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# Data Handling, Mayfield High School

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Introduction

Introduction:

My second coursework for my Mathematics GCSE course is based on statistics. I have been provided with data for Mayfield High School. Mayfield is a fictitious High School but the data is based on a real school.

Mayfield has 1183 students from years 7 to 11. The data that is provided on each student includes, Name, Age, Year Group, IQ, Weight, Height, Hair Colour, Eye colour, Distance from home to school, usual method of travel to school, number of brothers and sisters, key stage 2 results in English, Mathematics and Science.

 Year Group Number of Boys Number of Girls Total 7 151 131 282 8 145 125 270 9 118 143 261 10 106 94 200 11 84 86 170 Total 604 579 1183

This is the data given to us on the main question sheet. We had to go and retrieve the data for each pupil from the schools network in order for us to carry out the investigation.

There are a number of possible lines of enquiries that we could carry out. For example:

1. the variations in hair colour
2. the variations in eye colour
3. the relationship between the hair colour and the eye colour
4. the distances travelled to school
5. the relationship between the height and the weight
6. the relationship between two sets of Key Stage 2 results
7. the relationship between IQ and Key Stage 2 results
8. the height to weight ratio in terms of body mass index
9. the relationship between the number of hours of TV watched and the IQ
10. the relationship between the gender and the IQ

Middle

Male

1.43

41

I used the same method for each year right the way through from the remaining, Year 8 to Year 11 for both the girls and the boys.

Year 8:

From year 8 I need four boys and three girls.

Girls:

• (Ran#) x 125
 Number of Tries Random Number Number (nearest whole number) 1 100.625 101 2 66.875 67 3 39.75 40

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 40 8 Dom Kate Female 1.59 50 67 8 Indera Emily Sophia Female 1.52 45 101 8 Neelam Kate Female 1.45 81

Boys:

• (Ran#) x 145
 Number of Tries Random Number Number (nearest whole number) 1 46.4 47 2 53.215 53 3 66.12 66 4 72.5 73

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 47 8 Fahmed Ali Male 1.61 48 53 8 Gore Mike John Male 1.63 56 66 8 Jarvel Kenneth Male 1.66 46 73 8 Kevill Dean Michael Male 1.52 43

Year 9:

From year 9 I need four girls and three boys.

Girls:

• (Ran#) x 143
 Number of Tries Random Number Number (nearest whole number) 1 73.931 74 2 127.556 128 3 75.075 75 4 11.44 11

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 11 9 Bellfield Janet Female 1.58 40 74 9 Jones Sarah Ann Female 1.53 40 75 9 Jones Samantha Louise Female 1.62 45 128 9 Smith Anjelina Louise Female 1.50 45

Boys:

• (Ran#) x 118
 Number of Tries Random Number Number (nearest whole number) 1 100.654 101 2 74.222 74 3 55.106 55

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 101 9 Simons Jack Male 1.64 59 74 9 Laters Richard Tang Male 1.69 65 55 9 Huggard Malcolm Male 1.52 52

Year 10:

From year 10 I will need two girls and three boys.

Girls:

• (Ran#) x 94
 Number of Tries Random Number Number (nearest whole number) 1 39.95 40 2 74.448 74

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 40 10 Hall Jane Samantha Female 1.51 36 74 10 Scampion Stephanie Female 1.55 60

Boys:

• (Ran#) x 106
 Number of Tries Random Number Number (nearest whole number) 1 25.122 25 2 48.442 48 3 55.014 55

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 25 10 Chung Jason Male 1.71 56 48 10 Hunt Gareth Barry Male 1.72 62 55 10 Kaura Karan Kaz Male 1.66 63

Conclusion

Number (nearest whole number)

1

33.516

34

2

16.044

16

There was some rounding involved which would have introduced some bias into my results also some numbers were repeated which meant that I had to ignore it and redo it.

 ID Year Group Surname Forename 1 Forename 2 Gender Height (m) Weight (kg) 34 11 Hawkins Tim Male 1.62 63 16 11 Cripp Justin Carl Male 1.67 50

Now that I have picked out the 30 students who I will investigate, I will group them together and I will present them in a data capture sheet.

Skewness:

Skewness is a measure of the asymmetry of the data around the sample mean. If skewness is negative, the data are spread out more to the left of the mean than to the right. If skewness is positive, the data are spread out more to the right. The skewness of the normal distribution (or any perfectly symmetric distribution) is zero. In my investigation I will use the skewness to see the strength of the normal distribution.

Data I will use:

I now gathered all my results into one table which will make it easier to group. I assigned new id’s to each individual which would mean that if I ever need to refer to the student, writing his/her full name will take up unnecessary time. This new ID will make it easier to identify each individual.

 New ID ID Surname Forename Forename 2 Sex Height (m) Weight (kg) 1 18 Carney Esther Female 1.50 44 2 54 Higgins Joanne Alicia Female 1.50 45 3 69 Kelly Jenifer Fay Female 1.30 45 4 17 Bingh Daniel Male 1.56 35 5 20 Bond James Sean Male 1.47 50 6 97 McKracken Phil Peter Male 1.58 48 7 119 Sharpe Billy Richard Male 1.43 41 8 40 Dom Kate Female 1.59 50 9 67 Indera Emily Sophia Female 1.52 45 10 101 Neelam Kate Female 1.45 81 11 47 Fahmed Ali Male 1.61 48 12 53 Gore Mike John Male 1.63 56 13 66 Jarvel Kenneth Male 1.66 46 14 73 Kevill Dean Michael Male 1.52 43 15 11 Bellfield Janet Female 1.58 40 16 74 Jones Sarah Ann Female 1.53 40 17 75 Jones Samantha Louise Female 1.62 45 18 128 Smith Anjelina Louise Female 1.50 45 19 101 Simons Jack Male 1.64 59 20 74 Laters Richard Tang Male 1.69 65 21 55 Huggard Malcolm Male 1.52 52 22 40 Hall Jane Samantha Female 1.51 36 23 74 Scampion Stephanie Female 1.55 60 24 25 Chung Jason Male 1.71 56 25 48 Hunt Gareth Barry Male 1.72 62 26 55 Kaura Karan Kaz Male 1.66 63 27 57 McCreadie Billie Crystal Female 1.63 38 28 20 Buyram Dawn Elizabeth Female 1.65 42 29 34 Hawkins Tim Male 1.62 63 30 16 Cripp Justin Carl Male 1.67 50

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