• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month
• Level: GCSE
• Subject: Maths
• Word count: 3854

# Mayfield Coursework

Extracts from this document...

Introduction

Page  of

## Maths Coursework/Mayfield High School

In my Mayfield High coursework I am going to do three hypotheses. The first one is about the correlation between height and weight. I believe that the taller the person is, the heavier he is. My second hypothesis is a prediction to show that an older person is taller than younger people. Finally my third hypothesis will show that on average boys are taller than girls.

My hypotheses are:

• A = There is a positive correlation between height and weight.
• B = On average the students’ heights will become greater as they age.
• C = On average boys are taller than girls.

### Data

For both hypotheses I am going to use the Mayfield high school data. This specific data is secondary data as I have not personally taken it myself, another source ahs done it instead. It consists of the following:

• Year Group
• Surname
• Forename
• Age (Years and months)
• Gender
• Hair colour
• Eye colour
• Left/right handed
• Favourites (colour, type of music, subject, TV programme)
• Average number of hours TV watched per week
• IQ
• Height (m)
• Weight (kg)
• Distance between home and School
• Means of travel to school
• No. of siblings
• No. of pets
• KS2 results (Maths, English, Science)

I am going to get rid of all the unnecessary data, such as IQ and Favourite colour, as they are useless in my work. To take a sample from the data I am going to use STRATIFIED sampling. I will RANDOMLY take 20% from the boys from each year group and another 20% from the girls from each year group. This is so I can get the least bias and most accurate sample. This is so the result will be more accurate and be as fair as possible. If I just used a quota sample, where the population is divided into groups (gender, age, sex etc) and given number (quota)

Middle

10

106

94

21

19

11

84

86

17

17

Total

604

579

121

116

I worked out the sample by first finding out how many boys and how many girls there were in each year group. I then used the formula, =name of box*0.2, in each of the boxes in the “Amount of Sample” area. I had to round the amount of sample, as I cannot have a half of a person.

On each scatter diagram below it shows the equation to make the product moment correlation coefficient. For me to produce the actual result of the product moment correlation coefficient I would need to square root “R2”. The diagrams also show the correlation between the two sets of data, making it possible to compare them.

These are all my samples:

### Hypothesis 1

(There is a positive correlation between height and weight)

I believed that there is a positive correlation between height and weight. As you can see that on all of the graphs there is a positive correlation with a few of them (Yr 7 and 11 Boys) being a strong positive correlation.

The correlations between the height and weight are fairly strong. You can see this as the line of best fit is positive. This means that as the height of one person increases, their weight also increases; varying on the amount they grow.

A good example is of the Yr 11 boys’ graph. It shows that as the height increases, so does the weight.

This is an example from the Yr 11 boys’ graph; it shows that the person who is 1.78m, which is the smallest, tall weighs 37kg, which is also the smallest and the person who is 2.03m, which is the biggest, weighs 86kg, which is the biggest.

This clearly shows that there is a positive and fairly strong correlation between height and weight.

 Height (m) Weight (kg) 1.78 37 1.82 66 1.85 73 2.03 86
 Year Gender Spearman’s rank Correlation Coefficient 7 Boys 0.664 Girls 0.296 8 Boys 0.149 Girls 0.136 9 Boys 0.148 Girls 0.173 10 Boys 0.170 Girls 0.275 11 Boys 0.707 Girls 0.329

The above table shows the Spearman’s Rank Correlation Coefficient for each year and it is split into both of the genders. This is worked out by using the Product Moment Correlation Coefficient (on the top left hand corner of each graph) and square rooting it and rounding it to 3 decimal places.  From the table you can see that the lowest figure is 0.136 and the highest is 0.707, which is a big difference in terms of Spearman’s Rank Correlation Coefficient. A very weak negative correlation would be -1, a balanced normal correlation would be 0 and a very strong positive correlation would be 1. 2 of the graphs have a fairly strong positive correlation, Yr 7 boys and Yr 11 girls. The rest of the figures have a weak positive correlation.

### Hypothesis 2

(On average the students’ heights will become greater as they age)

I will prove that on average the students’ heights will increase. Below are the graphs for boys and girls that show a correlation between year 7 and 9 and year 9 and 11. This will show that the height does increase as the students’ age. For the samples I will obtain them from both genders.

The above diagram is of two box and whisker diagrams from Yr 7 heights and Yr 9 heights. From this you can see that on average, the Yr 9 heights are greater than the Yr 7s, indicating that their age has increased through the two years. I have used box and whisker diagrams to make it easier to compare both sets of data.

The two histograms above are from the two sets of data, Yr 7s heights and Yr 9 heights. From this you can also clearly see that the Yr 9s median height is greater than the yr 7s. The Yr 9s also have a smaller percentage for the lower quartile.

 Yr 7 Stats Yr 9 Stats Lower Quartile 1.45 1.54 Upper Quartile 1.62 1.66 Median 1.54 1.58 Standard Deviation 0.112095 0.0780745

Conclusion

### Evaluation

Overall, I think I have given accurate and reliable results for my investigation. I have taken out the outliers, used graphs to examine every hypothesis, and accurately calculated other figures like standard deviation and the mean. This helped me to identify correlations between the different data types and to provide me with reliable conclusions.

To improve my investigation, I would have used more factors from the Mayfield data, such as more pupils or from other schools. This would have given me more accurate results, because there would be more information to look at. Apart from that it would have been easier to identify any patterns and if I had more data to analyse I would have provided with more precise results

Apart from that I could have further developed my investigation by adding more hypotheses to examine other factors.

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.

## 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

# Related GCSE Height and Weight of Pupils and other Mayfield High School investigations essays

1. ## A hypothesis is the outline of the idea/ideas which I will be testing and ...

Here is another copy of the two-way table of the table, which helps me form my sampling method. 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

2. ## Mayfield. HYPOTHESIS 1: Boys at Mayfield School are Taller and Weigh more on ...

appropriate field and below Is a diagram of for the Cumulative Frequency which will help me identify percentiles and trends in my data. The graph will also include a line which helps me identify the trend clearly 2) BOYS WEIGHT BOYS Weight(kg)

1. ## Conduct an investigation comparing height and weight from pupils in Mayfield School.

For year 11 I again hypothesised that the taller you are the more you are likely to weigh, I thought if this was the case for student in year 7 then it definitely would be the case for year 11 student because there is going to be a more vaster

2. ## Statistics coursework Edexcell

Evaluation Through out this investigation boys seem to higher vales in weight and height compared to girls. But the Girls seem to have a smaller year nines compared to year eights and year seven girls have a higher range compared to the year nine girls (box and whisker diagram).

1. ## Statistics Coursework

I separated my original sample into the two genders. As there were only three marks obtained, there were only four possible percentages, 0%, 33%, 67% and 100% (as I rounded results to the nearest whole percentage). When I had obtained the results, I calculated the mean, mode and median of

2. ## Liquid chromatography is a technique used to separate components of a mixture to isolate ...

With sodium benzoate however, the efficiency remains steady between 2�l and 100�l, there is then a ~5% decrease between 100�l and 200�l, followed by ~17% decrease between 200 and 350�l and a ~13% decrease between 350�l and 750�l. The value for 1000�l and above is ignored, as the peak is very small.

1. ## Statistics GCSE Coursework. Height and weight of pupils. The sampling method I am ...

This positive correlation along with the positive correlation shown on the scatter graph, proves that from my pilot study there are grounds for further investigation. The main study After the results of my pilot study, I have proven there

2. ## I would like to know whether there is a link between ability in Maths ...

Female 4 5 7 Female 4 4 7 Female 4 4 7 Female 4 5 7 Female 4 5 7 Female 4 5 7 Female 4 5 7 Female 4 4 7 Female 4 4 7 Female 4 4 7 Female 4 4 7 Female 4 5 7 Female 4

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