This graph is showing the grades against the Alphabetical positions base on English Language and Literature results only. The line of best fit on this graph is useless because we can’t see any correlation so the line does not make any sense which has proved that it doesn’t matter where your name is on the alphabetical list it depends on how you work hard.

This is the same kind of graph like my first (the one above) graph but this graph is showing the grades against the Alphabetical positions base on Mathematic results only and as we can see on the graph there isn’t any trend (line of best fit) on the graph, so placing one there will be useless.

After seeing that the two graphs I have just made disprove my hypothesis already I decided to now create graphs that are against different subjects with the main 3 subjects which are Maths, English Language & Literature and Religious studies than using the alphabetical numbering that I assign on each student.

The data below shows the total grade point score of each student both on their maths and English Language/Literature also with the calculated correlation but without adding the students that had 0 on their grade points in total.

How to calculate the Correlation Coefficient

Base on my knowledge I have been taught in maths a positive correlation is an association between two variables, if an increase in one variable results in an increase in the an approximately linear manner. To find the strength of the association between the correlation you have to use the correlation coefficient (r) that it’s been range from 0 and 1 and that will be a positive correlation but if its from 0 to -1 then it becomes a negative correction. The correlation coefficient value of 0 suggests that there is no correlation while if the correlation coefficient value is 1 it suggests that there is a perfect positive correlation. For example the English ft maths result only graph shows a strong positive correlation.

The formula I used on Microsoft excel to calculate correlation between the English and Maths is ‘=CORREL(C10:C177,D10:D177)’, this shows that there is strong coefficient correlation between the subjects because it shows that a student doing well in his or her English class is also doing well in his or her Maths class.

This graph shows the total score for English against the total score for Mathematics for each student. This graph is showing a positive correlation and the line of best fit is correct and this time around it’s clearer. The two points on the 0 axis shows that the students dint do the exams (did not turn up on the day of the exam) or probably had a grade ‘U’ on their GCSE examination

The data below shows the total grade point score of each student both on their mathematics and Religious Studies also with the calculated correlation but without adding the students that had 0 on their grade points in total.

The formula I used on Microsoft excel to calculate correlation between the Mathematics and Religious Studies is ‘=CORREL(C10:C172, D10:D172)’, this shows that there is strong coefficient correlation between the subjects because it shows that a student doing well Mathematics also performs very well is Religious Studies.

The graph below shows the total score for Mathematics against the total score for Religious Studies for each student. This graph is showing a strong positive correlation coefficient and the line of best fit is very strong. This graph shows that the overall marks had in Maths where similar to their Religious Studies marks as well which very strange because there are two completely subjects and criteria’s.

Mode is the most number that reoccur in a data more than other numbers. So Base on my data that I collected what I did was I find the mode (most occurring number) of the data which I used the Microsoft excel functions to find the mode easily. The formula function that I used on Microsoft excel to find the mode was ‘=MODE(C10:AF195)’. As you can see below the most occurring number in my data was 40 which converted to a GSCE grade is a grade ‘C’. So this shows that from GCSE result in my secondary school 2010 most students had C’s.

Mean is the average of the numbers. It is easy to calculate because all you have to do is add up all the numbers, then divide by how many numbers there are. In other words it is the sum divided by the count. Base on my data I used the Microsoft excel to calculate my mean for me because my data contains of so many numbers, so the formula function key I used on Microsoft excel to calculate my mean was ‘=AVERAGE(C10:AF195)’ and as you can see below my mean average for my data base on the GCSE result 2010 for my secondary school was ‘42.58083’.

Median is the 'middle value' in the list. When the totals of the list are odd, the median is the middle entry in the list after sorting the list into increasing order. When the totals of the list are even, the median is equal to the sum of the two middle (after sorting the list into increasing order) numbers divided by two. To get the right median it is a must that you must arrange the numbers on the list in ascending order so as to get a right value, and the middle number is the median. To get the median on my data I used the Microsoft excel formula function ‘=MEDIAN(C10:AF195)’ so as you can see below the median number on my data is ‘40’.

Standard deviation is a measure of how widely values are dispersed from the average value (which is the mean). To calculate the standard deviation my data I used the Microsoft formula function which was ‘=STDEVA(C10:AF195)’ which as you can see below my standard deviation for my data is 9.5003691.

Conclusion

Having analyzed my data base on the 2010 GCSE results from my secondary school, I have disprove my hypothesis because having looked at the graphs the mean, the mode, the median and the correlation from my data, it shows that it did not matter where a student was placed on the register alphabetically because my hypothesis where meant to be showing a negative correlation but instead it showed a positive correlation and some of the graphs did not show any correlation at all. So my hypothesis was wrong because student being at the bottom of the alphabetic register did much well as the students with their names being at the top of the alphabetical rooter. Even though I tried to prove the point that students might do very well in math’s and be really bad in Religious studies did not also work because the graph turned up to be a positive correlation as well disproving me that in my secondary school the GCSE result 2010, the students where multi-covered in all the areas of the department in school leading them to do well in all their subjects so as to balance out and come out with good GCSE results in all their subjects.