The main goal with the report was to analyse the relationship from 16 different countries on how, if any, CO2 emission per capita is getting affected by population density and GDP per capita by using descriptive statistics and regression. The conclusion is that CO2 emission per capita is affected by changes in GDP per capita and that population density has no significant relation to CO2 emission per capita.
Global warming is one of the biggest problems in the international societies today. The politician keeps discussing how they can find solutions together to decrease the CO2 emissions worldwide. In this report we will try to examine if well-established countries have a higher CO2 emissions and we will examine how population density are affecting emission in our society today.
The aim with this report is first to examine the relationship with GDP per capita and CO2 emission and population density and CO2 emission. Then we will examine if high GDP per capita leads to higher CO2 emission per capita and if countries with low population density are polluting more than countries with high population density.
I believe that a country with high GDP are more likely to have a higher CO2 emission per capita since a country with high GDP are more likely to have higher productivity achieved through higher energy use. We will then start with measuring the linear association between these variables.
H0: β0≠β1 GDP≠0 (Correlation)
H1: β0=β1 GDP=0 (No correlation)
I believe that a country with high population density are more likely to have a lower CO2 emission per capita since the inhabitants need travel shorter and less often. We will therefor measure the linear association for CO2 emission per capita and population density.
H0: β0≠β2 pop.density≠0 (Correlation)
H1: β0=β2 pop.density=0 (No correlation)
We want to find out how much linear association the two variables has on CO2 per capita. This can be done with this model:
CO2per capita = β0+ β1 GDP+β2 pop. density+ ε
H0: β1 GDP≠0
H1: β1 GDP=0
H0: β2 pop.density≠0
H1: β2 pop.density=0
We can then see how strong the association these two variables are against the dependent variable CO2 emission per capita.
Further on we want to test the significance of these variables.
Data and descriptive statistics
The data (GDP per capita, CO2 per capita and population density) in this report is a sample of 16 different countries and are downloaded from the International Monetary Fund, US department of Energy and OECD. All the data are ratio scale and are continuous.
Some potential problems with the associated data is:
* Some countries may have a high productivity achieved by the efficient labour force and not trough higher energy use. Both ways of high productivity leads to higher GDP per capita, its unlikely to achieve it by efficient labour force, but it can occur. * Some countries (e.g. Australia) may have low population density although they mainly have big populated cities since they have a large amount of landmass that is not suitable for life. * The different data is not from the same years. CO2 emission per capita is from 2004, population density is from various years and GDP per capita is from 2010.
To get an idea of how the dataset looks like we need to use descriptive analysis.
Mean: x=xn Median: x=n+12th S.D: sx=x2-nx2n-1
Sample variance: s2=x2-nx2n-1 Range=xh-xl
For Co2 per capita the mean is 9,285 and the median is 9,49, this will suggest that the data is normally distributed and we can see in the graph in the appendix that there are 8 countries on each side of the mean. The skewness is 0,71, since the number is positive it will imply that Co2 emission per capita is slightly skewed to the right. The mean...