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• Level: GCSE
• Subject: Maths
• Word count: 4789

# Maths Data handling Corsework

Extracts from this document...

Introduction

Maths Data Handling Coursework

The aim for this piece of coursework is to make 3 hypotheses as a core plan for my investigations, then process, analyse and interpret information from the data I have been provided with from the school shared area. I will do this by using my data handling skills and using computer software such as Microsoft Excel to help me.

The data I have been provided with contains information about the fitness of Year 7, 8, 9 and 10 pupils. This data consists of information such as bleep test performances in autumn and spring, cross country-Pe house run positions, and whether pupils are involved in rugby or rowing teams. There is also additional information showing what grade pupils are on at their musical instruments as well as a year 10 sports GCSE class data that shows information about pupils and their abilities in many exercises, mostly in circuit training.

 Class Pupil Number Pe bleep test autumn Pe bleep test spring Pe house run position Musical Instrument Level School Team 1 1 9.0 Abs 67 Rugby 1 2 8.3 10.1 59 Rowing 1 3 9.6 9.0 65 1 4 9.7 9.6 DNR 2 Rugby 1 5 10.0 inj 66 Rugby 1 6 8.4 10.2 79 3 1 7 9.6 10.2 34 Rugby 1 8 7.4 7.5 85 1 9 9.2 10.2 DNR 1 10 inj 13.6 3 Rugby 1 11 5.4 7.5 100 1 12 inj 9.0 55 Rugby

This is an example of the data I have used. It is from the Yr 10 data spreadsheet and shows what class a pupil is in, their number, their Pe Bleep test scores in autumn and spring, their position in the Pe house run, the school team they are in and the grade of their musical instrument that they are on. There is also extra information showing why the pupil has not performed one or more of the pieces of information. This information is shown by:

Abs: Absent

DNR: Did Not Run

Inj: Injured

This data will help me in making my three hypotheses as well as help me produce sensible ones.

Middle

Box plots are useful for comparing the mean and median scores of the bleep test as well as finding out the skewness. The skewness is like the correlation but of a set of box plots. It also shows additional information such as the lower and upper quartiles. All of this information will help me interpret my results.

In order to predict what I will see in terms of the skewness of the box plots I have made two stem and leaf diagrams: 1 for the pupils’ year 7 bleep test scores, and 1 for the pupils’ year 10 bleep test scores. This will help me compare the 2 and look for any particular shape that takes place. I have done this on Excel by sorting the data in ascending order and then taking the information to make a stem and leaf diagram.

As shown here, the year 7 diagram has most of the information near the top and the highest value is at the score: 10. But the year 10 diagram shows that most of the information is near the bottom, showing higher scores in the bleep test: 12. These diagrams show that the year ten scores are better than the year 7 scores, which helps me see what the box plots results will look like.

A set of box plots and take the form of 3 appearances. These appearances are the skewness: a measure of which end of the data most values lie. There is positive skewness- when the median is lower than the mean; negative skewness- when the median is higher than the mean; and symmetrical skewness- when the median and the mean are the same.

I think that I will see both positive and negative skewness; positive in Years 7 and 8, but negative in Years 9 and 10.

Conclusion

On the whole, the cumulative frequency graph’s shape was the same as my prediction. It shows that the non-rugby line is very inconsistent and moves up and down, it also has a very steep line between the LQ and the median. Whereas the rugby players line is very consistent as it is smooth and there are no harsh turns in the line. This shows that the rugby players achieve much more consistent scores than the non rugby players, who get a very big range of scores. This might be due to the fact that there were many more non-rugby players than rugby players, which means that the range will be big. In order to achieve the best results, I will need to get a spreadsheet only showing one class with many rugby players, to lessen the amount of non rugby and maybe obtain a more accurate result.

On the cumulative frequency graph, the quartiles drawn show a clear finding of this investigation. The lines labelled with a red pen show the quartiles and median for the rugby players. The lines labelled with black show the non-rugby players. The lower quartile of the rugby players is higher than that of the non-rugby players. The upper quartile of the rugby players is lower than that of the non-rugby players. The inter quartile range (IQR) is equal to UQ – LQ.

RUGBY: UQ – LQ = 11.5 – 9.8 = 1.7              13.5(cf)

NON- RUGBY: UQ – LQ = 9.6 – 7.2 = 2.4     20(cf)

This clearly shows that the rugby players’ scores are more consistent than the non-rugby players, which are widely spread out which is what I predicted to see.

This shows that rugby players get better bleep test scores than non-rugby players, proving my prediction correct

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