• 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

Factors influencing girls athletic performance throughout secondary school.

Extracts from this document...

Introduction






















image00.pngimage01.pngimage02.png

Introduction

Athletics data has been collected for a number of years at Colchester County High School.

Colchester County High School is a selective school for girls in the Colchester district.  This means that it is not representative of the whole population.  Upon entry to the school, forms are chosen on the basis of musical, sporting and academic talent from previous years in primary school.  This means, that in theory, all the forms that are the outcome of one selective test should be equal in sporting ability.  

However, this is not to say that they would be equal in athletic activity, as in primary school, most pupils play sports such as netball, hockey, tennis and rounders.  Even primary schools that do some athletics do more common things like the 100m run, and long jump.  Most primary schools do not teach the athletic events such as 1500m or discus.  Girls that are good at sport are not necessarily good at athletics, and vice versa.  Also, girls whose schools do teach athletics are clearly priveliged.

This data is available to the pupils through the maths and sports departments.  The data includes times for running various distances and distances for long jump high jump and triple jump.  The data also includes distances that the girls can throw the rounders ball, discus and shot.

This data is to be treated as though it were primary data, as it is from a reliable source.  The physical education staff record the data, and sometimes the data is collected by the pupils themselves.  

Although the data is from a reliable source, it is essential to recognise that human error can be a factor in this data.  The data could have been “measured” inaccurately or recorded mistakenly.

...read more.

Middle

                        n

This shows how the data is spread in relation to the mean.

(2.15 – 2.70)2 + (2.16 – 2.70)2 + (2.17 – 2.70)2 etc.

                                  74

        =   0.3025 + 0.2916 + 0.2809 etc.

                                74

                = 37.89871

                        74

                = 0.512145m

The mean and standard deviation are in metres and have been rounded to three decimal places for ease.  The standard deviations show that the data for year 8 is the most widely spread and the data for year 9 is the closest together.  

The highest mean is for year 9 and the smallest standard deviation is also for year 9.  This means that year 9 peaked in long jump.  This may not be true for all the girls shown in the histograms or even for all girls, but the general population.  

The lowest mean was for year 7, but this did not have the highest standard deviation.  Year 8 has the highest standard deviation.  Both year 7 and 8 have fairly similar performances (2.70m and 2.78m).  This shows that their jumping ability did not really improve in these years.  This seems quite likely, as there is only one hour lesson in a year for long jump for the girls to perform in, and you can not expect girls to improve so much then.

The histograms are also fairly similar for year 9 and year 10, but year 9’s results are a little better, as they are more negatively skewed.  This also shows that the performance that peaked in year 9 slowly decreases in year 10.

Year 7

Class

Frequency

Class Width

Frequency Density

1.25  l  2.0

4

0.75

5.3

2.0  l  2.4

13

0.4

32.5

2.4  l  3.0

27

0.6

45

3.0  l  3.5

26

0.5

52

3.5  l  4.1

3

0.6

5

4.1 l 4.6

1

0.5

2

SD

0.512145

MEAN

2.783243

Year 8

Class

Frequency

Class Width

Frequency Density

1.25  l  2.0

4

0.75

5.3

2.0  l  2.4

11

0.4

27.5

2.4  l  3.0

26

0.6

43.3

3.0  l  3.5

28

0.5

56

3.5  l  4.1

6

0.6

10

4.1 l 4.6

1

0.5

2

SD

0.561594

MEAN

2.860395

Year 9

Class

Frequency

Class Width

Frequency Density

1.25  l  2.0

3

0.75

4

2.0  l  2.4

5

0.4

12.5

2.4  l  3.0

25

0.6

41.67

3.0  l  3.5

29

0.5

58

3.5  l  4.1

9

0.6

15

4.1 l 4.6

1

0.5

2

SD

0.491349

MEAN

2.9876

Year 10

Class

Frequency

Class Width

Frequency Density

1.25  l  2.0

2

0.75

2.67

2.0  l  2.4

10

0.4

25

2.4  l  3.0

23

0.6

38.3

3.0  l  3.5

26

0.5

52

3.5  l  4.1

11

0.6

18.3

4.1 l 4.6

2

0.5

4

SD

0.543238

MEAN

2.951216

...read more.

Conclusion

This hypothesis, like my last one suggests that the athletic peak for a pupil be during year 9.  This may be biased for the dozens of reasons outlined in the introduction.  We have no idea how the results would vary in a non-selective mixed school, non-selective boys/girls schools; selective mixed school or a selective boy’s school.  It would also differ greatly from a school that focused more on sport.  

One of the logical reasons for why the athletic peak may be in year 9, is that apart from year 10s, they are the eldest, and the Year 10s are under so much more pressure due to GCSE work, leaving less time for sport, etc.

It also possibly means that year 10s cannot concentrate as much when they are doing athletics because of the pressure of coursework.  It is also possible that this year is not representative of other years that may continue getting better.  There is however, only a certain amount that a teenager can improve before they reach the best of their ability and can only stay the same or get worse.  This was referred to as the “peak” and achieved by most in CCHS during year 9.

...read more.

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics 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

See related essaysSee related essays

Related AS and A Level Probability & Statistics essays

  1. Mayfield High School Maths Coursework

    5 103 5 4 4 100 4 4 4 103 4 4 5 100 4 4 4 98 4 4 5 92 3 3 4 My Graph From my samples I am going to create a graph.

  2. Investigating the Relationship Between the Amount of Money a Football Club Receives and its ...

    12 6 5 46 23 10 7 6 32 27 79 21412 �1,006,000 28 2 Reading 11 46 10 6 7 29 26 6 7 10 25 37 61 24200 �2,500,000 -9 2 Stoke City 8 46 10 4 9 32 32 11 2 10 27 31 69 24054 �2,500,000

  1. "The lengths of lines are easier to guess than angles. Also, that year 11's ...

    I now have to fins the square root of this to find the final standard deviation. The square root is 6.5. This means the spread is smaller than the spread of the year 9's estimates of angle 6, but the year 11's had a lower spread of data for the line estimates.

  2. Statistics coursework

    Although, it is clear you are more likely to find boys with a very high or very low IQ whereas girls are more likely to be within a narrower band. To see if a clearer picture can be given I have next decided to draw a stem and leaf diagram to compare year 11s data.

  1. Anthropometric Data

    be due to the fact that the scale of the axis's, from this u can have an impression on the where the line passes. This show a accurate number of mean point of foot length being 140 (mm) and foot breadth being 80 (mm).

  2. Statistics. The purpose of this coursework is to investigate the comparative relationships between the ...

    I should then divide by one less than the sample size, followed by taking the square root of this number. I will try to demonstrate this using the Porche example, for number of owners: no. of owners = 77 77/46 = 1.68 no.

  1. Frequency curves and frequency tables

    * Discrete data can only take certain values. For example, the shoe sizes can only be a whole shoe size or a half size (4, 5.5, etc), the number of houses built must be a whole number. * Continuous data can take any value. For example, when measuring heights or times, there can always be another value in between any two measurements.

  2. AS statistics coursework - correlation coefficient between height and weight in year 11 boys ...

    2.40 2916 83.7 1.77 72 3.13 5184 127.44 1.91 62 3.65 3844 118.42 1.68 50 2.82 2500 84 1.80 72 3.24 5184 129.6 1.74 64 3.03 4096 111.36 1.62 63 2.62 3969 102.06 1.81 54 3.28 2916 97.74 1.74 50 3.03 2500 87 1.70 57 2.89 3249 96.9 1.5 35

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