Statistics Coursework 2 Essay Example

are my final 60 countries after choosing them randomly:

Life Expectancy Birth Rate

• Algeria69.95
• Algeria22.76
• Argentina75.26
• Argentina18.41
• Australia79.87
• Australia12.86
• Belgium77.96
• Belgium 10.74
• Belize  71.19
• Belize 31.69
• Bolivia 64.06
• Bolivia 27.27
• Bosnia and Herzegovina71.75
• Bosnia and Herzegovina 12.86
• Botswana 37.13
• Botswana 28.85
• British Virgin Islands 75.64
• British Virgin Islands 15.18
• Burma 55.16
• Burma 20.13
• Burundi 46.06
• Burundi 40.13
• Canada 79.56
• Canada 11.21
• Chile 75.94
• Chile 16.8
• China 71.62
• China 15.95
• Colombia 70.57
• Colombia 22.41
• Comoros 60.41
• Comoros 39.52
• Costa Rica 76.02
• Costa Rica 20.27
• Croatia 73.9
• Croatia 12.82
• The Czech Republic 74.73
• The Czech Republic 9.11
• Denmark 76.72
• Denmark 11.96
• Dominica73.6
• Dominica 17.81
• France 78.9
• France 12.1
• Gabon 49.59
• Gabon 27.42
• Greece 78.59
• Greece 9.83
• Grenada 64.52
• Grenada 23.12
• Guyana 63.31
• Guyana 17.92
• Haiti 49.38
• Haiti 31.68
• Indonesia 68.27
• Indonesia 22.26
• Iraq 66.95
• Iraq 34.64
• Italy 79.14
• Italy 9.05
• Japan 80.8
• Japan 10.04
• Lesotho 48.84
• Lesotho 31.24
• Liechtenstein 78.95
• Liechtenstein 11.53
• Malaysia 71.11
• Malaysia 24.75
• Mali 47.02
• Mali 48.79
• Mauritania 51.14
• Mauritania 42.95
• Mauritius 71.25
• Mauritius 16.5
• Mongolia 64.26
• Mongolia 21.8
• Morocco 69.43
• Morocco 24.16
• Nauru61.2
• Nauru 27.22
• New Zealand 77.99
• New Zealand 14.28
• Nicaragua 69.05
• Nicaragua 27.64
• Norway 78.79
• Norway 12.6
• Philippines 67.8
• Philippines 27.37
• Puerto Rico 75.76
• Puerto Rico 15.26
• Qatar 72.62
• Qatar 15.91
• Romania 70.16
• Romania 10.8
• Saint Kitts And Nevis 71.01
• Saint Kitts and Nevis 18.78
• Samoa 69.5
• Samoa 15.59
• Sao Tome and Principe 65.59
• Sao Tome and Principe 42.74
• Senegal 62.56
• Senegal37.46
• Singapore 80.17
• Singapore 12.8
• Sudan 56.94
• Sudan 37.89
• Suriname 71.63
• Suriname 20.53
• Sweden 79.71
• Sweden 9.91
• Switzerland 79.73
• Switzerland 10.12
• Tanzania 51.98
• Tanzania 39.65
• Uruguay 75.44
• Uruguay 17.36
• Vanuatu 60.95
• Vanuatu 25.4
• Zimbabwe 37.13
• Zimbabwe 24.68

Analysis there is not a

very clear elliptical profile, my distribution will not be a normal distribution, therefore I shall use Spearman's rank correlation coefficient to work out a value for r. The hypothesis test for this situation would be H0: There is no correlation between life expectancy and birth rate of a country.H1: There is a negative correlation between life expectancy and birth rate of a country. After ranking my data (as shown on the page ), I worked out the difference between the ranks of a particular country for each of life expectancy and birth rate, I found the difference2(d2), which enabled me to calculate the value of r, according to my data, using the following formula: -r = 1 - 6? d2n2(n-1) where n=the number of the sample used the spreadsheet to do this calculation, which I worked out as r = -0. 8823. Using a data book, I looked in the statistics tables to find the critical value for r, when n=60, and rcritical=0.2144 where my significance level is 5%. As |-0.8823| is greater than 0.2144, which is what I was looking for to satisfy H1 and to reject H0, therefore I shall accept H1. This means that there is a correlation. However, the nearer r is to +1, the nearer the data is to a perfectly positive correlation. The nearer r is to -1, the nearer the data is to a perfectly negative correlation.

My value is very close to being -1 and so shows a very strong negative correlation. In conclusion, my prediction was correct, and there is a trend between the birth rate and life expectancy of a country: as the life expectancy increases,

the birth decreases, and vice versa.InterpretationThe results show that there is a relationship between life expectancy and birth rate. It shows there is a negative correlation, which shows that the higher the life expectancy of a country, generally, the lower the country's birth rate is.

From the above results, it is clear to see that, when focusing on developed countries, life expectancy and birth rate are very strongly negatively correlated. This means, relating these results to the original aim, that, one way of measuring the average birth rate in a particular country relative to another is to look at the life expectancies of these countries. This is important, as life expectancy in a developed country can be measured very accurately by censuses, and problems related to obtaining the average birth rate can be ignored. However, it would have been more useful if the above facts were true for the whole population. On the graph, you can see a densely populated right-hand side and a less densely populated left-hand side.

The right-hand side is that of developed countries, as their life expectancies are generally higher than less developed countries, whose birth rates are very high. It is a larger problem of getting figures for birth rate in third world countries, as the data gained from these countries are often not the whole picture; many births and deaths are not accounted for due to vast numbers of people who have not been accounted for, for example, tribal families, living in deserted parts of a country, and therefore are very inaccurate. Accuracy and RefinementsMy data were collected from a website, www.globastat.com, which is "an award-winning website with a wealth of

geographic information".

The site contains lots of data about various geographical aspects of a wide variety of countries, for example, birth rate and life expectancy. Therefore, the data will not be biased and is fairly accurate. My data and results may be accurate, but they are not representing every single country. The results are only true for the countries, which are specified above, i.e.

Fairly common and well known. This is because for smaller countries, the information is hard to obtain and so was not found by the makers of the website. I found 196 countries' data, which would have taken a very long time to interpret and show graphically; therefore I took a completely random and non-biased sample of 60 countries from it, which was a more manageable size. From these 60 countries, I was able to show their results graphically, draw a line of best-fit showing a rough and general trend, and calculate a correlation coefficient. Anomalies are likely to occur because data does not indicate the environment and for example, political stability of a country; I assume that conditions are equal for each of the countries. But this is false because many countries have been struck by natural disasters, for example, which influence birth rates and life expectancy.

A lot of poorer countries undergo starvation, leading to famine, which also affects the birth rate and life expectancy of countries. Migration may also have an effect: many healthy people may come into a country and boost the life expectancy, or many women may leave a country, therefore reducing the birth rate of a particular country.