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  • Level: GCSE
  • Subject: Maths
  • Word count: 2742

Data Handling

Extracts from this document...


    In this piece of coursework I am going to test to see if my Hypothesis is correct. The aim of this investigation is to investigate the following hypothesis: image00.pngimage01.png

”The lower your BMI is the fitter you are so your mean resting pulse rate should be lower.“


     The data has been collected from three-year groups. Year 7, 9 and 11. The maths department has decided on these year groups, as there is two years in between each group. In years 7 and 9 the data was collected from sets A,B,C and D. Whereas year 11 the data was collected from all sets from A to F. The decision to only use A-D for years 7 and 9 was that it is more practical and some pupils from the lower sets are immature and may not take it seriously and this would obstruct the investigation. While in year 11 pupils are more grown up and act in a civilized manner.

     The data had no results from girls as it is an all boys’ school in which the data was collected from. Each pupil was given a survey sheet. There was a few details that need to be filled in they were:

  1. Do you have a medical condition? Yes / No
  2. How old are you?Years and Months
  3. Are you a member of a sports team?Yes / No
  4. How many hours of sport do you do a week?Number of hours

      Each class took their pulse rate 5 times and found the average of the 5 results. The mean was then recorded.

...read more.



      The mean shows us the average mean resting pulse rate for each BMI condition rather than the most common resting pulse rate in each category. In order to calculate this I added all of the results collected for mean resting pulse rate in each individual BMI condition and divided it by the total amount of results in that individual BMI condition category. The mean for the mean return to resting rate for BMI condition Underweight is 61 which increase as you go down the table, Normal 71, Overweight 81, Obese 83. Once again this shows that my hypothesises is correct, showing that people who have a lower BMI are fitter as they have a lower mean resting rate i.e. Normal 18.6 – 25 has an average mean resting rate of 71.

    I worked out the mean and mode by drawing a dotplot that indicates all the values to work out the mode and mean.        

Box and Whisker Plots

      A box and whisker plot is another way of showing the spread of collected data and statistics. The graph presents information in a simple form. It especially shows anomalous results. They are good when there is a large number of data to analyse, this is why it is a good way to analyse my data this way because the information I have is on a large scale and using this method is a quick and easy way to analyse the information selected. The diagram below indicates what a box and whisker diagram looks like:




The diagram shows the range, median and the quartiles.

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    By looking at the values I could see that my hypothesis is strongly supported by the data. The average mean resting pulse rate for underweight was 64, normal was 74, overweight was 82 and obese was 82.5. This indicates my hypothesis was correct.

     I then decided to investigate factors that could affect my hypothesis. Age, number of hours of sport per week and time to return to resting rate. The data was placed into scatter graphs which is easy to analyse two variables. The results all grouped together suggest that:

The younger you are the fitter you are because your BMI is lower, your mean resting pulse rate is lower and the time to return to resting rate is lower.

   This data is shown in the scatter graphs and boxplot. If I was to do this investigation again I would have collected data from each person in all year groups ranging from year 7-11 and use all classes in each year group. The bigger sample would have made my results more accurate.

    I would also experiment with different techniques to record my data to easily analyses it and compare it with the test I have done in this investigation. Also I would use more accurate data with more precise measuring instruments for example measuring height, use a ruler to the nearest millimetre instead of nearest centimetre. This would then improve precision and close to the true value. I would also do the investigation twice over 1 week an average the results. This would therefore increase the reliability which would make it more of a fair investigation.

By Matthew O’Hara          

...read more.

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