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• Level: GCSE
• Subject: Maths
• Word count: 1113

# Mayfield high school. We will investigate the correlation between height and weight throughout the school. These two variables are examples of quantitative data, this will allow us to use advanced statistical techniques. We will be able to extend our rese

Extracts from this document...

Introduction

 Chantel Sullivan
 Mr Paul Statistics Coursework…

Contents.

## Planning…Page 1

Page 2

Collecting Data…Page 3

Page 4

Page 5

Planning.

Aim:

We plan to investigate the sample of data from the factious school called Mayfield High School. We plan to conduct a statistical investigation, which will determine whether our hypothesis is right or wrong.

Population:

We will take a sample of 40 pupils from the school by using the stratified method. This sample will lead us to determine different conclusions about students, whether it is similarities or differences.

Variable:

We will investigate the correlation between height and weight throughout the school. These two variables are examples of quantitative data, this will allow us to use advanced statistical techniques. We will be able to extend our research further by looking at the correlation within each year group.

Hypothesis:

I believe that the taller a person is, the more they will weigh. This is because when the more you grow the more you need to eat to satisfy you, also bones and muscles get bigger which all adds up.

Middle

Female

1.59

54

0.0168

3

7

Miles

Amanda

12

9

Female

1.46

40

0.021749

4

7

Dickinson

Antonia

12

5

Female

1.51

50

0.023224

5

7

Brown

Mia

12

2

Female

1.48

39

0.028049

6

7

Sing

Asha

12

9

Female

1.43

45

0.033333

We sorted the students into year groups, and gender then we sorted the data by random number, this randomised the results but inside each year group and gender...

From this we were able to take a stratified sample. For each year we calculated a stratum sample size. We did this by taking the total in the year, and dividing that by the total number in the school, then dividing that by the sample size, which in this case are 40.

Conclusion

### Y Mean

X Standard Deviation

Y Standard Deviation

Correlation Coefficient

1.632

52.35

0.1242

13.55

0.4725

The results from the graph show these five different things.  These findings support my theory that the taller you are, the more you weigh as they indicate a stronger correlation between the two axis and the mean’s look accurate and look like they support my hypothesis aswell.

From my stratified sample, I can find out the inter-quartile range for both weight and height. The lower quartile for my height data is 1.52. The upper quartile for my data is 1.69. To calculate the inter-quartile range, you have to use the formula…

upper quartile-lower quartile= inter-quartile range

So, 1.69 – 1.52 = 0.17 = IQR

0.17 x 1.5= 0.255

UQ= 1.69+0.255= 1.945m

LQ= 1.52-0.255=1.265m

These are the boundaries for outliers, for heights.

These are the outliers for the height.

The lower quartile for my weight data is 43. The upper quartile for my data is 62. To calculate the inter-quartile range, you have to use the formula…

upper quartile-lower quartile= inter-quartile range

So, 62-43=19=IQR

19 x 1.5 = 28.5

UQ= 62 + 28.5 = 90.5kg

LQ= 43 – 28.5 = 14.5kg

These are the boundaries for outliers, for weights.

This is the outlier for the weight.

I will remove these from my data and produce a new scatter graph.

Chantel Sullivan

This student written piece of work is one of many that can be found in our GCSE Height and Weight of Pupils and other Mayfield High School investigations section.

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