Differences in wealth and life expectancy of the countries of the world Essay Example

Differences in wealth and life expectancy of the countries of the world Essay Example Differences in wealth and life expectancy of the countries of the world Essay Differences in wealth and life expectancy of the countries of the world Essay For my mathematics coursework I have been given the task of finding the differences in wealth and life expectancy of the countries of the world. To my aide I shall have the World Factbook Data which was given to me by my maths teacher. The World Factbook Data contains the Gross Domestic Product (GDP) per capita; this is the economic value of all the goods and services produced by an economy over a specified period. It includes consumption, government purchases, investments, and exports minus imports. This is probably the best indicator of the economic health of a country. It is usually measured annually. Another thing the data contains is the Life expectancy at birth. Life expectancy is called the average life span or mean life span, in this case of the countries or continents. This informs me of the average age a person in the specified country is likely to like to. Using this data I shall try to prove hypotheses that I shall personally predict before carrying out the investigation. For my investigation I shall be using varieties of different ways to presenting my data and results. I shall use graphs, charts as well as tables to make the data easier to read and understand for the reader. This would enable me also to keep organised and follow what I have to do. To develop my work I shall use very reliable as well as advanced methods to prove my hypotheses. These shall consist of Spearmans rank correlation coefficient, box plots, standard deviation aswell as histograms. Bearing my hypotheses in mind, I think that it would be they are irrelevant to my hypotheses and I shall gain no evidence or support from them inappropriate for me to use averages such as the mode or the range as I feel. My Hypotheses : I have chosen two hypotheses. My first Hypotheses is linked directly to my task whereas my second hypotheses is an extension task to develop my work. My hypotheses consist of: * The wealth and life expectancy of a continent is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. Method I shall acquire a systematically method. This will enable my work to be organised and easy to read. First, and foremost, I shall gather all the data that is presented before me. As my hypotheses are based on worldwide data I believe it is essential for me to use all the data. Once I have obtained the data I shall extract the data that will be used for my investigation. For this I shall use the stratified sampling method. This method is chosen because it is a fair and unbiased method. Also stratified sampling would give me an even spread of the whole continent, not compromise of the highest or lowest sets of data (as this would give me inaccurate results of the continents). Once obtaining the data specified I shall then separately, for each continent, put the data onto a table. I have chosen not to opt for putting the data in one big table, although my hypotheses are both related to worldwide information not separate continents, as this would narrow my results. Another advantage of putting the data onto separate tables for each continent is that I can then see which countries and continents prove my hypotheses and which countries and continents go against my hypotheses. After having my data separated into continents I shall first draw a scatter graph for each continent. This is to get me started and show me how spread out the data roughly is. Stratified Random Sampling Since it is generally impossible to study the entire population (every country in every continent) I must rely on sampling to acquire a section of the continent to perform my investigation. I believe it is important that the group selected be representative of the continent, and not biased in a systematic manner. For example, a group comprised of the wealthiest countries in a given continent probably would not accurately reflect the opinions of the entire continent. For this reason I have employed stratified random sampling to achieve an unbiased sample. Using this method shall: a) Give me the estimates of the countries needed for each continent b) Make selecting the data fair, as there will be no biasness. c) Give me a more accurate result. Firstly I used stratified sampling to find the number of countries needed from each continent, for my investigation. I deployed the formula: Number of countries in continent à ¯Ã‚ ¿Ã‚ ½60 Total number of countries in The World Factbook Database I multiplied the answer by sixty because that is the number that I wish to reduce the data to. I believe sixty to be the right number as it is not too big or too small and I am capable of working with that number. Results: Asia: 54/235à ¯Ã‚ ¿Ã‚ ½60=14 Africa: 57/235à ¯Ã‚ ¿Ã‚ ½60=15 Europe: 48/235à ¯Ã‚ ¿Ã‚ ½60=12 Oceania: 25/235à ¯Ã‚ ¿Ã‚ ½60=6 North America: 37/235à ¯Ã‚ ¿Ã‚ ½60=9 South America: 14/235à ¯Ã‚ ¿Ã‚ ½60=4 I then randomly selected the amount presented to me for each continent. I put the countries and their given data in a graph. In some cases I had to randomly reselect a country as the previously selected country didnt have sufficient data for me to include it in my investigation. Also for Cyprus I had to add both the Greek Cypriot area and the Turkish Cypriot area to give me the totals for the GDP-per capita for Cyprus. Data Tables Asia Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Afghanistan 700 42.27 42.66 42.46 Bangladesh 1,900 74.37 80.02 61.71 Cyprus 24,800 75.11 79.92 77.46 Gaza Strip 600 70.31 72.94 71.59 Jordan 9,000 75.59 80.69 78.06 Malaysia 4,300 69.29 74.81 71.95 Maldives 3,900 62.41 65.01 63.68 Mongolia 1,800 61.97 66.48 64.17 Oman 13,100 70.66 75.16 72.85 Qatar 3,300 70.90 76.04 69.71 Saudi Arabia 21,500 73.26 77.30 73.40 Syria 11,800 68.47 71.02 75.23 United Arab Emirates 23,200 72.51 77.60 74.99 West Bank 800 71.14 74.72 72.88 Mean 8,621 68.45 72.46 69.30 Data Tables Africa Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Burundi 600 42.73 44.00 43.36 Cape Verde 1,400 66.83 73.54 70.14 Cote dIvoire 1,400 40.27 44.76 42.48 Egypt 4,000 68.22 73.31 70.71 Gabon 5,500 54.85 58.12 56.46 Liberia 1,000 46.90 48.99 47.93 Libya 6,400 74.10 78.58 76.28 Madagascar 800 54.19 58.96 56.54 Morocco 4,000 68.06 72.74 70.35 Mozambique 1,200 37.83 36.34 37.10 Niger 800 42.38 41.97 42.18 South Africa 10,700 44.39 43.98 44.19 Sudan 1,900 56.96 59.36 58.13 Swaziland 4,900 39.10 35.94 37.54 Zambia 800 35.19 35.17 35.18 Mean 3,027 51.47 53.72 52.57 Data Tables Europe Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Belarus 6,100 62.79 74.65 68.57 Bosnia and Herzegovina 6,100 69.82 75.51 72.57 Faroe Islands 22,000 75.60 82.51 79.05 Finland 27,400 74.73 81.89 78.24 Guernsey 20,000 77.17 83.27 80.17 Macedonia 6,700 72.45 77.20 74.73 Malta 17,700 76.51 80.98 78.68 Man, Isle of 21,000 74.80 81.70 78.16 Norway 37,800 76.64 82.01 79.25 Portugal 18,000 74.06 80.85 77.35 Slovakia 13,300 70.21 78.37 74.19 Sweden 26,800 78.12 82.62 80.30 Mean 18,575 73.58 80.13 76.77 Data Tables Oceania Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) American Samoa 8,000 72.05 79.41 75.62 Australia 29,000 77.40 83.27 80.26 French Polynesia 17,500 73.29 78.18 75.67 Palau 9,000 66.67 73.15 69.82 Papua New Guinea 2,200 62.41 66.81 64.56 Vanuatu 2,900 60.64 63.63 62.10 Mean 11,433 68.74 74.08 71.34 Data Tables North America Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Anguilla 8,600 73.99 79.91 76.90 Aruba 28,000 75.64 82.49 78.98 Belize 4,900 65.11 69.86 67.43 Costa Rica 9,100 74.07 79.33 76.63 Dominica 5,400 71.48 77.43 74.38 El Salvador 4,800 67.31 74.70 70.92 Netherlands Antilles 11,400 73.37 77.95 75.60 Saint Vincent and the Grenadines 2,900 71.54 75.21 73.35 Trinidad and Tobago 9,500 66.86 71.82 69.28 Mean 9,400 71.04 76.52 73.72 Data Tables South America Countries GDP per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Argentina 11,200 71.95 79.65 75.70 Guyana 4,000 60.12 64.84 62.43 Suriname 4,000 66.77 71.55 69.10 Venezuela 4,800 71.02 77.32 74.06 Mean 6,000 67.47 73.34 70.32 Data Table Result * The wealth and life expectancy of a continent is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. Summary Continent Mean GDP per capita ($) Mean Male Life Expectancy Mean Female Life Expectancy Mean Population Life Expectancy (years) Europe 18,575 73.58 80.13 76.77 Oceania 11,433 68.74 74.08 71.34 North America 9,400 71.04 76.52 73.72 Asia 8,621 68.45 72.46 69.30 South America 6,000 67.47 73.34 70.32 Africa 3,027 51.47 53.72 52.57 Worldwide 9,509 66.79 71.71 69.00 Hypotheses 1 This data supports my first Hypotheses that wealth and life expectancy of a continent is linked and is likely to have a strong positive correlation. This is seen because the higher a continents mean GDP per capita the higher its mean Population Life Expectancy has been. This is with the exception of South America. This goes against my hypotheses. This does not prove my hypotheses incorrect as I need more sufficient evidence. Hypotheses 2 This hypothesis has already been proven correct because on in every continent the mean Male Life Expectancy is always lower then the mean Female Life Expectancy. Scatter Graphs A scatter graph is a graphical summary of bivariate data (two variables X and Y), usually drawn before working out a linear correlation coefficient or fitting a regression line. In scatter graphs every observation is presented as a point in (X,Y)-cordinate system. The resulting pattern indicates the type and strength of the relationship between the two variables. A scattergraph will show up a linear or non-linear relationship between the two variables and whether or not there exist any outliers in the data. Scatter graph is a graph made by plotting ordered pairs in a coordinate plane to show the correlation between two sets of data. The reason for me choosing the scatter graph as a way of displaying my data is because the scatter graph is easy to read and understand. Also you can visibly see the correlation which is not possible with other methods. Reading a scatter graph: * A scatter graph describes a positive trend if, as one set of values increases, the other set tends to increase. * A scatter graph describes a negative trend if, as one set of values increases, the other set tends to decrease. * A scatter graph shows no trend if the ordered pairs show no correlation. Interpreting a Scatter graph High positive correlation Perfect positive Low correlation Perfect positive High positive correlation High negative correlation Scatter Graphs Asia Scatter Graphs Africa Scatter Graphs Europe Scatter Graphs Oceania Scatter Graphs North America Scatter Graphs South America Scatter Graph Results * The wealth and life expectancy of a continent is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. Only hypotheses one was attempted with this data as hypothesis two could not be preformed with this graph. It would have had no extra information and would have been too time consuming. Hypotheses 1 This data shows the data table in a visual form. Personally, it is easier to see that continents that have less GDP capita also have a lower life expectancy. The most visible are the countries that have been circled around. These countries are a lot worse then the rest of the countries. These countries can actually be seen to be totally different compared to the rest of the world. Histograms In statistics, a histogram is a graphical display of tabulated frequencies. That is, a histogram is the graphical version of a table which shows what proportion of cases fall into each of several or many specified categories. The categories are usually specified as non overlapping intervals of some variable. .Histogram is a specialized type of bar chart. Individual data points are grouped together in classes, so that you can get an idea of how frequently data in each class occur in the data set. High bars indicate more frequency in a class, and low bars indicate fewer frequency. One of the main reasons for me choosing histograms is because it provides an easy-to-read picture of the location and variation in a data set. The histogram is another way of visually displaying your data. This makes it more appealing than a set of tables. Interpreting Histograms If the columns in a histogram are all the same width then you can compare the frequencies of the class by comparing the heights of the columns. The column with the largest area indicates the modal class. The height of a column is like averaging out the frequency over all the values in the class. Height = Frequency Class interval The taller the column is the greater the average frequency for the values in that class is. Histograms Asia Population Life Expectancy (years) Frequency Mid point Frequency Density 41-50 1 45.5 0.11 51-60 0 55.5 0.00 61-70 4 65.5 0.44 71-80 9 75.5 1.00 Total 14 1.56 Histograms Africa Population Life Expectancy (years) Frequency Mid point Frequency Density 31-40 3 35.5 0.33 41-50 5 45.5 0.56 51-60 3 55.5 0.33 61-70 0 65.5 0.00 71-80 4 75.5 0.44 Total 15 1.67 Histograms Europe Population Life Expectancy (years) Frequency Mid point Frequency Density 61-70 1 65.5 0.11 71-80 9 75.5 1.00 81-90 2 85.5 0.22 Total 12 1.33 Histograms Oceania Population Life Expectancy (years) Frequency Mid point Frequency Density 61-70 3 65.5 0.33 71-80 2 75.5 0.22 81-90 1 85.5 0.11 Total 6 0.67 Histograms North America Population Life Expectancy (years) Frequency Mid point Frequency Density 61-70 2 65.5 0.22 71-80 7 75.5 0.78 Total 9 1.00 Histograms South America Population Life Expectancy (years) Frequency Mid point Frequency Density 61-70 2 65.5 0.22 71-80 2 75.5 0.22 Total 4 0.44 Histogram Results * The wealth and life expectancy of a country is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. Population Life Expectancy (years) Frequency Mid point Frequency Density 31-40 3 35.5 0.33 41-50 6 45.5 0.67 51-60 3 55.5 0.33 61-70 12 65.5 1.33 71-80 33 75.5 3.67 81-90 3 85.5 0.33 Total 60 6.67 This was extended work to give me more information indirectly concerning hypotheses one. This data shows me that the modal group for population life expectancy worldwide is the 71-80 age range. Unsurprisingly the economically worst off continent, Africa, was the only continents to have any country with a Population Life Expectancy of below 40. On the other hand Asia, not being the second worst economically continent, alongside with Africa, had countries with Life Expectancy lower then 60. To summarise so far in my investigations only South America has not fitted in with my first hypotheses. Standard Deviation Standard deviation is the most commonly used measure of statistical dispersion. It is a measure of the degree of dispersion of the data from the mean value. It is simply the average or expected variation around an average. Standard deviation would show me how spread out the values in the sets of data are. It is defined as the square root of the variance. This means it is the root mean square (RMS) deviation from the average. It is defined this way in order to give us a measure of dispersion that is: I have chosen this method because although the scatter graph and histograms do show population distribution they do not give a precise and exact answer. This can easily be obtained by using standard deviation. * A non-negative number, and * Has the same units as the data. Interpreting Standard deviation Interpreting standard deviation is quite easy to read. A large standard deviation indicates that the data points are far from the mean and a small standard deviation indicates that they are clustered closely around the mean. In this case 0.9 is a large standard deviation and 0.1 is a small standard deviation. The formula for standard deviation is; ?à ¯Ã‚ ¿Ã‚ ½xà ¯Ã‚ ¿Ã‚ ½ -x à ¯Ã‚ ¿Ã‚ ½ V ?à ¯Ã‚ ¿Ã‚ ½ Standard Deviation Asia Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 42.27 68.45 -26.18 685.24 74.37 68.45 5.92 35.08 75.11 68.45 6.66 44.39 70.31 68.45 1.86 3.47 75.59 68.45 7.14 51.02 69.29 68.45 0.84 0.71 62.41 68.45 -6.04 36.45 61.97 68.45 -6.48 41.95 70.66 68.45 2.21 4.90 70.90 68.45 2.45 6.02 73.26 68.45 4.81 23.16 68.47 68.45 0.02 0.00 72.51 68.45 4.06 16.51 71.14 68.45 2.69 7.25 Variance 68.30 Standard Deviation 8.26 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 42.66 72.46 -29.80 887.74 80.02 72.46 7.57 57.23 79.92 72.46 7.47 55.73 72.94 72.46 0.48 0.24 80.69 72.46 8.24 67.82 74.81 72.46 2.36 5.55 65.01 72.46 -7.44 55.43 66.48 72.46 -5.97 35.70 75.16 72.46 2.71 7.32 76.04 72.46 3.59 12.85 77.30 72.46 4.85 23.47 71.02 72.46 -1.44 2.06 77.60 72.46 5.15 26.47 74.72 72.46 2.27 5.13 Variance 88.77 Standard Deviation 9.42 Standard Deviation Asia Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 42.46 69.30 -26.84 720.16 61.71 69.30 -7.59 57.54 77.46 69.30 8.16 66.66 71.59 69.30 2.29 5.26 78.06 69.30 8.76 76.81 71.95 69.30 2.65 7.05 63.68 69.30 -5.62 31.54 64.17 69.30 -5.13 26.27 72.85 69.30 3.55 12.63 69.71 69.30 0.41 0.17 73.40 69.30 4.10 16.85 75.23 69.30 5.93 35.22 74.99 69.30 5.69 32.42 72.88 69.30 3.58 12.85 Variance 78.67 Standard Deviation 8.87 Standard Deviation Africa Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 42.73 51.47 -8.74 76.33 66.83 51.47 15.36 236.03 40.27 51.47 -11.20 125.37 68.22 51.47 16.75 280.67 54.85 51.47 3.38 11.45 46.90 51.47 -4.57 20.85 74.10 51.47 22.63 512.27 54.19 51.47 2.72 7.42 68.06 51.47 16.59 275.34 37.83 51.47 -13.64 185.96 42.38 51.47 -9.09 82.57 44.39 51.47 -7.08 50.08 56.96 51.47 5.49 30.18 39.10 51.47 -12.37 152.93 35.19 51.47 -16.28 264.93 Variance 154.16 Standard Deviation 12.42 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 44.00 53.72 -9.72 94.43 73.54 53.72 19.82 392.94 44.76 53.72 -8.96 80.23 73.31 53.72 19.59 383.87 58.12 53.72 4.40 19.38 48.99 53.72 -4.73 22.35 78.58 53.72 24.86 618.15 58.96 53.72 5.24 27.49 72.74 53.72 19.02 361.86 36.34 53.72 -17.38 301.97 41.97 53.72 -11.75 138.00 43.98 53.72 -9.74 94.82 59.36 53.72 5.64 31.84 35.94 53.72 -17.78 316.03 35.17 53.72 -18.55 344.00 Variance 215.16 Standard Deviation 14.67 Standard Deviation Africa Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 43.36 52.57 -9.21 84.85 70.14 52.57 17.57 308.66 42.48 52.57 -10.09 101.84 70.71 52.57 18.14 329.01 56.46 52.57 3.89 15.12 47.93 52.57 -4.64 21.54 76.28 52.57 23.71 562.10 56.54 52.57 3.97 15.75 70.35 52.57 17.78 316.08 37.10 52.57 -15.47 239.36 42.18 52.57 -10.39 107.98 44.19 52.57 -8.38 70.25 58.13 52.57 5.56 30.90 37.54 52.57 -15.03 225.94 35.18 52.57 -17.39 302.46 Variance 182.12 Standard Deviation 13.50 Standard Deviation Europe Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 62.79 73.58 -10.79 116.32 69.82 73.58 -3.76 14.10 75.60 73.58 2.02 4.10 74.73 73.58 1.16 1.33 77.17 73.58 3.60 12.92 72.45 73.58 -1.13 1.27 76.51 73.58 2.94 8.61 74.80 73.58 1.22 1.50 76.64 73.58 3.07 9.39 74.06 73.58 0.48 0.24 70.21 73.58 -3.37 11.32 78.12 73.58 4.55 20.66 Variance 16.81 Standard Deviation 4.10 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 74.65 80.13 -5.48 30.03 75.51 80.13 -4.62 21.34 82.51 80.13 2.38 5.66 81.89 80.13 1.76 3.10 83.27 80.13 3.14 9.86 77.20 80.13 -2.93 8.58 80.98 80.13 0.85 0.72 81.70 80.13 1.57 2.46 82.01 80.13 1.88 3.53 80.85 80.13 0.72 0.52 78.37 80.13 -1.76 3.10 82.62 80.13 2.49 6.20 Variance 7.93 Standard Deviation 2.82 Standard Deviation Europe Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 68.57 76.77 -8.20 67.27 72.57 76.77 -4.20 17.65 79.05 76.77 2.28 5.19 78.24 76.77 1.47 2.16 80.17 76.77 3.40 11.55 74.73 76.77 -2.04 4.17 78.68 76.77 1.91 3.64 78.16 76.77 1.39 1.93 79.25 76.77 2.48 6.14 77.35 76.77 0.58 0.33 74.19 76.77 -2.58 6.67 80.30 76.77 3.53 12.45 Variance 11.60 Standard Deviation 3.41 Standard Deviation Oceania Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 72.05 68.74 3.31 10.93 77.40 68.74 8.66 74.94 73.29 68.74 4.55 20.67 66.67 68.74 -2.07 4.30 62.41 68.74 -6.33 40.11 60.64 68.74 -8.10 65.66 Variance 36.10 Standard Deviation 6.01 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 79.41 74.08 5.33 28.46 83.27 74.08 9.19 84.55 78.18 74.08 4.11 16.85 73.15 74.08 -0.92 0.86 66.81 74.08 -7.27 52.78 63.63 74.08 -10.45 109.10 Variance 48.77 Standard Deviation 6.98 Standard Deviation Oceania Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 75.62 71.34 4.28 18.33 80.26 71.34 8.92 79.60 75.67 71.34 4.33 18.76 69.82 71.34 -1.52 2.31 64.56 71.34 -6.78 45.95 62.10 71.34 -9.24 85.35 Variance 41.72 Standard Deviation 6.46 Standard Deviation North America Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 73.99 71.04 2.95 8.70 75.64 71.04 4.60 21.15 65.11 71.04 -5.93 35.18 74.07 71.04 3.03 9.17 71.48 71.04 0.44 0.19 67.31 71.04 -3.73 13.92 73.37 71.04 2.33 5.42 71.54 71.04 0.50 0.25 66.86 71.04 -4.18 17.48 Variance 12.39 Standard Deviation 3.52 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 79.91 76.52 3.39 11.48 82.49 76.52 5.97 35.61 69.86 76.52 -6.66 44.39 79.33 76.52 2.81 7.88 77.43 76.52 0.91 0.82 74.70 76.52 -1.82 3.32 77.95 76.52 1.43 2.04 75.21 76.52 -1.31 1.72 71.82 76.52 -4.70 22.11 Variance 14.38 Standard Deviation 3.79 Standard Deviation North America Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 76.90 73.72 3.18 10.12 78.98 73.72 5.26 27.68 67.43 73.72 -6.29 39.55 76.63 73.72 2.91 8.47 74.38 73.72 0.66 0.44 70.92 73.72 -2.80 7.83 75.60 73.72 1.88 3.54 73.35 73.72 -0.37 0.14 69.28 73.72 -4.44 19.70 Variance 13.05 Standard Deviation 3.61 Standard Deviation South America Male Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 71.95 67.47 4.49 20.12 60.12 67.47 -7.34 53.95 66.77 67.47 -0.69 0.48 71.02 67.47 3.56 12.64 Variance 21.80 Standard Deviation 4.67 Female Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 79.65 73.34 6.31 39.82 64.84 73.34 -8.50 72.25 71.55 73.34 -1.79 3.20 77.32 73.34 3.98 15.84 Variance 32.78 Standard Deviation 5.73 Standard Deviation South America Population Life Expectancy Mean Deviation Deviationà ¯Ã‚ ¿Ã‚ ½ 75.70 70.32 5.38 28.92 62.43 70.32 -7.89 62.29 69.10 70.32 -1.22 1.49 74.06 70.32 3.74 13.97 Variance 26.67 Standard Deviation 5.16 Standard Deviation Results * The wealth and life expectancy of a country is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. Continents Asia Africa Europe Oceania North America South America Male Life Expectancy 8.26 12.42 4.10 6.01 3.52 4.67 Female Life Expectancy 9.42 14.67 2.82 6.98 3.79 5.73 Population Life Expectancy 8.87 13.50 3.41 6.46 3.61 5.16 Hypotheses 1 This data does mainly concentrate on Hypotheses two but it can also be relevant to Hypotheses one as well. The continent with the highest GDP- per capita, Europe is also the continent which on average is closer to its mean then any other country. Also the continent with the lowest GDP- per capita, Africa is also the continent which on average is furthest away from its mean then any other continent. Hypotheses 2 This data proves that females have longer Life Expectancy then males, without a doubt. The females live so longer that they are further away from the mean then the males. This is because females are above the mean for each and every continent, unlike the males who are always below the mean. This table can be misleading in the concept that it seems as if men in Europe have a Longer Life Expectancy then women in Europe. This is not true. The fact is that both men and women have high Life Expectancy in Europe; (with the women averaging higher then the men again).This results leads to a high Population Life Expectancy which is close to both of them. In this case the women are closer to it, but they still contain a higher Life Expectancy. Spearmans Rank Correlation Spearmans rank correlation is used to compare two given sets of data. You use the formula p = 1- 6?dà ¯Ã‚ ¿Ã‚ ½ n(nà ¯Ã‚ ¿Ã‚ ½-1) d is the difference between the GDP-per capita and Population life expectancy. n is the number of countries in the specified continent. To work out the value of p for the results of the GDP-per capita and the Population life expectancy you add another two rows to the table. The first row is for the value of d (difference) and the second row is for the value of dà ¯Ã‚ ¿Ã‚ ½ (differenceà ¯Ã‚ ¿Ã‚ ½). Interpreting Spearmans rank correlation The value of p will always be between -1 and +1. ________________________________________________________________________ -1 0 1 If the value of p is close to 0 there is almost no correlation. If the value of p is close to -1 there is strong negative correlation. If the value of p is close to -0.5 there is weak negative correlation. If the value of p is close to 1 there is strong positive correlation. If the value of p is close to 0.5 there is weak positive correlation. Spearmans Rank Correlation Results * The wealth and life expectancy of a country is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. The Spearmans rank correlation tables show the following results about the correlation between the GDP-per capita and the Population life expectancy of a continent: Continent Results Correlation between GDP-per capita and the Population life expectancy of continent Asia. 0.7010989 Medium positive correlation Africa 0.499452321 Weak positive correlation Europe 0.8023324 Strong positive correlation Oceania 0.8857143 Strong positive correlation North America 0.55 Weak positive correlation South America 0.50 Weak positive correlation Looking at my data it is visible that all the continents have positive correlation. This proves my hypotheses, that all the continents have a positive correlation between the GDP-per capita and the Population life expectancy of a continent. The accuracy of my hypotheses can be further developed. Instead of saying that there is a positive correlation between the GDP-per capita and the Population life expectancy worldwide, I could further develop this. Looking at my data I can tell the strength of the correlation of each specific continent. Strong Accuracy Intermediate Accuracy Weak Accuracy Europe Asia. North America Oceania South America Africa Conclusion * The wealth and life expectancy of a country is linked and is likely to have a strong positive correlation. I believe this happens worldwide. * Females generally tend to live longer than males worldwide. My first hypothesis was proven correct. I realised that the continent do contain a correlation between the wealth and life expectancy of a continent. However for most of my data South America did seem to be an exception. I believe this to be because of the size of data for this continent. Although stratified random sampling was accurate it did not work in these circumstances. Another method I could have used was to give each continent the same number of countries to represent it. Only four countries were chosen for South America, I do not think that this was a sufficient enough number to represent a whole continent. I say this because I believe that the chosen method was mainly all about luck, which countries are chosen to represent a continent. This would give a biased reading. To overcome this problem I would definitely have to increase my data. For this reason I think that although my hypotheses was correct and if I was to try the same investigation again with a data size of seventy instead of sixty my hypotheses would be more successful as well as more accurate. For my second hypotheses there were no such problems. My hypothesis was not one hundred percent accurate because as always there were a few exceptions. The exceptions consisted of four countries four countries all from Africa. These countries had a higher male Life Expectancy then the female Life Expectancy. These countries are listed below. Countries Male Life Expectancy Female Life Expectancy Mozambique 37.83 36.34 Niger 42.38 41.97 South Africa 44.39 43.98 Swaziland 39.10 35.94 Zambia 35.19 35.17 Apart from these few countries, (which just prove that men can live longer then women!) my hypotheses was correct, because worldwide females tend to live longer then males. Looking at my investigation I feel in order for this data to be more accurate I would certainly need to have some minor adjustments, like the size of my data. I feel this did affect my results as the size of the data resulted in me being restricted from significant data that was not chosen due to my method of sampling. If this investigation was done again I would actually stick with the same methods, however I would expand my database and also use an even wider variety of representing my data (for example I could use the cumulative frequency graph). This would enable me to have a more accurate set of results.