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• Level: GCSE
• Subject: Maths
• Word count: 4377

# Maths: Data Handling Coursework

Extracts from this document...

Introduction

Data Handling Coursework

This coursework is based on data handling. The information that I’ll be gathering is off an exam board. It contains the information of 1183 students from the key stages 3 and 4. It relates to students from Mayfield Secondary School and contains qualitative and quantitative data. Quantitative data is data produced numerically, for example, IQ, height and weight. Quantitative data is good especially if you want to do statistical comparisons. They are used to measure data and tend to give a better picture than qualitative. This is because we can gather a lot of information and make comparisons. Qualitative data is data produced in words and is more descriptive than quantitative data. It is less useful than quantitative data if we want to do statistical comparisons.

The data from the exam board is secondary data. Secondary data means that it is research which had been collected by somebody else. As this is secondary data, I assume it to be reliable, but I am aware that I have to keep in mind that there may be a few outliers. Outliers are usually assumed to be errors. These kinds of outliers are called rogue data. Other data which can also be seen as an error are sometimes also seen as indicating extra information. These outliers are called valid data. If the result does include a few outliers, it could have effect on my results, making it either less or more reliable.

The pupils I would be concentrating on are the girls and boys of years 7, 9 and 11. The reasons why I have left out the other years is because I feel that by looking at only the three, I would still be able to gain a reliable result.

Middle

The slight difference between the tallest girl and the tallest boy could show that girls in Year 9 can be only slightly taller than boys in Year 9; however, the shortest girl is 125cm, whereas the shortest boy is 130cm. The shortest boy is taller than the shortest girl. This could be showing that where the height for the girls in Year 9 varies slightly, boys are more similar with their heights between each other.

 Year 7 Girls(Total of 55) Weight(kg) Year 7 Boys(Total of 64) 9 2 6 98876530 3 13455577888 9887777776555555554333221100000000 4 00000000112234444555556777888 764321000 5 002334566799 2 6 0000223459 7 5 0 11

This stem and leaf diagram is of the weight of pupils in Year 7. I have noticed that most pupils fall into the 40-49kg group. There are more girls than boys in the 40-49kgs category. Also, more boys than girls weigh over 60kg. This shows that boys are generally taller than girls. In this stem and leaf diagram, I spotted an outlier. One of the girls’ in year 7 weighs 110kg which is much higher than the average weight for girls which is 45kg.

Stem and Leaf diagram for Year 9 pupils:

 Year 9 Girls(Total of 60) Height(cm) Year 9 Boys(Total of 50) 62 10 5 13 2 965 14 6 99988755555443322110000 15 022344445566788 87555554322111000000 16 0000012445556778 511111000 17 0002235578 00 18 0001122

Here is a stem and leaf diagram for the pupils in Year 9. It shows that more boys are the height of 180-189cm. The tallest girls in this year group are both the height of 180. However, the tallest boy in this year group is 182. Compared to the number of girls, there also much more boys in this category. This shows that boys are generally taller than girls in year 9. The shortest girls fall in the category of 10cm, whereas the shortest boy is 132cm.

I felt that the shortest girls, where one is the height of 102cm and the other was 106, may have been outliers.

Conclusion

Evaluation

In future when doing the same research, I may increase the sample size in order to gain more accuracy. For example, instead of looking at just year 7 and 9 and 11, I would include year 8 and year 10 too. I feel that if I have more pupils to look at, my results would also be more reliable. I would choose to increase the sample sizes because bigger sample sizes can be convenient because you can present finding onto graphs, etc.

Another thing I would do is collect my own data. This is because the data which I used to help me with this investigation was secondary data which I had to assume to be reliable. If I had collected my own primary data, I think it would be less biased.

However, I some people may find that decreasing the sample size could also be a good idea. This is because when looking at big sample sizes, we may make errors as there is too much to count and look at. For example, we may accidently skip a pupil, etc. During this research I miscounted several times when trying to find the mean and modes and also quartiles. And so, I had to keep going back and correcting my mistakes and checking whether I had missed anymore out. This can be time consuming. With a smaller sample, errors like such can be avoided. Although you can still achieve well results with small sample sizes, I feel that accuracy is more important so a bigger sample size is better.

This student written piece of work is one of many that can be found in our GCSE Height and Weight of Pupils and other Mayfield High School investigations section.

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