I will then go on to do a cumulative frequency diagram this will show me a spread of my data. I will then find the upper quartile, lower quartile and the median. By doing this I will do a box and whiskers diagram using the box and whiskers I will compare my results.
Once I have done all this for the mixed data I will extend my investigation by following the same procedure as above. But choosing a sample of 25 males and 25 females using the systamatic sampling method.
I
I am now going to sort my data for Attendence (%) and GCSE maths (%) using a tally chart.
Tally chart for mixed data
I have found there is only 1 student who has attendance between
50>Att<60.
The majoraty of students have an attendence beween 80>Att<90.
Tally chart for mixed data
I have found there is no student who has GCSE maths (%) between 20>Mth<30 and 80>Mth<90.
The majoraty of students have a gcse maths (%) beween 50>Mth<60.
Here is a bar chart for my Mixed sample of data for
Attendence (%). I have analysed and found the modal class interval for attendence (%) is between 80>Att<90. 9 students have this maodal attendence.
Here is a bar chart for my Mixed sample of data for GCSE Maths (%). I have analysed and found the modal class interval for GCSE Maths (%) is between 50>Mth<60. 9 students have this maodal GCSE Maths (%).
Here is a frequency polygon for my Mixed sample of data for
Attendence (%).I have used the frequency polygon to show the mid point for each interval and show the trend of the attendence (%).
Here is a frequency polygon for my Mixed sample of data for GCSE Maths (%). I have used the frequency polygon to show the mid point for each interval and show the trend of the GCSE Maths (%).
After observing my data for my mixed sample, I have come up with a hypothesis.
My hypothesis is “the higher your attendence (%) the better GCSE score (%)”.
This shall be my general hypothesis.
The investigation shall be extended to prove my hypothesis.
I have put my data into a scatter graph.
Scatter diagrams shows the relationships between attendence (%) and
GCSE score (%)”.
From looking at the scatter graph I can see a direct link between the two variables. The higher the students attendence (%) is, the better GCSE score (%) they have.
The investigation shall be extended to prove my hypothesis. I have chosen another two sets of data for Males and Females.
I have chosen to systamatically pick a sample of 25 by picking every 8th student from the data.
200/8 = 25
I have chosen a sample of 25 Males and 25 Femlaes using systamatic sampling. I have chosen every 8th male and every 8th Female.
Tally chart for Male data- Attendence (%)
I have found there only 1 male student who has Attendence (%) between 60>Mth<70.
The majoraty of male students have a Attendence (%) beween 80>Mth<90.
Tally chart for Male data GCSE maths (%)
I have found there only 1 male student who has GCSE maths (%) between 60>Mth<70 and 70>Mth<80.
The majoraty of male students have a gcse maths (%) beween 50>Mth<60.
Tally chart for Female data- Attendence (%)
I have found there only 2 female students who has Attendence (%) between 50>Mth<60.
The majoraty of female students have a Attendence (%) beween 80>Mth<90.
Tally chart for Male data GCSE maths (%)
Conclusion
After carrying out this investigation I have proved my “general hypothesis” to be correct. I have concluded that as the attendence of a pupil increases, the chance that they will get a higher GCSE score wil as increase.
Evaluation
After carrying out this investigation I have found that I could have improved the accuracy of my data by using primary data. I would use primary data because it is first hand therefore more accurate and it will be to date. The data I used was secondary data this makes it innacurate. Also I could have compared attendence with the final GCSE score of students. Also the sample size diidnt reflect the data, as it was too small. But if the sample that was used were bigger it would have made the investigation too complecated.