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Data Handling.

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Introduction

Maths Coursework: Data Handling In this investigation I am comparing body mass index (BMI) against average number of hours of TV watched a week to see what correlation (if any) there is. This is because it seems relevant to BMI (since the less exercise people do the heavier they are, thus the higher BMI). The aim will be to show the correlation, if one exists, clearly and prove it, taking into account and bad results or bias in the data. To do this, I will first look at the population (1200 people). I must have at least 5% for the results to be reliable and without bias, so I will take 240 people (5%) as the sample. I will take stratified samples, and work out each person's body mass index; this is done by dividing their weight in kilograms over their height squared in metres. The groups I will be dividing them into for stratified sampling will be gender and year group, which I will then take 48 simple random samples from each group. A more detailed investigation could be done by adding other factors, such as distance from school or method or transport to school (allowing more or less time to watch TV). My hypothesis is that males will watch TV more, and the higher the years will watch more TV. ...read more.

Middle

Average deviation for Y7 Females I then made a graph to illustrate these results: Year groups are: 1=Y7, 2=Y8, 3=Y9, 4=Y10, Y=11. This graph shows that as the year groups go higher, deviation increases for males until year 11, when in drops greatly. For females, their deviation drops in the first year, then slowly increases, then drops again in the final year. This suggests as males get older, their BMI increases greatly, then hardly increases at all; for females, their BMI increases very slowly over the years, then averages out. I then made the BMI results into a bar chart and scatter graph to see if there was a pattern: As we can see, the graph appears to show no correlation. There are exceptional results, but when looked at closely there is a small positive correlation between peoples BMI and number of hours of TV watched. I next constructed a pie chart from the frequency table to see if I could extract any useful data: As we can see, there is a large amount of people in the same group of BMI (just under half). There appears to be no correlation in peoples BMI on its own (i.e. there is no correlation between year group BMI or gender BMI). From this we can tell that although there is no correlation between BMI in year groups and/or gender alone, when hours of TV watched is introduced, a correlation becomes apparent. ...read more.

Conclusion

The Spearman's Correlation result shows my theory was right and proved it. My hypothesis was that as there was an increase in BMI there was an increase in hours of TV watched (which there is), but it is negligible, leaving my results open to counter-arguments. Also, it is only right for the middle years (8, 9 and 10 mostly), showing as people get older their TV habits change. Overall however there is a small increase in BMI according to the more TV hours watched. Teenagers are also experiencing growth spurts during their teenage years, increasing their BMI quickly, and rounding off around 15 or 16. The sampling I took (stratified) was a good sampling, but it was only just over 5%, and more could be taken for better results and less chance of bias. Also, the topic could have been better. For example, instead of exploring hours of TV and BMI (which there is unlikely to be a link between in thousands of people), I could have investigated method of travel, distance from school, and BMI, which could have proved more conclusive. My results aren't conclusive as they do not show a correlation either way in great size, and could be left open to interpretation. There are however a large amount of results and they do show a small correlation, which is better than none at all. Ralph Weatherburn 11T ...read more.

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