Association Between Electric Consumption and Co2 Emission

 

Introduction

In gapminder data, the association between electric consumption and Co2 Emission was trying to find out.

Getting and Preparing Data

Data Analysis

The p-value is lower than 0.05 so we reject the null hypthothesis that there is definetly an association between electric consumption and Co2 Emission.

R-squared value is %3, which is pretty low . The low value indicates that we might find other explanatory variables to make our model stronger.

Basic Linear Regression Model

The regression function with their coefficients are "Co2 Emission = 3.482e+6 relectrciperperson + 1.58e+06"

Codes

import numpy

import pandas as pandas

import statsmodels.api

import statsmodels.formula.api as smf

import matplotlib.pyplot as plt

from sklearn.preprocessing import Normalizer

import seaborn

# bug fix for display formats to avoid run time errors

pandas.set_option('display.float_format', lambda x:'%.2f'%x)

#call in data set

data = pandas.read_csv('data/gapminder.csv')

# convert variables to numeric format using convert_objects function

data['co2emissions'] = pandas.to_numeric(data['co2emissions'], errors='coerce')

data['relectricperperson'] = pandas.to_numeric(data['relectricperperson'], errors='coerce')

############################################################################################

# BASIC LINEAR REGRESSION

############################################################################################

scat1 = seaborn.regplot(x="relectricperperson", y="co2emissions", scatter=True, data=data)

plt.xlabel('Electric Usage per Person')

plt.ylabel('Co2 Emission')

plt.title ('Scatterplot for the Association Electric Usage per Person and Co2 Emission')

print(scat1)

print ("OLS regression model for the association between electric usage per person and Co2 Emission")

reg1 = smf.ols('co2emissions ~ relectricperperson', data=data).fit()

print (reg1.summary())