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Investigate if there is any correlation between the GDP per capita (\$) of a country and the life expectancy at birth (years).

Extracts from this document...

Introduction

Aim: -

My aim is to investigate if there is any correlation between the GDP per capita (\$) of a country and the life expectancy at birth (years).

The GDP is the gross domestic product or value of all final goods and services produced within a nation in a given year. GDP dollar (\$) estimates are derived from purchasing power parity (PPP) calculations.  The GDP per capita (\$) shows GDP on a purchasing power parity basis divided by population.

The life expectancy at birth shows the average number of years to be lived by a group of people born in the same year, if mortality at each age remains constant in the future. It shows the life expectancy on average for the total population for male and females. Life expectancy at birth is also a measure of overall quality of life in a country and summarizes the mortality at all ages.

The reason for doing this investigation is that I have seen a lot of documentaries and read a lot of articles in the newspaper which have talked about how the gap between rich and poor has increased.  This has led to a poorer quality of life in developing countries.

Middle

San Marino

81.43

4.539076099

Saudi Arabia

68.73

4.056904851

 Country GDP - per capita, Purchasing Power Parity (\$) Log of Life expectancy at birth Log (years) American Samoa 8000 1.879382637 Anguilla 8600 1.884795364 Armenia 3600 1.823995591 Bahamas, The 15300 1.817631467 Barbados 15000 1.856366324 Benin 1100 1.708250889 Bolivia 2500 1.811440944 British Virgin Islands 16000 1.881156321 Burma 1700 1.746556361 Cameroon 1700 1.681693392 Central African Republic 1200 1.62024019 China 4700 1.858657484 Congo, Democratic Republic of the 600 1.689575216 Cote d'Ivoire 1400 1.629919036 Djibouti 1300 1.634779458 East Timor 500 1.814247596 El Salvador 4600 1.848927713 Ethiopia 700 1.615318657 French Guiana 14400 1.884738738 Gambia, The 1800 1.735439203 Ghana 2000 1.752278985 Grenada 5000 1.809694359 Guatemala 3900 1.814447379 Guinea-Bissau 700 1.67182056 Honduras 2500 1.823800154 India 2600 1.803593665 Iraq 2400 1.831293744 Jersey 24800 1.897242103 Kenya 1100 1.655330558 Korea, South 19600 1.87714089 Laos 1800 1.73479983 Liberia 1000 1.682596291 Macau 18500 1.91312479 Malaysia 8800 1.855337404 Malta 17200 1.894482215 Martinique 10700 1.896085085 Mayotte 600 1.782472624 Monaco 27000 1.899108858 Morocco 3900 1.845346137 Nauru 5000 1.792041311 New Caledonia 14000 1.866405498 Nigeria 900 1.707655324 Pakistan 2000 1.793790385 Papua New Guinea 2100 1.807467376 Philippines 4600 1.840670561 Reunion 5600 1.865873528 Saint Helena 2500 1.888628725 Saint Pierre and Miquelon 11000 1.892706638 San Marino 34600 1.910784435 Saudi Arabia 11400 1.837146344

 Country Log of GDP - per capita, Purchasing Power Parity  Log (\$) Log of Life expectancy at birth Log (years) American Samoa 3.903089987 1.879382637 Anguilla 3.934498451 1.884795364 Armenia 3.556302501 1.823995591 Bahamas, The 4.184691431 1.817631467 Barbados 4.176091259 1.856366324 Benin 3.041392685 1.708250889 Bolivia 3.397940009 1.811440944 British Virgin Islands 4.204119983 1.881156321 Burma 3.230448921 1.746556361 Cameroon 3.230448921 1.681693392 Central African Republic 3.079181246 1.62024019 China 3.672097858 1.858657484 Congo, Democratic Republic of the 2.77815125 1.689575216 Cote d'Ivoire 3.146128036 1.629919036 Djibouti 3.113943352 1.634779458 East Timor 2.698970004 1.814247596 El Salvador 3.662757832 1.848927713 Ethiopia 2.84509804 1.615318657 French Guiana 4.158362492 1.884738738 Gambia, The 3.255272505 1.735439203 Ghana 3.301029996 1.752278985 Grenada 3.698970004 1.809694359 Guatemala 3.591064607 1.814447379 Guinea-Bissau 2.84509804 1.67182056 Honduras 3.397940009 1.823800154 India 3.414973348 1.803593665 Iraq 3.380211242 1.831293744 Jersey 4.394451681 1.897242103 Kenya 3.041392685 1.655330558 Korea, South 4.292256071 1.87714089 Laos 3.255272505 1.73479983 Liberia 3 1.682596291 Macau 4.267171728 1.91312479 Malaysia 3.944482672 1.855337404 Malta 4.235528447 1.894482215 Martinique 4.029383778 1.896085085 Mayotte 2.77815125 1.782472624 Monaco 4.431363764 1.899108858 Morocco 3.591064607 1.845346137 Nauru 3.698970004 1.792041311 New Caledonia 4.146128036 1.866405498 Nigeria 2.954242509 1.707655324 Pakistan 3.301029996 1.793790385 Papua New Guinea 3.322219295 1.807467376 Philippines 3.662757832 1.840670561 Reunion 3.748188027 1.865873528 Saint Helena 3.397940009 1.888628725 Saint Pierre and Miquelon 4.041392685 1.892706638 San Marino 4.539076099 1.910784435 Saudi Arabia 4.056904851 1.837146344

Conclusion

Even though the data is very reliable there are some improvements that could be made.  First of all the data was only collected for a given year in my case it was for 2003.  For more accurate data I could have used data over five years to see if there is actually a difference and to see if for example at that given years there may have been a low life expectancy due to an external factor like war or disease.  Also the sample was only from 228 countries and there are more countries in the world so a more fair representation would be to random sample from every country in the world.  This was not possible because my source did not include some of these countries due to political reasons and from lack of information for those countries.

In my investigation I had to reject 11 statistics for 11 countries this reduced the randomness of my sample.  I would improve this by making sure that data was available for every item in the parent population.

Overall I am very happy with the accuracy and reliability of my data because I got it from a very reliable source which was www.CIA.gov.  Having a reliable source for my data enables me to achieve my aim of a positive correlation.

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