Examining Data of 60 year 10 students and 60 year nine students plus 120 year 8 student.

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Maths Coursework

I have been given data of 60 year 10 students and 60 year nine students plus 120 year 8 student. They took 4 exams at Christmas; English, French, maths and science. The results of the students were collected and put in a table ranging from A to E. The data given to me is secondary data, as I didn’t collect myself however this does not affect the reliability of the source as it was collected my people from ccea. I have chosen to use the year 8 data, as it would give e a better sample of data, which would help in validating my hypotheses.

 I theorise that boys who are good at science will be good at maths because for both subjects require logical thinking, as opposed to English which needs creative thinking.

However based on this it highly possible that girls good at English will be bad at will be bad at science as English needs creative thinking rather than logical thinking. To help validated my theories I will use scatter diagrams to assess if there is any correlation between maths and science. Since this happens to be discrete data i.e. data which consists of a set of separate numbers, I therefore could use scatter diagrams or frequency polygons to observe if the is any relationship between maths and science. The data collected is also “raw data”. To assess the validity of my hypotheses it would be wise to use the three averages; mean, mode, and median as they would show the most frequently occurring number, the overall average of the data once added up and divided by the number of items of data.

I would also consider using interquartile range as I could then see the relationship of the data through box and wisker plots and or cumulative frequency diagrams. However for cumulative frequency diagrams I would need to group the data. Using box and wisker plots has a number of advantage; as you can clearly see if there is a negative or positive skew in the data; the main advantage however is that box and wisker plots are brilliant for comparative purposes. As the data is far too large to handle, I'm going to use “stratified random sampling”, to get easier results. I will need to choose I value for my sample, as a result I’m going to choose 30 as that is the first number I thought to give me accurate results. I figured out if I needed to use random sampling by this table:

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I added up the girls and boys in each grade group, and then I totalled each column and row.

I found that this sample is bias as there are fewer boys than girls. Subsequently I shall divide each grade group for the girls by 56 and divide the boys grade groups by 64.

Girls

A 12÷56x30 =6.4

                 =6

B 11÷56x30 = 6

C 8÷56x30=4.3

              =4

D 10÷56x30=5.4

            ...

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