• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month   # Maths - Handling Data

Extracts from this document...

Introduction

Maths Handling Data Coursework.

In this coursework I will be investigating whether or not height affects weight by looking at data from years 10 and 11 at Mayfield High School. I would expect that taller people will weigh more but obviously there will exceptions as some people are tall and skinny and some are short and broader, but generally the taller people should weigh more. I also expect year 11 to be taller and therefore weigh more than year 10 as they are older and should have grown more.

The data that I have been provided with is secondary data as I have not collected it myself. I intend to get a sample of 60 from the database consisting of 370 students. This sample will be a fair sample with each person having an equal chance of being chosen. I will ensure this is a fair sample by doing a stratified random sample in order to get a correct proportion of year 10 and 11 students. The year groups will be separated and each person in the year group given a number. Then I will use the random number button on my calculator, I will do this until I have the number of year 10 students I want, and then I will do the same for year 11 students. Using

Middle

50

11

1.72

64

11

1.80

42

11

1.53

42

Frequency Tables - Height.

 Height (h meters) Frequency 1.0 < h <1.15 1 1.15 < h <1.30 0 1.30 < h <1.45 0 1.45 < h <1.60 8 1.60 < h <1.75 10 1.75 < h <1.90 9 1.90 < h <2.05 0

Year 10Year 11

 Height (h meters) Frequency 1.0 < h <1.15 0 1.15 < h <1.30 0 1.30 < h <1.45 1 1.45 < h <1.60 5 1.60 < h <1.75 18 1.75 < h <1.90 6 1.90 < h <2.05 2

Here are two tables representing the height of students in year 10 and in year 11.

By looking at the two tables, I can see that the modal class for each year group is 1.60 – 1.75 metres. The difference is huge in year 10 but in year 11 the difference is very small and by looking at the data it could be said that more people in year 10 are taller which is very surprising.

Percentages for Pie Charts.

Year 10Year 11

 Height (h metres) Percentage 1.0 < h < 1.15 0/32 x 100 = 0 1.15 < h < 1.30 0/32 x 100 = 0 1.30 < h < 1.45 1/32 x 100 = 3.13 1.45 < h < 1.60 5/32 x 100 = 15.63 1.60 < h < 1.75 18/32 x 100 = 56.25 1.75 < h < 1.90 6/32 x 100 = 18.75 1.90 < h < 2.05 2/32 x 100 = 6.25 Height (h meters) Percentage 1.0 < h < 1.15 1/28 x 100 = 3.57 1.15 < h < 1.30 0/28 x 100 = 0 1.30 < h < 1.45 0/28 x 100 = 0 1.45 < h < 1.60 8/28 x 100 = 28.57 1.60 < h < 1.75 10/28 x 100 = 35.71 1.75 < h < 1.90 9/28 x 100 = 32.14 1.90 < h < 2.05 0/32 x 100 = 0

Pie Charts – Height.

Year 10 Year 11 Frequency Tables – Weight.

Year 10Year 11

 Weight (W kg’s) Frequency 30 < w <40 0 40 < w <50 8 50 < w <60 14 60 < w <70 6 70 < w <80 1 80 < w <90 3

Conclusion

1. Year 10: Here there is a strong positive correlation suggesting that a pupil who is taller also weighs more.

1. Year 11: This graph is very similar to that of year 10 as it clearly shows a strong correlation suggesting that a pupil who is taller also weighs more.

Conclusion

My hypothesis that taller people will weigh more has been proved correct through this investigation. There is a strong correlation, which suggests that the two sets of data are related as I thought. However, my theory that the older you are the taller you are and therefore the more you weigh have been shown to be incorrect has year 10 pupils had both a higher average height and weight. This may be because the figures given by the pupils were incorrect.

Evaluation

In order to make this investigation more accurate and reliable I could have used a much bigger sample. Although this would involve a lot more time being spent and more calculations, the accuracy of the investigation would be much better and this may show and even stronger correlation and even prove my theory that the older you are, the taller you are and therefore the more you weigh.

However, I have only looked at a sample of the data, the sample was taken randomly and I have not been bias at any time throughout the investigation so I do believe that my conclusion is correct and that there is a strong correlation between height and weight of pupils in year 10 and in year 11.

This student written piece of work is one of many that can be found in our GCSE Height and Weight of Pupils and other Mayfield High School investigations section.

## Found what you're looking for?

• Start learning 29% faster today
• 150,000+ documents available
• Just £6.99 a month

Not the one? Search for your essay title...
• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month

# Related GCSE Height and Weight of Pupils and other Mayfield High School investigations essays

1. ## Maths Data Handling

I will find the range while creating the stem and leaf diagrams as I will know what the highest and lowest height and weight for boys and girls. Once I have got all the information that I need and

2. ## Maths: Data Handling Coursework

I thought that it wouldn't be as reliable if I used the same sample size of boys and girls because the actual total of girls were higher than of boys. For each year group, I am going to draw a stem and leaf diagram.

1. ## mayfield high school handling data coursework

10 Javidson Carlos Male 1.70 57 0.154367 10 McManus Anthony David Male 1.73 50 0.162861 10 Mine Peter Male 1.70 55 0.120262 10 Smith John Peter Male 1.73 56 0.889929 10 Grant Michael Paul Male 1.74 64 0.562017 10 Doens John Male 1.77 72 0.263432 10 Casey Fred Male 1.62

2. ## Data Handling - GCSE Coursework

I'm going to do a scatter graph so I can look at the correlation of the sample. Boys Results Height 167 180 132 151 165 173 181 169 183 171 197 172 165 203 184 Weight 66 60 45 40 54 65 75 54 84 64 50 86 76 50

1. ## Mayfield Maths Coursework

it would become unrepresentative if I ended up with a sample of 30 10 year olds and no other pupils from any other age group. Even though you have to be careful when using this method it is quicker than random sampling.

2. ## Data Handling Project

This, in my opinion shows that the hypothesis that I have formulated is correct. Although both graphs are representing the same data, I believe that each graph has its value and that both help me to provide a more in-depth analysis and a more accurate conclusion.

1. ## Data handling coursework

Mode The mode is the number that occurs most in a set of data. To work out the mode I will leave the measurements in order of size and find the number that occurs most. I will find the mode for each gender separately for all the years.

2. ## Data handling

is 1, the data will fit perfectly on the line of best fit. If the relationship between the data is negative, the correlation coefficient will be also be negative. Every time the C.C. id close to -1, the closer the data are together. • Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to 