• Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month
Page
  1. 1
    1
  2. 2
    2
  3. 3
    3
  4. 4
    4
  5. 5
    5
  6. 6
    6
  7. 7
    7
  8. 8
    8
  9. 9
    9
  10. 10
    10
  11. 11
    11
  12. 12
    12
  13. 13
    13
  14. 14
    14
  15. 15
    15
  16. 16
    16
  17. 17
    17
  18. 18
    18

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

image00.pngimage01.png

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.

...read more.

Middle

San Marino

81.43

4.539076099

Saudi Arabia

68.73

4.056904851

image03.png


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

image04.png


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

image05.png


...read more.

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.

...read more.

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics section.

Found what you're looking for?

  • Start learning 29% faster today
  • 150,000+ documents available
  • Just £6.99 a month

Not the one? Search for your essay title...
  • Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

See related essaysSee related essays

Related AS and A Level Probability & Statistics essays

  1. Data Handling - Planning - I intend to investigate the relationship between the number ...

    for the number of hours of TV watched, the values for mean and interquartile range for all three curves are very close to each other i.e. with a very small distribution. Cumulative frequency curves for hours of TV watched (GRAPH1), for males and females, have a number of common points.

  2. The aim of this investigation was to look at the reliability and validity of ...

    It can be stated therefore that the EPI is a reliable psychometric test, a point that we set out to prove with our original hypothesis. However, the sample used was opportunity because we tested it on ourselves and the size was limited with only eighteen subjects.

  1. "The lengths of lines are easier to guess than angles. Also, that year 11's ...

    Next, I drew some histograms, and from these you can see that even though the most densely populated groups were not the groups of 4.5 - 5 for the line, or 30 - 35 for the angle, you can see that in the year 11 estimates for line 2, the most densely populated group was 4 - 4.5cm group.

  2. Anthropometric Data

    and foot breadth being 80 (mm). Comparing the calculator variables against the excel variables is that the calculator gives a whole clear numerical value of what 'a' is and 'b' where as excel rounded up the value to 3 decimal places. Bring the calculator value to 3 decimal places will be: y=ax+b a= 0.2179960043 b= 27.42162287

  1. AS statistics coursework - correlation coefficient between height and weight in year 11 boys ...

    etc... then d (residual) would fit the formula: I did not use this so that I can show the stages of the workings. di = yi - (a + bx) 1. y = -12.23 + (39x1.73) = 55.24kg To work out the residual you take the actual weight from the theoretical weight.

  2. Driving test

    Instructor 0-10 mistakes 10-15 mistakes 15-20 mistakes 20-40 mistakes Average % pass A M=13/29x100 = 44.8% F=10/31x100 = 32.3% M=8/29x100 =27.6% F=13/31x100 =41.9% M=5/29x100 =17.2% F= 6/31x100 =19.4% M=2/31x100 =6.5% F=3/29x100 =10.3 M= 72.4% F= 74.2% B M=19/44x100 =43.2% F=8/49x100 =16.3% M=5/44x100 =11.4% F=6/49x100 =12.2% M=8/44x100 =18.1% F=11/49x100 =22.4% M=12/44x100

  1. Data Analysis of American House Price

    By comparing the values of the range and inter-quartile range in relation with the standard deviation, it is clear that the houses with a pool have a higher dispersion in price and the prices are more spread out than houses without a pool.

  2. Used Cars - What main factor that affects the price of a second hand ...

    x 89 = 17.8 Large cars: (50 / 250) x 92 = 18.4 In the sample of 50 cars there need to be: 14 Small cars 18 Medium cars 18 Large cars Now that I now the proportions of the different sized cars that are meant to be in the sample I can go on to select the cars from the database.

  • Over 160,000 pieces
    of student written work
  • Annotated by
    experienced teachers
  • Ideas and feedback to
    improve your own work