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  • Level: GCSE
  • Subject: Maths
  • Word count: 5980

Mayfield High School

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GCSE Statistics Coursework Introduction: Mayfield High School is a fictional secondary school where all the students are surveyed about their body, habits, likes and dislikes. My task will be to test my hypotheses using a variety of statistical techniques and analysing my findings. The data I have been provided with is secondary data. This is data previously gathered by someone else and has been made and accessible or has been published so that it can be used by someone else. This therefore means, it is not primary data- which is data collected by the researcher (me) specifically for this project. Hypotheses: To work out a person's BMI, we take their weight in kilograms and divide it by the square of their height in metres. I travel 3km to get to school. My height is 1.65m and my weight is 65kg. Therefore, my BMI (Body Mass Index) is 24. My friend, who travels 0.5km to get to school, has a BMI of 28. This has given my hypotheses: i) Students who have to travel further to get to school will have a higher BMI compared to those who don't have to travel as far The probability of a longer journey home compared to those who live closer to school is very high. During a bus or car journey, it is likely that the student will eat snacks. When they get home, the chances are that they will watch TV, eat dinner, do homework and play on the computer. It is highly unlikely that they will get round to exercise. ii) Students who travel further to get to school would be taller than those who live closer would would. I expect those closer to the school to have a lower BMI. Ultimately, I expect them to be taller or lighter to reduce their BMI. iii) Students who live closer to the school are lighter than those who live further away are. ...read more.


This means interpreting data will be very easy. It is also used because I summarising ordinal data. When drawing the bar chart, I shall ensure that: i) An appropriate scale is used A bar chart with an inappropriate scale can be very misleading. It can suggest unreal failures, successes or correlations. ii) All the bars are of the same width If bars are not of the same width, then the graph can also be very misleading. It can suggest opinions and therefore be biased. iii) All the axes are labelled If the axes were left unlabelled, the reader will not know what the bar chart is representing. iv) Both axes start on zero False zeros are used to present correct but misleading data. As I do not wish to lead the reader into any conclusions, my axes will start at zero. Bar Chart Calculations I will plot the mean of each group, rather than the median. Using the mean will allow me to work with all of the data in each group and the mean is probably the most accurate average. Therefore, I will not be excluding any results and therefore not causing any sort of bias. However, I accept that the mean itself may not be a true value and that it may be distorted by any extreme values. Students that live with 2.9 kilometres of the school: 15.6 + 18.5 + 16.6 + 20.8 + 18.0 + 24.0 + 27.1 + 24.8 + 15.7 + 20.1 + 18.3 + 21.5 + 18.2 + 16.0 + 16.0 + 17.9 + 19.6 + 24.8 + 17.4 + 24.8 + 17.4 + 21.1 + 17.2 + 19.5 + 23.0 + 14.8 + 16.9 + 17.3 + 18.1 + 18.3 + 19.9 = 578.3 578.3 / 30 = 19.3 Students that live between 3.0 miles and 4.9 miles of the school: 20.0 + 16.8 + 16.6 + 18.2 + 19.4 + 20.6 + 17.6 + 18.6 + 28.9 + 23.8 = 200.5 200.5 / 10 = 20.1 (3SF) ...read more.


To obtain someone's IQ, you could give them a test based on pictures or give them a written test. We are not told that every single person in the Mayfield database had their IQ tested in the same manner. Also, distances to and from school could be estimates rather than precise measurements. There is no doubt that my conclusions could be incorrect. Sample sizes: Overall, I feel that the sample sizes I have used for each part of my project are very sensible. I have not had to spend ages manipulating the data and plotting graphs. This shows that I have not taken a sample which is too large. However, my graphs and calculations were not done very quickly either, although they did not take hours, they still took some time. I feel that my sample sizes were not too small. My hypotheses: Overall, I feel that I have satisfied my goals on the first page. I have used a range of statistical skills, techniques and data representation methods to try and prove or disprove my hypotheses. In general, I feel that my work could have been improved if I had taken a sample double the size of all of the ones that I have done. I feel that would have made outliers more obvious and therefore given me a clearer statement on the quality of the data. I am happy that my conclusions are reliable because they were blatantly obvious. For example, it was obvious from the composite bar chart that the distance travelled to get to school had no impact on your height. My findings: As a result of my sampling, calculations, and data representations, I can state the following: - Those who live closer to Mayfield High School have a lower BMI - Those who live further to Mayfield High School weigh more. - Those who live closer to Mayfield High School are just as tall as those who live further away from the school. AQA GCSE Statistics Coursework 1 Vinson Yeung 10W King Edward VI Camp Hill Boys ...read more.

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