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Essay / Research Paper Abstract
This 4 page paper uses health data on live expectancy for males over a period of time to demonstrate the use of regression analysis. The data is presented, the method of analysis and results are discussed and presented in graph form. The paper ends by looking at the potential of model breakdown. The bibliography cites 2 sources.
Page Count:
4 pages (~225 words per page)
File: TS14_TEreghealth.rtf
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Unformatted sample text from the term paper:
and using those data points to project forward by trying to carry on the graph line the existing data used, the least square method seeks to predict that line with
the existing line drawn so that it has the least squares from the data points. This is then carried forward. This is an old technique that the
French mathematician Legendre first published in 1805, but the method is older than this, as a later published memoir published in 1808 indicated that Gauss had used this approach in
1795 (McCullagh and Nelder, 1983). The ordinary least squares (OLS) has many advantages, it is easy to use and the basics are well understand, leading to a simple implementation. To
consider this we can look at the application of regression analysis. Linear regression used forecasting can be particularly important in planning activities. For example, we know that with increasing medical
advances and better lifestyles mortality levels are generally increasing. However, for governments and other service providers to plan for the future they need to project how much of the population
will be living into their later years. In order to demonstrate linear regression analysis we can use data which gives us the proportion of the population in a country which
are living to between the ages of 17 years and 80 years. The data, and the data table are shown in figure 1 with the data taken from the National
Audit Office for England and Wales. Figure 1 Original data for analysis Where there is regression analysis used it is useful if the data is roughly in a
line. Here the dependent variable is the percentage of the population that are living to between the ages of 17 and 80 years, the independent variable the years. The original
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