• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month
Page
1. 1
1
2. 2
2
3. 3
3
4. 4
4
5. 5
5
6. 6
6
7. 7
7
8. 8
8
9. 9
9
10. 10
10
11. 11
11
12. 12
12

# The aim of investigation is to see if there is a link between the Gross National Product (GNP), average BMI (Body Mass Index) and average Life Expectancy of a country. I have chosen to do this because it gives a good insight

Extracts from this document...

Introduction

Statistics Investigation

## Aim

The aim of investigation is to see if there is a link between the Gross National Product (GNP), average BMI (Body Mass Index) and average Life Expectancy of a country. I have chosen to do this because it gives a good insight into how healthy people in a country are and how good their standard of living is.

I have chosen the above factors to investigate because:

• GNP measures the value of goods and services that the country's citizens produced regardless of their location. GNP is one measure of the economic condition of a country, under the assumption that a higher GNP leads to a higher quality of living, all other things being equal. It is the best indicator of a countries wealth.
• BMI is a measure of body fat that is the ratio of the weight of the body in kilograms to the square of its height in meters. BMI is a better measure of health risk than actual weight in pounds. The medical terms, overweight and obesity, are based on BMI values. A BMI of between 25 and 30 is defined as overweight, and a BMI of 30 or more is considered obese. The higher your BMI, the greater the risk of developing a weight-related illness. It is best indicator of health as it shows average nutrition of a country.
• Life expectancy is the average lifespan of a person taken at birth. It is helpful because it shows standard pf living and available health resources in a country. It is also easy to compare with out country.

## Hypotheses

Middle

For my results I am using Spearman’s Rank Correlation Coefficient. This is a measure of the agreement between two sets of data. It is used to find the extent to which two sets of data correlate and is measured on a scale of –1 to +1 with one being a perfect positive agreement between the two sets on data, -1 being a perfect negative disagreement and 0 meaning now correlation. I am ranking each counties BMI and average GNP scores by the highest amount first. (B denotes average BMI, G denotes average GNP, a C denotes country)

 c a b c d e f g h i j k l m n o p q r s t u v w x y z A1 B2 C2 D2 b 8 15 6 14 10.5 12 13 7 4 10.5 9 20 17 3 27 19 5 1 16 18 28 2 30 25 26 22 24 23 21 29 g 15 7 5 10 13 6 1 8 21 12 23 22 18 11 26 19 16 4 14 20 30 8 27.5 29 25 2 24 3 17 27.5 d 7 8 1 4 2.5 6 12 1 17 1.5 14 2 1 8 1 0 11 3 2 2 2 6 2.5 4 1 20 0 20 4 1.5 D2 49 64 1 16 6.25 36 144 1 289 2.25 196 4 1 64 1 0 121 9 4 4 4 36 6.25 16 0 400 0 400 16 2.25

Spearman’s coefficient= 1- 6∑d2

N(n2-1)

= 1- 6X1893/30(302-1)= 1- 11358/26970= 1-0.4211345= 0.58

• Analysis

From my spearman’s rank results I can see that there is a good positive correlation between average BMI of a country and average GNP. I would expect this because a country with a higher average GNP would have more money to spend on food and going out therefore having a larger BMI. The countries with lower GNPs have much lower BMIs. Examples of these are the USA with a high average BMI of 27.83 and a high GNP of \$29240 and Cambodia with a low GNP of 21.69 and a low GNP of \$260. However, there are some anomalous results in my work for example Japan, who have the highest GNP but not the highest BMI. This may be because the Japanese eat healthy and culture in Japan promotes a healthy way of life. My results from this graph support my hypotheses.

• Graph 5: Average Life Expectancy Plotted against Average BMI

• Results

Conclusion

The data that I used was collected in 1998. This was several years ago and average BMI’s, Life expectancies and average GNPs would have changed, meaning that my data and research is not valid, as new data has been found.

Also, the World Health Orgainsation only had all of my relevant data for certain countries so I did not have many countries to choose from which may have limited my results.

I also felt that I would have gotten better results if I had more time to investigate more countries, as it would have made a better comparison. However, I am pleased with the amount of countries that I have investigated in this investigation.

Points for further work:

I have two possible sets of inter- related hypothesis that I could suggest for further work.

1. Does the geographically position of the country affect the BMI of the population? Does the geographically position affect the GNP of a country? Does the average GNP affect the infant mortality rate? Does the average BMI affect the average infant morality rate?
2. Does the average life expectancy affect the average number of children per household? Does the average GNP affect the number of children per household? Does the average BMI affect the number of children per household?

Sources of Information

I collected my data and information from the following places;

• The World Health Orgainsation  www.who.int
• Royal Society of Medicine www.rsm.ac.uk
• The World Bank  www.worldbank.org
• GCSE Geography Dictionary
• Statistics GCSE for AQA

This student written piece of work is one of many that can be found in our University Degree Statistics section.

## Found what you're looking for?

• Start learning 29% faster today
• 150,000+ documents available
• Just £6.99 a month

Not the one? Search for your essay title...
• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month

# Related University Degree Statistics essays

1. ## A Critical Appraisal of Three Research Studies Related To Peripheral Venous Cannulae and the ...

Curran et al (2000) has analysed data using various non-parametric tests that are appropriate to what was being tested. For instance, the Mann-Whitney U Test has been used to test the difference in the rank of scores of two independent groups whilst the Wilcoxon Signed Rank test was used to

2. ## Dorfman, Robert (1943) essay 'The detection of defective members of large populations

So if blood samples results in being negative, then the test for that pool is finished, otherwise the test should run individually again until a defective is detected. By following "this procedure until a negative pool is found", the amount of savings attainable would increase by average 5.5% with each extra percent decrease in the prevalence rate.

1. ## Stochastic Applications of Actuarial Models with R coding

and ARIMA(1,1,0) models are found and compared with the AIC for the proposed model. The AIC from ARIMA(0,1,1) model is -2035.74 while the AIC from ARIMA(1,1,0) model is -2035.79. The AIC of the proposed ARIMA(0,1,0) model is clearly smaller than the AIC of the other 2 models.

2. ## Analysing Cross-sectional Data

You can read that on the horizontal line of Chinese food. Now we understand the information that is given on the horizontal line of every food type. On the grand total of the vertical line of each column you can see how 'many' people spend between (for example)

1. ## Dress code study. The method of random sampling in this investigation was cluster ...

p = 1 - 0.583 = 0.417 Mean, � = np = 60(0.583) = 34.98 � 35.0 Standard deviation, ? = = = 3.819 � 3.82 X ~ Bin (60, 0.583) What is the probability that majority of the female ABC University students wish to abolish the restriction of wearing short shirt which does not cover bottom?

2. ## Factors predicting disclosure of chronic illness status in the workplace and general well-being for ...

However, no significant regression models were found. It therefore appeared that illness severity did not have any additional predictive effect on general well-being to the set of coping styles. In terms of the correlation between each predictor and general well-being, while all the other predictors were controlled, both 'seeking emotional social support' (�1 = .26, p < .05; �2 = .27, p < .05)

1. ## Discriminant Analysis on Determing if an MLB team will make the playoffs

In this analysis only ARZ and CHC were classified as not making the playoffs when they actually made the playoffs.

2. ## Does the data indicate that the revised (one week) forecast is significantly more accurate ...

Year No Investment Index Changes Rate of decline 0 100.00 0.00 0.00% 1 96.25 -3.75 -3.75% 2 88.75 -11.25 -11.25% 3 87.50 -12.50 -12.50% 4 86.25 -13.75 -13.75% 5 87.50 -12.50 -12.50% 6 80.00 -20.00 -20.00% 7 72.50 -27.50 -27.50% 8 73.75 -26.25 -26.25% 9 67.50 -32.50 -32.50% Using the tools in Excel, the following graph is created.

• Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to