPvvvtuk53/22yzTZKkpqbmE+vC+rT11lunVCqtdB6T+uvLggULXFsgH7zxnTx5cnr37t0g1EqSNm3aZM8990xNTU3eeecd/QU+pLlcQ4p8fybY2sBtsskm6dmzZ1566aW8//77DZbV1dXlueeey5ZbblmZDA6K7Nprr82tt96aoUOH5pprrlnlc+r9+vXLjBkz8vbbb6+w7Nlnn02bNm2y8847J/ngE0SWL1++0k8AmTx5cpJk9913r9T9cPlHt5skX/ziF9fiyKDxTJo0KeVyObfccksOOOCABl/JB3NvHXDAAbnoooua7PyvX6e+/OPqwvrUr1+/LF26NNOmTVthWf01pH6SXdcWNnZLlixJuVxe4T1Hvfo34e+//77+Ah/RHPpEke/PBFsbgSOOOCKLFy/OXXfd1aD8/vvvz5w5c3LEEUesp5ZB45k0aVKuvvrqDBw4MBdccEGqqqpWWbf+nL/55psblD/11FOZMmVKDjvssMow22HDhqVUKq1Q980338zDDz+cvfbaK9tuu22SZNCgQWnTpk1uu+22LFu2rFL3//2//5cxY8Zk2223zV577dUIRwtrb9CgQbnuuutW+pUke++9d6677rocf/zxTXb+77///unSpUtGjx6dBQsWVOrW1tbm9ttvT8eOHXPooYc28W8CPln9HHTXXHNNgzlOXn755TzzzDPp2bNn5T8HXVvY2HXq1Cnbbbdd/v73v68QBs+bNy+TJk1K+/bt06NHD/0FPqI59Iki359VnXfeeeet70bQtHbeeec8/vjjue+++zJ//vzMmzcvY8eOzeWXX54ddtghF1100UqH2EORfPe7382sWbMycuTIvPPOO5k2bdoKX1tvvXVatWqV7bffPq+88kruvffevPPOO1m4cGEmTJiQCy64IJtvvnkuv/zyyoWjc+fOWbBgQe699968/PLLWbp0aSZNmpSf/vSnKZfLueqqqyqPo2yyySZp3759Ro8enWeeeSblcjnPPfdczj///MyaNSuXX3555SID68vmm2+e7bfffqVf11xzTfbYY49861vfSufOnZvs/K+qqsq2226be+65J48++mhKpVKmTp2aCy+8MC+//HJ+9rOfZdddd12fvyZI8sHHo7/77ru59957M2XKlCxbtiwPP/xwzjvvvCxbtiyXXnpp5dEN1xb44PHdsWPH5sEHH8zixYtTU1OTSZMm5ZxzzsmsWbNyzjnnpG/fvvoLG41p06blqaeeqrwfGTduXNq2bZstttiiwXuUHj16rPc+UeT7s1J5TabYp7AWLFiQq6++Og899FBmzZqVTp06ZeDAgfnOd76TzTbbbH03Dz61nj17fmKd//u//6u8Aamtrc3111+fBx54INOnT0/Hjh2z33775Qc/+EG23HLLBuuVy+Xcfvvtqa6uzptvvpm2bdtmzz33zPe///2VfrLPgw8+mJtvvjmvvvpqqqqq0q9fv5x22mmV4b3QXPXs2TOHH354Lr744kpZU57/jz/+eP7nf/4nU6ZMSblczk477ZSTTjopX/nKV5r0OGFNlMvlVFdX584778wbb7yR1q1bZ/fdd89pp522wg2+awskzz33XG644YZMnjw58+fPT7t27dKnT58cf/zx2X///Sv19Bc2BldffXWuueaaj61T/x6lufSJIt6fCbYAAAAAKCRzbAEAAABQSIItAAAAAApJsAUAAABAIQm2AAAAACgkwRYAAAAAhSTYAgAAAKCQBFsAAAAAFJJgCwBgA3XnnXemZ8+e6dmzZ5577rn13RwAgEYn2AIAAACgkARbAACN4Ic//GFldNRjjz22xus/8cQTlfW///3vN0ELAQA2PIItAIBGMHLkyMr3d9555xqvf9ddd610WwAArJpgCwCgEXzxi1/MzjvvnCR55JFHUlNTs9rrzpo1Kw8//HCSpGfPntljjz2apI0AABsawRYAQCMZMWJEkqSuri533333aq83evToLF26NInRWgAAa6Ll+m4AAMCG4mtf+1ouvfTSzJs3L/fcc09OOeWUVFVVfew6y5cvzz333JMk6dixYwYPHtxgeblczkMPPZQHH3wwU6ZMydy5c7Ns2bJ07NgxPXr0yFe/+tUMHTo0rVq1WqO2HnXUUXn++eez/fbbZ9y4cZ+q3muvvZY77rgjTz75ZN5+++0sXbo0m2++eXr16pVDDz00gwcP/sTfAwDA2hBsAQA0kjZt2mTo0KG58cYbU1NTkwkTJuSggw762HUee+yxTJ8+PUkydOjQtG3btrLs/fffzymnnJK//OUvlbKqqqq0aNEic+bMycSJEzNx4sT8/ve/z4033pj27ds3zYF9jBtuuCGXX3556urqkiSlUiktW7ZMTU1Nampq8sgjj+S2227L9ddfn86dO6/z9gEAGzaPIgIANKIRI0akRYsPbrFWZxL5+knjS6VS5VHGeldddVUl1Dr44IPzhz/8IVOmTMkLL7yQCRMm5Nhjj02SPP/887nkkksa8zBWy1133ZVLL700dXV12X///XP33Xfnb3/7W55//vn8+c9/zje/+c20atUqU6ZMyUknnVQJvwAAGotgCwCgEXXv3j37779/kuTxxx/Pv/71r1XWnTFjRh599NEkyX777ZftttuusqxcLmf06NFJkk6dOuWKK67IjjvumFKplFKplK222io//vGPKxPN33///amtrW2qw1rB/Pnz86tf/SpJcsABB+TXv/51+vbtm9atW6eqqirdu3fPmWeemR//+MdJkilTpuTee+9dZ+0DADYOgi0AgEZWPwF8uVxOdXX1Kuvdc889lVFM9aOv6pVKpTz00EMZO3ZsbrnlllXOUVUfoi1ZsiT//Oc/G6P5q+X+++/PggULkiQ//OEPK6PUPurII49M165dkyQPPPDAOmsfALBxEGwBADSyL3/5y/nc5z6XJPn973+/0pFUdXV1lRFZHx7l9WGbbrppdthhh/To0WOV++rQoUPl+4ULF37Klq++p556KknSunXr7LTTTqus16JFi8qoshdeeCHlcnmdtA8A2DiYPB4AoJGVSqUcc8wxueiiizJ37tw89NBDGTRoUIM6EyZMyIwZM5I0nJfroxYsWJAxY8Zk4sSJmTFjRubMmZNFixZVAqKlS5dW6q7L0Ogf//hHZf/9+/f/2LpLlixJkixatChz5swxiTwA0GgEWwAATWDo0KG58sors2jRotx1110rBFv1k8a3adMmw4YNW+k2nnnmmXz3u9/NnDlzmry9a2r+/PlJPgjT3nvvvdVeb+HChYItAKDRCLYAAJpAx44d87WvfS3V1dV5+umn89prr2WHHXZIkrz11lt5/PHHkySDBg3KpptuusL6NTU1OeWUU/Luu+8mSYYMGZIhQ4akR48e2XTTTdOy5Qe3cXfeeWfOO++8dXNQH1IqlZIknTt3rhwLAMC6Zo4tAIAmUj+JfPJBAFXv7rvvzvLly5OsOGl8verq6kqodcopp+SSSy7J3nvvnS222KISaiUNH0VsCqvafseOHZMkc+bMafI2AACsimALAKCJ9OzZszJx+gMPPJBly5Zl+fLlGTNmTJJk9913T69evVa67pQpUyrfjxgxYpX7ePXVV9eqbfVzetV/KuOq1NTUrLS8fvRZuVzOW2+9tVZtAAD4tARbAABNqD6UmjdvXh5//PE8+eSTmTlzZpJVj9ZKPpg0vt5mm2220jrvvvtu/vjHP65Vu9q1a5ckHzt/18svv7zK5fWBXZKMHz/+Y/c1bty4vPjiiz4REQBodIItAIAmdPDBB6dr165JkrFjx1aCqC5duuTggw9e5XrdunWrfD916tQVltfW1ubMM89MVVVVpWxNJnHv3r17kg8mc3/hhRdWWF4ul3P55Zevcv1BgwZlk002SZLcdNNNeeedd1Za74UXXsjpp5+eYcOG5eqrr17t9gEArA7BFgBAE2rZsmWGDx+eJPnzn/9cGd101FFHpVWrVqtc79/+7d8q3//kJz/JK6+8knK5O9RBngAAAkRJREFUnNra2jz66KM5+uij89e//jUXX3xxpd64ceNSV1e3WiOjDjzwwMr3P/7xj/Pyyy9Xfn711Vdz2mmn5Zlnnsk+++yz0vXbt2+fM844I8kHo9GOPfbYPPzww6mtrU3ywUiwW2+9Nd/4xjeybNmybL755g3mHAMAaAylsjHhAABNatasWTnwwAMrk6y3bNkyDz/8cINRWR9VV1eXb37zm5k0aVKlrHXr1lm6dGnK5XI222yzXHfddendu3cGDBhQebyxVatWOfbYY3PWWWc1+MTE6urq9OvXr7Ktcrmck08+ORMmTGiw/eSD0WBt2rTJFVdckT//+c8ZPXp0tt9++4wbN26Fdl5//fW54oorKnN1lUqltGrVqhJwJUnXrl1z7bXXZtddd13TXx0AwMcyYgsAoIl16dIlhxxySOXngQMHfmyolSRVVVW54YYb8r3vfS9f+MIX8pnPfCblcjnbbbddTjrppNx///3Zbbfd0rp161x++eXZaaed8pnPfCabb755ZWL3j1MqlXL11VfnjDPOyM4775xNNtkkdXV12WyzzTJs2LCMHj26waiuVTnppJPy4IMP5rjjjssXvvCFtGvXLsuXL89mm22WPfbYI2eeeWb++Mc/CrUAgCZhxBYAAAAAhWTEFgAAAACFJNgCAAAAoJAEWwAAAAAUkmALAAAAgEISbAEAAABQSIItAAAAAApJsAUAAABAIQm2AAAAACgkwRYAAAAAhSTYAgAAAKCQBFsAAAAAFJJgCwAAAIBC+v8AjgQRHGqUsQkAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,10))\n",
"plt.style.use('seaborn-ticks')\n",
"plt.hist(data_use['pention1'], alpha=0.7, rwidth=0.99)\n",
"plt.xlabel('Value').set_fontsize(30)\n",
"plt.ylabel('Numbers').set_fontsize(30)\n",
"plt.title('Histogram of Pentions').set_fontsize(30)\n",
"plt.xticks(rotation=0, fontsize=20)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see a concentration of low pensions of people. The distribution seem somewhat similar to the beta distribution with parameters $\\alpha = 2$ and $\\beta = 5$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Modeling and Hypothesis Testing\n",
"We will use a one way ANOVA to test if there is any relationship between the predictors and the dependent variable the pension.\n",
" \n",
"The Null hypothesis H0 is that the distributions are the same, so there is no relationship.\n",
" \n",
"The alternative hypothesis Ha is that there is a difference, so there is a relationships.\n",
"For more information on the topic view this link:\n",
"https://en.wikipedia.org/wiki/One-way_analysis_of_variance"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"df_anova = data_use[['pention1', 'health']]\n",
"grps = pd.unique(df_anova.health.values)\n",
"d_data = {grp:df_anova['pention1'][df_anova.health == grp] for grp in grps}"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"F, p = stats.f_oneway(d_data['good'], d_data['excellent'], d_data['excellent'], d_data['satisfactory'],\n",
" d_data['poor'],d_data[\"don't know\"])\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"df_anova = data_use[['pention1', 'education']]\n",
"grps = pd.unique(df_anova.education.values)\n",
"d_data = {grp:df_anova['pention1'][df_anova.education == grp] for grp in grps}"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"F, p = stats.f_oneway(d_data['not finished initial'], d_data['initial '],\n",
" d_data['higher, specialist '], \n",
" d_data['primary '],d_data['higher, master '],\n",
" d_data['secondary '],\n",
" d_data['secondary specialized '],d_data['secondary vocational-technical '],\n",
" d_data['vocational-technical '], \n",
" d_data['never been to school'],d_data['higher, bachelor '],\n",
" d_data['higher, ph.d. '])\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Depending on the p-value we reject or accept the null hypothesis. In all of our cases we reject it. So there is a present relationship. One of the drawbacks of the this analysis is the assumption of a normal distribution of the data. Because ours does not seem to have a normal distribution we will use another method the non parametric Kruskal–Wallis one-way analysis of variance. You can find more on the topic here:\n",
"https://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"df_anova = data_use[['pention1', 'financial_situation']]\n",
"grps = pd.unique(df_anova.financial_situation.values)\n",
"d_data = {grp:df_anova['pention1'][df_anova.financial_situation == grp] for grp in grps}"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"F, p = stats.f_oneway(d_data['poor'], d_data['very poor'], d_data['good'], d_data['very good'],\n",
" d_data['rich'])\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will try to use the 2 way ANOVA to use all of the variables. For more information on the topic you can view here:\n",
"https://en.wikipedia.org/wiki/Two-way_analysis_of_variance"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overall model F( 53, 52338) = 115.665, p = 0.0000\n"
]
}
],
"source": [
"model = ols('pention1 ~ C(health) + C(education) * C(financial_situation)', data_use).fit()\n",
"\n",
" # Seeing if the overall model is significant\n",
"print(f\"Overall model F({model.df_model: .0f},{model.df_resid: .0f}) = {model.fvalue: .3f}, p = {model.f_pvalue: .4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model consist of adding the health variable with the product of the education and the financial_situation.The conclusion is the same. The variables do have an effect on the pension levels."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['health']== 'good']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['health']== 'excellent']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['health']== 'satisfactory']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['health']== 'poor']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0.05917\n",
"The null hypothesis cannot be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['health']== \"don't know\"]['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The normalisy test (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html) have been used to acess if the distribution is normal or not for the pensions depending on the health variable. Only the Don't know cateogory is insufficient to reject the null hypothesis, but its absolute frquency is very low, so we will, not address the issue more. Meaning that the populations are not normaly distributed."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11339"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data_use[data_use['health']== 'excellent']['pention1'])"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6888"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data_use[data_use['health']== 'poor']['pention1'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will now try to use the non paramteric Spearman rank correlation coefficent. For more information on the topic you can view here: https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient. The test does not assume normal distribution. First we need to match the number of observations. In our case the 2 comparisons are the populations of the different health statuses. The poor health quantity is less, so we will remove parts of the excelent."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SpearmanrResult(correlation=0.020244049274967774, pvalue=0.09295705242568156)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.spearmanr(data_use[data_use['health']== 'excellent']['pention1'][0:6888]\n",
" , data_use[data_use['health']== 'poor']['pention1'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There seems to be some dependency between the 2 populations of the health status."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 2.132428954707578e-230\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"F, p = stats.kruskal(data_use[data_use['health']== 'excellent']['pention1'],\n",
" data_use[data_use['health']== 'poor']['pention1'] )\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 3.073432956639389e-160\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"F, p = stats.kruskal(data_use[data_use['health']== 'good']['pention1'],\n",
" data_use[data_use['health']== 'satisfactory']['pention1'] )\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 2 Kruskal-Wallis tests shows for that there is a connection between the health status and the pension. "
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 1.066264741767595e-230\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['health']== 'excellent']['pention1'],\n",
" data_use[data_use['health']== 'poor']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 2.58180341598765e-128\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['health']== 'good']['pention1'],\n",
" data_use[data_use['health']== 'poor']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.948038596771298\n",
"Accept null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['health']== 'satisfactory']['pention1'],\n",
" data_use[data_use['health']== 'poor']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 1.5367414076244768e-160\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['health']== 'satisfactory']['pention1'],\n",
" data_use[data_use['health']== 'good']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 1.4165210593375793e-261\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['health']== 'satisfactory']['pention1'],\n",
" data_use[data_use['health']== 'excellent']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1172.3564872757613\n",
"1035.6697239615487\n",
"1399.5917444717445\n",
"1371.9382984901279\n",
"2048.8\n"
]
}
],
"source": [
"print(data_use[data_use['health']== 'good']['pention1'].mean())\n",
"print(data_use[data_use['health']=='excellent']['pention1'].mean())\n",
"print(data_use[data_use['health']== 'satisfactory']['pention1'].mean())\n",
"print(data_use[data_use['health']== 'poor']['pention1'].mean())\n",
"print(data_use[data_use['health']==\"don't know\"]['pention1'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mann–Whitney U test was performed to access the relationship beween the health status and the pensions. It seems that the pensioners with the poorer health status have a higher pension. This is although not present with between the satifactory and the poor health status. For more information on the test you can see here:\n",
"https://ipfs.io/ipfs/QmXoypizjW3WknFiJnKLwHCnL72vedxjQkDDP1mXWo6uco/wiki/Mann–Whitney_U_test.html"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 5.02945e-09\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'higher, ph.d. ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'not finished initial']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'secondary vocational-technical ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 1.20332e-123\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'higher, bachelor ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 7.99709e-128\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'higher, master ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'secondary ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'primary ']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 1.59379e-49\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['education']== 'never been to school']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All of the populations are not having a normal distribution. We will again proceed with Mann–Whitney U test. We state the alternative hypothesis as the first population having a statically higher mean then the second. The null hypothesis is that they are the same."
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 3.794381960844234e-47\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'not finished initial']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 6.235689592591135e-25\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'primary ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 2.8207918094542113e-11\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'secondary ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 5.517724835398561e-08\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'secondary specialized ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 2.0277852612492133e-13\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'vocational-technical ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.008656751284442366\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'higher, specialist ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0013952020158243756\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, ph.d. ']['pention1'],\n",
" data_use[data_use['education']== 'higher, master ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 1.6962998494495033e-08\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, master ']['pention1'],\n",
" data_use[data_use['education']== 'higher, bachelor ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.710690782326092\n",
"Accept null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, master ']['pention1'],\n",
" data_use[data_use['education']== 'higher, specialist ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.93751409535605\n",
"Accept null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, bachelor ']['pention1'],\n",
" data_use[data_use['education']== 'secondary specialized ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.09761061258012049\n",
"Accept null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, bachelor ']['pention1'],\n",
" data_use[data_use['education']== 'secondary vocational-technical ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 6.830275706094211e-27\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, master ']['pention1'],\n",
" data_use[data_use['education']== 'secondary vocational-technical ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 6.830275706094211e-27\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['education']== 'higher, master ']['pention1'],\n",
" data_use[data_use['education']== 'secondary vocational-technical ']['pention1'], alternative ='greater')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performed tests indicate that with better education comes higher pention. This is not always the case for example some people with secondary specialized education do have higher pentions then bachelors."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"832.2723311546841\n",
"1004.135039309388\n",
"1629.2562814070352\n",
"1123.1535418695228\n",
"1597.4994690265487\n",
"1324.6948835274543\n",
"1405.214073473798\n",
"1351.000378000378\n",
"1241.1495145631068\n",
"878.234935163997\n",
"1399.5984911986588\n",
"1880.7739130434782\n"
]
}
],
"source": [
"print(data_use[data_use['education']== 'not finished initial']['pention1'].mean())\n",
"print(data_use[data_use['education']=='initial ']['pention1'].mean())\n",
"print(data_use[data_use['education']== 'higher, specialist ']['pention1'].mean())\n",
"print(data_use[data_use['education']== 'primary ']['pention1'].mean())\n",
"print(data_use[data_use['education']==\"higher, master \"]['pention1'].mean())\n",
"print(data_use[data_use['education']== 'secondary ']['pention1'].mean())\n",
"print(data_use[data_use['education']=='secondary specialized ']['pention1'].mean())\n",
"print(data_use[data_use['education']== 'secondary vocational-technical ']['pention1'].mean())\n",
"print(data_use[data_use['education']== 'vocational-technical ']['pention1'].mean())\n",
"print(data_use[data_use['education']==\"never been to school\"]['pention1'].mean())\n",
"print(data_use[data_use['education']== 'higher, bachelor ']['pention1'].mean())\n",
"print(data_use[data_use['education']==\"higher, ph.d. \"]['pention1'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally we move to the financial status. The normalisy test shows that the distribution is not normal."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['financial_situation']== 'poor']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 1.64474e-303\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['financial_situation']== 'very poor']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 0\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['financial_situation']== 'good']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p = 3.39023e-137\n",
"The null hypothesis can be rejected\n"
]
}
],
"source": [
"k2, p = stats.normaltest(data_use[data_use['financial_situation']== 'very good']['pention1'])\n",
"print(\"p = {:g}\".format(p))\n",
"p \n",
"if p < 0.05: # null hypothesis: x comes from a normal distribution\n",
" print(\"The null hypothesis can be rejected\")\n",
"else:\n",
" print(\"The null hypothesis cannot be rejected\")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 6.813399368854946e-14\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['financial_situation']== 'poor']['pention1'],\n",
" data_use[data_use['financial_situation']== 'good']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['financial_situation']== 'very poor']['pention1'],\n",
" data_use[data_use['financial_situation']== 'good']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.44036126730257436\n",
"Accept null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['financial_situation']== 'good']['pention1'],\n",
" data_use[data_use['financial_situation']== 'very good']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P-value for significance is: 0.0\n",
"Reject null hypothesis\n"
]
}
],
"source": [
"u_statistic, p = stats.mannwhitneyu(data_use[data_use['financial_situation']== 'very poor']['pention1'],\n",
" data_use[data_use['financial_situation']== 'poor']['pention1'], alternative ='less')\n",
"print(\"P-value for significance is: \", p)\n",
"if p<0.05:\n",
" print(\"Reject null hypothesis\")\n",
"else:\n",
" print(\"Accept null hypothesis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the higher the pention the better the financial status. This is entirely logical."
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1238.4464961067854\n",
"876.8548387096774\n",
"1347.6690250754343\n",
"1432.3967661691543\n"
]
}
],
"source": [
"print(data_use[data_use['financial_situation']== 'poor']['pention1'].mean())\n",
"print(data_use[data_use['financial_situation']=='very poor']['pention1'].mean())\n",
"print(data_use[data_use['financial_situation']== 'good']['pention1'].mean())\n",
"print(data_use[data_use['financial_situation']== 'very good']['pention1'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Conclusion\n",
"All the test show that there is a relationship between the variables and the pension. More work is needed to determine the strength and way of the relationship."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. References:\n",
"1. https://www.worldbank.org/\n",
"2. https://www.wikipedia.org/\n",
"3. https://medium.com\n",
"4. https://towardsdatascience.com"
]
}
],
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