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The aim of this investigation is to examine whether or not the number of people per doctor affects a countries average life expectancy.

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Introduction

Statistics.2 - Bivariate Data

Ben Ward

AIM:

The aim of this investigation is to examine whether or not the number of people per doctor affects a countries average life expectancy.

The life expectancy of many lesser economically developed countries is lower than that of more economically developed countries. Generally, better-developed countries have a greater doctor to population ratio. So I wish to determine whether this is a factor that affects life expectancy.

I choose this investigation, as I'm interested in geography particularly travelling. I plan to take a gap year after my A-levels, prior to university and hopefully visit many areas of the world including less economically developed countries. This led me to an interest in the variation of death rates between countries and I decided to compare this data to the number of doctors per person and to see if this influences the death rate in anyway.

DATA COLLECTION:

Firstly, I collected a list of all the countries in the world and their doctor to patient ratio.

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Middle

iyi ) and the sum of all columns (shown page. 5)

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Data No.

People per Dr.

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Conclusion

Within the calculations itself the results are also of my highest possible accuracy. I used Excel to initially calculate Pearson's Product Correlation Coefficient, Mean, Standard Deviation and Co-variance, that was then check by hand using a calculator and the formula's included within my investigation. I kept the data to 3signifcant figures as accuracy beyond this wasn't necessary for this particular investigation.

The regression line was also drawn by Excel and not by hand as to be most accurate.

The only inaccuracy that I felt might have effected my investigation is a particular significant outlier or anomal result: (a result over two standard deviations from the mean). This could have caused my standard deviation of X to increase and Y to decrease compared to all other data figures, leading to a possible inaccuracy to my Co-variance and Pearson's Product Correlation Coefficient. The anomaly is highlighted in my scatter diagram (including the regression line) as to show the change in the regression line to incorporate this outlier- another possibly affected factor in my investigation.

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