• 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
13. 13
13
• 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

This student written piece of work is one of many that can be found in our GCSE Miscellaneous 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 GCSE Miscellaneous essays

1. ## For our GCSE statistics coursework, we were given the question Where are houses most ...

North 3(236,959.83-219,498) / 104055.7943 = 0.5. This shows a positive skew, for house prices in the north of England, as my histogram predicted. Box plots North LQ=�176,237.50 UQ=�288750 MEDIAN=�219,498 IQR=�43,260 South LQ=380,000 UQ=570,000 MEDIAN=487500 IQR=190000 I used box plots as from my Pearson's measure of skewness calculation I found a skewed distribution, therefore to display my

2. ## Statistics coursework

When sampling I am going to take proportional representation of male and female drivers by driving instructors too because this reflects on the whole population sample and avoids biased data. For the female population Instructor A - taught 31 female drivers which are 31 / 124 = 25 % so

1. ## Maths Statistics Coursework

We must also look at the spread of data by looking at the range and standard deviation. The ranges for each set of data are not exactly as I would have expected. In both Key Stages, upper band has the lowest range, as expected as they should be the best and most consistent at estimating in their Key Stage.

2. ## Statistics Coursework. I am going to study the wealth of countries in the ...

529 Mean 59.7 14854.0 ?d� 7231.5 ?d� = 7231.5 6 x 7231.5 = 43389 n is the number of countries in my sample, 50.

1. ## GCSE STATISTICS/Data Handling Coursework 2008

I will then be able to see how the results change as the students are older. I will analyse the distribution of 100 metre times by grouping the data into different class intervals, calculating the frequency density for each group, making a grouped data table then producing a histogram with autograph.

2. ## Data handling - calculating means and standard deviations

710 860 740 795 800 685 650 650 820 * The mean () is calculated using the equation below: = * The standard deviation () is calculated* using the equation below: = 85.43658304 (b) The table blow shows the height of 60 students after dividing each height by 5: Table

1. ## The relationship between level of parental education and SAT scores

These reports are considered very highly accurate since the college-bound population is relatively stable from year to year. Here are the URLs of the SAT total group reports from 2006 to 2010: Total group reports in 2006 http://www.collegeboard.com/prod_downloads/about/news_info/cbsenior/yr2006/national-report.pdf Total group reports in 2007 http://www.collegeboard.com/prod_downloads/about/news_info/cbsenior/yr2007/national-report.pdf Total group reports in 2008 http://professionals.collegeboard.com/profdownload/Total_Group_Report.pdf

2. ## Statistical Experiment Plan to investigate the ability to estimate 30 and 60 seconds.

If the scatter graph shows clearly no correlation then I shall reject my hypotheses. If I see some correlation or am in doubt then I will want to get a better way of measuring the correlation rather than having a mere visual look but would like a numeric value.

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