My first prediction is that as the height of a child increases so will the weight because it is logical that if a person is taller there will be more mass and therefore the person will weigh more.

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Maths Coursework

Using the data provided in the Mayfield High School data sheet I have decided to investigate the relationship between height and weight in both boys and girls from year seven through to year eleven.

The data sheet provides lots of other figures other than height and weight but I have chosen to use these values because they are both examples of continuous data that can be plotted well on a graph and can be easily compared.

Hypothesis 1

My first prediction is that as the height of a child increases so will the weight because it is logical that if a person is taller there will be more mass and therefore the person will weigh more.

To investigate this hypothesis I will plot all the data of height and weight of the whole school as a scatter graph and if the correlation is positive this will prove my hypothesis

Hypothesis 2

My next prediction is that as the year increases so will the average weight and height because the children are still developing and so as they get older they will grow taller therefore gaining weight.

To investigate this hypothesis I will find an average height and weight for each year and plot them on a scatter graph. If the graph has a positive correlation this will prove my hypothesis to be correct.

Hypothesis 3

My final prediction is that as the age of the students increases the boys will become heavier than the girls because it is generally correct to say that boys weigh more than girls because they have more muscle.

To prove this hypothesis I will plot the data for boys and girls for each year as a scatter graph and create a box plot for weight on each graph. Comparing the box plots should show the increase in weight of boys.

I will also use stem and leaf diagrams to further illustrate and represent my data.

Plan

Mayfield high has 1183 students. This amount of data is far too big to handle and so I will collect a stratified sample of 100 students.

Using a stratified sample ensures that the data is not biased because the amount of each gender and year are a fair representation of the distribution throughout the whole school.

This size sample is big enough to get a fair set of results but not too big to be too time consuming.

To collect a stratified sample I will use the following equation to find out how many people from each year should be in the sample;

number of boys/girls in year    

1183

The table below will be used to work out how many boys and girls are in each year to put into the equation.

Year 7 B 151/1183 = 0.1276         0.1276 x 100 = 13

            G 131/1183 = 0.1107        0.1107 x 100 = 11

Year 8 B 145/1183 = 0.1225        0.1225 x 100 = 12

        G 125/1183 = 0.1056        0.1056 x 100 = 11

Year 9 B 118/1183 = 0.099          0.099 x 100 =  10

        G 143/1183 = 0.1208       0.1208 x 100 = 12

Year 10 B 106/1183 = 0.0896      0.0896 x 100 = 9

         G  94/1183  = 0.079        0.079 x 100 = 8

Year 11 B 84/1183 = 0.071       0.071 x 100 = 7

         G 86/1183 = 0.072       0.072 x 100 = 7

Now that I have worked out how many people I should chose from each year I will use a random number generator to randomly pick the correct amount of people from the sheet of data. This process should provide me with a workable set of data that I can use to prove my hypothesis.

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I have used secondary data provided by the Mayfield high data sheet because it would be time consuming to collect primary data and then stratify it. Stratifying the data and screening for outliers should ensure this data is useable.

To prove my first hypothesis I will take all of my stratified data and plot it as a scatter graph using autograph.

Plotting these points on a scatter graph is useful because it shows each individual pupil and gives a full analysis of the data. After plotting all my data on the graph I will be able ...

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