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Coursework Topic: The Correlation Between Gdp Per Capita Against Life Expectancy At Birth In Different Countries

1513 words - 6 pages

Life expectancy at birth and GDP per capita are two major indicators of a country's development. Life expectancy is one of a main social indicators. It is the average number of years a newborn infant would be expected to live if health and living conditions at the time of its birth remained the same throughout its life. It reflects the health of a country's people and the quality of care they receive when they are sick. GDP per capita or Gross Domestic Product per capita is one of economic indicators. It measures the size of economies and thus provides an indicator on the level of national income for the resident population. Due to eEach of these indicators has its own validity and limitations. Therefore, these two indicators have to be used together, so that we could can prevent inaccuracy and bias provided by one found in each of them. I set up a hypothesis that is where the life expectancy at birth in high GDP per capita countries is higher than in low GDP per capita countries. The reason for my hypothesis is that high GDP per capita countries have primary health care and more access to nutritious foods, while low GDP per capita countries have less of them.It is very important to choose an appropriate method of sampling in for geographical analysis. There are 151 pairs of the data taken from "The World Bank Organization - Data of life expectancy at birth and GDP per capita". Since they are too many to make use of, I will choose only 40 pairs of data for this analysis. I use random sampling and systematic sampling as two appropriate methods. Random sampling is the most accurate method as it has no bias. I choose 20 random countries by marking a number, from 1 to 151, in front of each country. Since these countries have already been categorized into 6 continents of the world, so it has less bias because many Economic More Developed Countries (EMDCs) or Economic Less Developed Countries (ELDCs) are usually concentrated in one continent. For example, many EMDCs are concentrated in Western Europe, and ELDCs are concentrated in Africa. I use my calculator to generate 20 random numbers. These numbers are used to find random countries from the data. Another 20 countries are chosen by systematic sampling. I put 151 countries into alphabetically order. Every 6th countryies are is picked, therefore I am left with have 25 countries at last. However, only 20 countries are required in each sample. I used the a calculator to generate some random numbers again. Finally, I choose chose 20 countries for a systematically selection selected sample.There are some problems during the process of choosing 20 random numbers by the calculator. One is that some numbers generated by the calculation have decimal numbers, for example: 54.643. In this case I rounded this number into 2 significantce figures i.e. 55. Another problem is that the previous number or the next number of the randomly selected number is as the same as the randomly selected number after rounding its...

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