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Statistical investigation into pupils at Mayfield high school

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Introduction

STASTICAL INVESTIGATION INTO PUPILS AT MAYFIELD HIGH SCHOOL

AIM

In this investigation I have set out to explore two hypotheses regarding secondary data supplied to me concerning pupils at Mayfield High School in years 7 to 11.  The secondary data supplied included data on a range of issues including each pupil’s weight, their IQ and the average number of hours each pupil watched TV per week.  My hypotheses below explore specifically these elements from the secondary data supplied to me.

Hypotheses 1 – Correlation between time spent watching TV and weight

My hypothesis is that there will be a connection between the average number of hours spent watching television and the weight of each pupil.  My prediction is that there will be a positive correlation: i.e. that the heavier pupils will be those who spend more hours watching TV.

The reason for my prediction is that I believe that more time spent in front of the television means that there is less time for activity and, therefore, TV watchers are more likely to put on weight.  I also think that TV watchers are more likely to eat snacks and junk food while watching TV, which would also result in weight gain.

In my conclusion below, I consider outside factors which I have not taken into account in the investigation of this hypothesis which may influence my results.  Such factors are: height, gender, age, method of travel.

Hypotheses 2 – Correlation between time spent watching TV and IQ

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Middle

131

131/1183x50

6

8

125

125/1183x50

5

9

143

143/1183x50

6

10

94

94/1183x50

4

11

86

86/1183x50

4

Boys

Year

No. in Year

Calculation

No. of pupils taken to create sample

7

151

151/1183x50

6

8

145

145/1183x50

6

9

118

118/1183x50

5

10

106

106/1183x50

4

11

84

84/1183x50

4

I created, from my secondary data, separate charts for each gender and year group and numbered each pupil within that group according to their alphabetical order. To ensure that there is no bias, the actual pupils selected were selected by a random number generator which was a function available in Excel.  The random number generated was always less than or equal to 1.  I, therefore, multipled each number generated with the number of pupils in each gender and year group to ensure that each pupil was capable of being selected.  If the same number in the same group was generated twice, which did happen once, I selected another random number using the random number button on my calculator and, again, multiplying it by the number of pupils in that gender and year group.  

In this way I produced my stratified sample of 50 pupils which I have set out at Appendix 1.  The number column in appendix is the number of the pupil in that year and gender group, assigned through the random number generation process.

Analysis of Stratified Sample

Because both of my hypotheses predict a connection between the two sets of data in each hypothesis, I decided that, for each hypothesis, I should present the data on a scatter graph.

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Conclusion

It must be remembered that these results have been drawn from a sample only.  More conclusive results are likely to be obtained from a larger collection of data than the 50 in my sample.

I am surprised by my finding of a negative correlation in the TV v Weight comparison.  Possible explanations for such a correlation, if it is genuine (ignoring factors such as dirty data and limited sample), may include the fact that there is a lot of sport on television and it is possible that the more athletic pupils watch it.

However, my investigations into both hypotheses were limited and always unlikely to have proved a link between the two pieces of data in each case, and even less likely, a direct causal relation.  For instance, I did not take into account external factors which could have affected or improved my results.

External factors which I could have taken into account to improve both my investigations include factors like age, height, gender, method of travel to school and programmes watched.  Most of these factors were available to me in data form on the secondary data supplied to me.  I would have to have done further primary investigation (through e.g. questionnaires) into a breakdown of types of programme watched, if I wanted to discover further possible explanations for any correlations found.  A more conclusive investigation is likely to result if this additional material is used and taken into account, which may support a direct causal relationship.

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