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Data Handling, Mayfield High School

Extracts from this document...

Introduction

Introduction:

What is my investigation about?

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

...read more.

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

...read more.

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

...read more.

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