• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month
Page
1. 1
1
2. 2
2
3. 3
3
4. 4
4
5. 5
5
6. 6
6
7. 7
7
8. 8
8
9. 9
9
10. 10
10
11. 11
11
12. 12
12
• Level: GCSE
• Subject: Maths
• Word count: 2294

# Mayfield High School- Statistics and&amp;#13;&amp;#10; Data Handling Coursework&amp;#13;&amp;#10;

Extracts from this document...

Introduction

## Rosanna Marr

For this project I have decided to look at the correlation between the height and weight of pupils at Mayfield High. We were given data about an imaginary high school which showed pupils’ gender, date of birth, IQ, eye colour along with other information. On deciding what I was going to investigate it became clear that much of this information was unnecessary, so, on my database, provided from my school computer, shown on Excel, I deleted certain categories, but kept information on height, weight, surname, forename, gender, year group and age, which I felt might come in handy during my enquiry.

To collect the data I needed, I simply transferred it from the provided statistics on Excel, to other worksheets, splitting the information into several different categories. My first category was a mixed sample, including both genders and all of the years. I used an equation;

 =INT(RAND()*(1183-1)+1)

I used this in order to generate random whole numbers between 1 and 1183, giving me a completely unsystematic mix of pupils from Mayfield High. This means that during this section of work, I will

Middle

Parreen

Saika

12

Female

1.60

45

979

8

Patel

Stephanie

13

Female

1.65

49

982

8

Royale

Danniel

13

Male

1.25

61

1021

9

Saleem

14

Female

1.64

70

1027

9

Saleem

14

Female

1.64

70

1027

7

Sayers

Ben

11

Male

1.54

40

1035

9

Singh

Karam

14

Female

1.49

40

1058

9

Slone

Mark

14

Male

1.58

50

1064

7

Smith

Karan

12

Female

1.42

29

1070

7

Thompson

12

Female

1.73

49

1112

7

Tnguine

Tina

12

Female

1.63

50

1121

7

White

Catherine

12

Female

1.56

45

1155

7

Whitworth

Kayleigh

12

Female

1.52

40

1160

10

Banks

Robbin

15

Male

1.66

66

Before I explain how I went about using this sample to try to discover correlation between my two chosen factors, I think it

necessary to state what outcome I think I will find after putting this information into graph-form and analysing it. I think I will definitely find a certain amount of correlation, but that the relationship will not be very strong. I think I will have to carry on looking into this in more depths in order to find a distinct relationship between pupils’ height and weight.

Firstly I decided to make some tally charts to analyse the data about the weights of the pupils, I then put this into a bar chart for easier viewing.

 283 11 Ratty Louise 15 Female 1.65 45 291 7 Afsal Oliver 11 Male 1.55 45 378 9 Banks Robin 14 Male 1.73 45 426 7 Bates Holly 12 Female 1.61 45 436 9 Bowy Jake 14 Male 1.54 45 472 9 Burn Suzanne 14 Female 1.6 46 498 7 Clarke Claire 12 Female 1.59 46 548 7 Craft Clara 12 Female 1.58 47 566 7 Davies Jamie 12 Male 1.57 47 588 7 Dickinson Ben 12 Male 1.49 48 603 8 Drayton Benjamin 13 Male 1.55 48 618 9 Dwyer Tommas 14 Male 1.58 49 622 7 Fisher Emma 12 Female 1.31 49

This chart shows that the modal weight is between 45-49, and from looking at my random mixed sample data, is it clear to see that from this range of numbers, 45kg is the most common.

I also worked out, that the mean weight of the pupil’s in this sample rounds down to 51kg. I worked this out by adding up all the weights then dividing the total by 50, the number of pupils. Also I found, by sorting the weights into ascending order, and finding the 25th weight, that the median is 49kg.

Using tally charts I also decided I would work out the mean, mode and median of the heights of the pupils using the same strategies. These turned out to be:

## mean=1.5892 m

modes were 1.55m and 1.58m with 4 each. (1.6m to 2significant figures or 156.5 as the number inbetween)

median =1.58m

I also changed the tally chart into a bar chart, using grouped frequencies.

……ranges?

Also using this sample I also decided to plot a scatter graph, to see if there was an overall correlation between height and weight. Firstly, I used Data Sort on Excel to arrange my information starting with the lowest height, and working up to the highest, so that when I plotted my graph, it would be in the correct order to see if there was any correlation.

Here is my scatter graph;

The circled crosses represent data, which occurred twice, for example:

 1155 7 Whitworth Kayleigh 12 Female 1.52 40 1160 10 Banks Robbin 15 Male 1.66 66

From this scatter graph it seems that there is quite poor correlation, but definitely some, as roughly the crosses do go from left to right, bottom to top. However,

it seems that this random mixed sample is not the best way to work out the relationship between the height and weight of the pupils in Mayfield High.

To try and see why this did not work out the amount of male/female pupils, and the number of pupils from each year within my sample to check its correspondence with the data on a whole.

 Total in my random sample Total in school Boys 20 604 Girls 30 579 Year 7’s 18 282 Year 8’s 4 270 Year 9’s 14 261 Year 10’s 9 200 Year 11’s 5 170 Total 50 1183

Conclusion

 Year Group Surname Forename 1 Years Months Gender Height (m) Weight (kg) 1 10 Abejurouge Henry 15 3 Female 1.63 60 2 10 Aberdeen Richard 15 0 Female 1.75 45

If I hadn’t of checked that this separation worked, I would have ended up using data such as this, which is really supposed to belong to my male sample. Therefore, I simply had to go through it all the long way, to make sure my data was correct.

I have decided to analyse this data like I did before, in order to see whether, like I assumed in my hypothesis, there is stronger correlation now between height and weight because I have made this gender split. In order to do this I will hopefully work out the mean, median, mode and ranges of this data and plot some graphs, to try and find out the relationship between height and weight.

MEAN, MEDIAN AND MODE.

 Height weight mean (total)46.21/30=1.54m (total)1495/30=49.8kg median 1.63m 49.5 kg mode 1.62 and 1.68m 45 kg

After analysing the female data I plan to do the same for the male, so I can see if there is much similarity between the two. I am hoping that there will be noticeable difference between the two genders, in order for there to show some sign of a difference when I plot some graphs based on my male/ female samples to see if separating the two causes more correlation to become apparent.

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. ## Edexcel GCSE Statistics Coursework

1.83 75 Hypothesis 1: I used Microsoft Excel to create two scatter graphs showing the relationship between height and weight for both females and males. Ignoring the anomaly which appears on the scatter graph for females, stating a female 1.03M tall is 45KG, the lightest female weighing in at 30KG is 1.42KG.

2. ## Maths Statistics Coursework - relationship between the weight and height

The yellow lines help me to indicate this. If the points are about the same everywhere and scattered anywhere, this can be considered as no correlation. The points on this graph are more frequently spotted in the bottom left and top right quadrant indicating me that this is a relatively weak positive correlation.

1. ## Mayfield High School data handling Coursework

whereas if we pick 60 for each it will be too much therefore time consuming. The amount that I have picked is manageable to use. Each year has different number of boys and girls, so the sampling should represent that.

2. ## Mayfield School Mathematics Statistics Coursework

3.86 4.14 Median 4 and 4 4 5 Mode 4 4 4 4 Standard Deviation 0.88 0.83 0.83 0.91 Skewness Males Females Maths 0.44 -0.51 Science 0.72 0.46 [Tables 3 and 4: Male / Female Measures Of Spread And Skewness] Studying these values together with the combined scatter diagram inserted over the page, I can reach a conclusion.

1. ## GCSE Maths Statistics Coursework

My first graph 'Male and Female Scatter Graph' showed me that there was a strong positive correlation between the two variables (IQ and Average SAT's Results). But as I started to break my graphs down into different categories like IQ with a particular subject or gender I found that some

2. ## Mayfield High Statistics Coursework

This is done by adding the frequencies in turn. Length Frequency Cumulative Frequency 21-24 3 3 25-28 7 10 (= 3 + 7) 29-32 12 22 (= 3 + 7 + 12) 33-36 6 28 (= 3 + 7 + 12 + 6) 37-40 4 32 (= 3 + 7 + 12 + 6 + 4)

1. ## Math Coursework-Mayfield High Data Handling

add on to the upper quartile, then any data above that sum will be considered as an outlier. I will use the negative side of the number to add on to the lower quartile, then any data below that sum will be considered as an outlier.

2. ## Liquid chromatography is a technique used to separate components of a mixture to isolate ...

104.2 1.30 2000 2000 5.08 114984 109.7 1.39 * Sodium benzoate Volume (�l) Loop size (�l) Retention time (min) Efficiency (plates/m) Efficiency (%) Symmetry (10%) height 5 5 7.33 123491 121.7 1.28 10 10 7.30 101489 100 1.02 20 20 7.32 110651 109.0 1.34 100 100 7.40 95138 93.7 1.24

• Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to