Correlation for AIDS knowledge using current residence stability

Introduction

As said in the link,

The National Longitudinal Study of Adolescent Health (AddHealth) is a representative school-based survey of adolescents in grades 7-12 in the United States. The Wave 1 survey focuses on factors that may influence adolescents’ health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities.

source: Data Analysis Tools par Université Wesleyenne 

NB. This study is under Coursera training (Outils d'analyse des données) using python

Data

• H1GI3 (General Introductory)How old was the adolescent when he moved to his current residence?
  It's a quantitative value =>response (1, 2, 3 ... years)

• H1GH45 (General Health)
  

  How many people do you know who have AIDS?
  

  it's a quantitive value => response: range 0 to 98 people

Objective:

Study of the correlation between time spent by adolescents in current residence and AIDS people in the neighborhood.

Python code:

# -*- coding: utf-8 -*-

"""

Created on Sun Apr 19 17:51:51 2020

@author: Aymen ABID

"""

import pandas

import numpy

import seaborn

import scipy

import matplotlib.pyplot as plt

import math  

mydata = pandas.read_csv('addhealth_pds.csv', low_memory=False)

mydata['H1GI3'] = mydata['H1GI3'].apply(pandas.to_numeric, errors='coerce')

mydata['H1GH45'] = mydata['H1GH45'].apply(pandas.to_numeric, errors='coerce')

mydata['H1GI3'] = mydata['H1GI3'].apply(pandas.to_numeric, errors='coerce')

mydata['H1GH45'] = mydata['H1GH45'].apply(pandas.to_numeric, errors='coerce')

mydata['H1GI3']=mydata['H1GI3'].replace(' ', numpy.nan)

mydata['H1GH45']=mydata['H1GH45'].replace(' ', numpy.nan)

data_clean=mydata.dropna() #clean data from Nan measurements for correlation

print ('Association between residence stability and AIDS people knowledge  (r,p)')

[r,p]=scipy.stats.pearsonr(data_clean['H1GI3'], data_clean['H1GH45'])

print([r,p])

print('Fraction of the variability ')

print (math.sqrt( r))

scat1 = seaborn.regplot(x="H1GI3", y="H1GH45", fit_reg=True, data=mydata)

plt.xlabel('Residence Rate')

plt.ylabel('AIDS people knowledge Rate')

plt.title('Association Between time in current residence and AIDS that adolescent know')

Results:

Association between residence stability and AIDS people knowledge  (r,p)

[0.15162579814828608, 9.506977908603123e-35]

Fraction of the variability 

0.3893915743159912

So,

The association between urbanrate and internetuserate, the correlation 

coefficient is approximately 0.15, with a very small p-value.

The association between current residence period rate and AIDS pepoplle in neghborhood knowlge is fairly strong, 

and it's also positive as the scatter plot has already shown us. 

The association between two analysed rates is also positive

.

If we square our correlation coefficient of 0.15, 

we get 0.38. 

This could be interpreted the following way. 

If we know the residence rate, 

we can predict 38% of the variability we will see in the rate of AIDS knowlage. 

Of course, that also means that 62% of the variability is unaccounted for it.