GCSE Maths Statistics CourseworkMark Howson

Table of Contents

Introduction 2

The Data 2

The Questions 2

Hypotheses 2

The Plan 2

Samples 4

Outliers 9

Hypothesis One: Students who watch more TV will weigh more on average 10

Hypothesis Two: People with Pets will Weigh Less 14

Hypothesis Four: Girls will weigh less, on average, than boys 18

Evaluation of Strategy and Results 20

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Introduction

I have been given some secondary data on the school population of Mayfield High School in Manchester. The population contains 851 Key Stage 3 students and 371 Key Stage 4 students, for a total of 1183 students. I have been asked to investigate the data and ask questions on it so I can set several hypotheses. The Data The data I have been given is a mix of female and male data and includes the following headings:

Qualitive Data• Year Group• Gender• Means of Getting to School

Discrete Data• Age in Years and Months• Average Number of Hours of TV Watched• No. of Pets• Weight The Questions From the data it would be interesting to see if there are any relationships between weight and TV watched, no. of pets, age and gender as I would like to see if the amount of activity completed effects the weight of a person. Hypotheses I believe I will find that people who weigh less and are a comparable age will generally watch less TV. This is because if people are not watching TV, they are more likely to be doing more strenuous activities. Furthermore, I believe people who weigh less will have more pets, because pets need to be walked in many circumstances. Also, I believe that people who weigh less will walk to school, as that means they are getting more exercise every morning and evening. I will compare the male results to the female results to see if there is a correlation between the different genders. The press generally says that the obesity problem is more apparent with boys, so it will be interesting to see if males, on average, weigh more or less than females. I also believe that age will effect this, so I will take a sample of 60 KS3 year old males, 60 KS4 year old females, 60 KS4 Males and 60 KS4 Females. I believe that I will find in general females will weigh less than males of a comparable age. The Plan To help me ensure I investigate these hypotheses to the best of my ability, I have written a plan. First, I will need to sample the population to reduce the number of data samples and make the project easier to do (this will also be an accurate sample of the population assuming there is not too much skew in the data). I will take a random sample of: • 30 KS3 Males with pets• 30 KS3 Females with pets

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• 30 KS4 Males with pets• 30 KS4 Females with pets

• 30 KS3 Males without pets• 30 KS3 Females without pets• 30 KS4 Males without pets• 30 KS4 Females without pets To do this, I will need to split the data into with pets and without pets, and then into male and female groups. The data is already split into KS3 and KS4 groups. When I have the data sorted into the relevant groups (strata), I will then use a random number generator to generate 30 random numbers for each group, to collate with the row numbers on the Excel spreadsheet. This type of sampling gives everyone an equal chance of being chosen. These random samples will then be imported into another Excel spreadsheet which will be used as a working spreadsheet. The samples will be exported to a .csv file, ready for import into Autograph. Then, I will ensure that the data has no outliers using a standard calculation, in which I will find values 1.5x the interquartile range below the lower quartile, and also above the upper quartile. Any values outside this range can be considered outliers, and will be excluded. They will not be replaced unless a significant proportion (10% or more) of the data set are outliers, as this should not make a significant change to the data.

To investigate the hypotheses I will use a scatter graph to compare hours of TV watched with weight, and if I find correlation I will draw a line of best fit and calculate the equation of the line for each set of results to allow me to make predictions about my data. I will also create box and whisker diagrams to directly compare the male and female data in both KS3 and KS4 to prove that females will weigh less than males in general.

I shall prove that both KS3 Females and KS4 students weigh less than their male counterparts, and that overall females at the school weigh less than males, also.

Drawing a box plot will also show me:

• The range of the data for males and females• Whether there is more variation in males or females• The Upper and Lower Quartiles of the data• The Median of the data I will then do the same for people with pets, and then compare the results for people with pets, to the results for people without pets in each category, as well as taking results from a variety of other categories. I will then calculate the average weight of people with each method of transport and plot a variety of graphs. I will calculate the standard deviation to show the spread of values for each method of transport, this will help to show how much weight varies, and distinguish where there is, and isn't, a real difference.

I will finish by evaluating the difference in male and female weights.

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When I have done all of this I will make conclusions on the above statistics, and evaluate my work.

Samples

KS3 Males without Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 170 60 Car 0Male 15 58 Car 0Male 7 53 Car 0Male 20 70 Combination 0Male 10 40 Bus 0Male 15 60 Walk 0Male 14 57 Tram 0Male 10 48 Tram 0Male 7 40 Bus 0Male 3 57 Tram 0Male 24 31 Tram 0Male 35 55 Bus 0Male 20 38 Car 0Male 5 48 Tram 0Male 10 52 Car 0Male 11 51 Bus 0Male 15 57 Car 0Male 17 47 Bus 0Male 31 51 Bus 0Male 69 46 Bus 0Male 10 40 Bus 0Male 26 70 Walk 0Male 7 43 Walk 0Male 15 50 Tram 0Male 13 60 Bus 0Male 3 49 Tram 0Male 16 48 Tram 0Male 28 38 Bus 0Male 7 38 Walk 0Male 28 29 Tram 0

KS3 Females without Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 2 50 Bus 0Female 22 57 Car 0Female 28 53 Car 0Female 5 46 Walk 0Female 13 57 Bus 0Female 14 65 Walk 0Female 14 44 Walk 0Female 26 60 Car 0Female 14 47 Tram 0

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Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 16 55 Bus 0Female 26 36 Walk 0Female 14 44 Walk 0Female 8 47 Tram 0Female 12 45 Bus 0Female 17 56 Walk 0Female 100 57 Car 0Female 9 48 Bus 0Female 35 40 Walk 0Female 16 42 Car 0Female 22 39 Walk 0Female 26 52 Car 0Female 14 40 Walk 0Female 39 48 Walk 0Female 12 52 Bus 0Female 15 58 Bus 0Female 32 57 Walk 0Female 14 46 Tram 0Female 13 45 Bus 0Female 10 47 Tram 0Female 14 38 Tram 0

KS3 Males with Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 20 48 Bus 2Male 20 42 Bus 1Male 21 45 Walk 2Male 20 52 Tram 1Male 8 42 Bus 5Male 14 44 Walk 6Male 13 68 Bus 1Male 14 42 Bus 3Male 19 48 Walk 2Male 22 67 Bus 2Male 8 59 Walk 1Male 20 59 Car 3Male 21 55 Bus 2Male 40 51 Bus 1Male 12 68 Car 1Male 14 51 Bus 2Male 24 56 Bus 1Male 20 47 Bus 2Male 20 50 Bus 2Male 13 59 Walk 1Male 16 44 Bus 1Male 14 46 Tram 4Male 32 50 Car 3Male 20 50 Walk 2Male 17 47 Bus 6Male 17 62 Car 1Male 4 40 Bus 1Male 20 42 Bus 3Male 30 49 Bus 3

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Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 24 52 Walk 2

KS3 Females with Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 10 42 Car 2Female 10 50 Tram 10Female 2 59 Tram 4Female 25 51 Tram 2Female 35 54 Combination 3Female 42 42 Bus 3Female 13 40 Bus 6Female 6 50 Tram 1Female 18 35 Walk 3Female 14 45 Bus 4Female 40 29 Bus 1Female 6 62 Bus 1Female 21 48 Car 3Female 14 66 Bus 2Female 100 43 Walk 10Female 4 43 Tram 1Female 21 49 Walk 1Female 15 49 Bus 2Female 12 53 Walk 3Female 24 38 Walk 2Female 15 40 Bus 2Female 17 54 Walk 6Female 35 52 Bus 2Female 21 48 Car 2Female 6 47 Bus 2Female 21 48 Car 3Female 11 58 Walk 2Female 10 52 Bus 1Female 10 57 Bus 1Female 8 37 Bus 4

KS4 Males without Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 42 60 Bus 0Male 16 70 Walk 0Male 2 70 Tram 0Male 17 60 Bus 0Male 6 56 Car 0Male 19 50 Tram 0Male 31 66 Bus 0Male 20 77 Tram 0Male 13 65 Walk 0Male 10 57 Car 0Male 48 52 Walk 0Male 48 52 Walk 0Male 7 56 Bus 0Male 16 56 Bus 0

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Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 21 40 Bus 0Male 20 38 Bus 0Male 30 5 Walk 0Male 20 50 Walk 0Male 30 54 Bike 0Male 40 72 Walk 0Male 40 72 Walk 0Male 4 72 Car 0Male 40 63 Walk 0Male 40 63 Walk 0Male 17 59 Walk 0Male 2 54 Bus 0Male 17 93 Tram 0Male 14 86 Tram 0Male 7 52 Car 0Male 10 50 Combination 0

KS4 Females without Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 16 45 Car 0Female 26 56 Bus 0Female 28 36 Tram 0Female 26 60 Car 0Female 26 56 Bus 0Female 70 51 Walk 0Female 3 56 Car 0Female 9 48 Bus 0Female 70 51 Walk 0Female 9 48 Bus 0Female 7 54 Tram 0Female 36 54 Car 0Female 36 54 Combination 0Female 20 55 Walk 0Female 2 52 Walk 0Female 14 54 Car 0Female 21 54 Car 0Female 25 48 Bus 0Female 5 45 Walk 0Female 6 60 Tram 0Female 6 60 Tram 0Female 56 45 Walk 0Female 17 54 Combination 0Female 5 45 Tram 0Female 14 48 Walk 0Female 26 36 Walk 0Female 28 50 Walk 0Female 20 50 Walk 0Female 12 45 Bus 0Female 6 48 Bus 0

KS4 Males with Pets

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Gender Hours of TV watched per week Weight Means of Travel No. of PetsMale 18 50 Bus 3Male 50 56 Walk 1Male 7 72 Car 1Male 18 67 Bus 1Male 22 49 Walk 5Male 12 68 Bus 1Male 27 51 Tram 1Male 14 45 Bus 2Male 12 62 Bus 2Male 7 50 Bus 1Male 10 80 Bike 7Male 21 45 Bus 19Male 4 57 Walk 2Male 15 64 Walk 1Male 35 58 Bus 3Male 190 9 Walk 6Male 20 40 Walk 3Male 13 64 Bus 2Male 20 68 Bus 7Male 3 50 Tram 1Male 3 64 Combination 2Male 12 60 Tram 1Male 1 72 Bike 4Male 7 64 Walk 2Male 3 62 Car 4Male 25 72 Walk 2Male 13 72 Bus 1Male 20 50 Train 1Male 15 50 Bike 2Male 20 60 Tram 3

KS4 Females with Pets

Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 24 65 Bus 1Female 14 48 Bus 3Female 100 50 Combination 5Female 14 58 Tram 1Female 15 55 Car 2Female 15 58 Bus 1Female 5 55 Walk 11Female 17 54 Bus 3Female 20 65 Car 7Female 7 63 Combination 1Female 12 50 Bus 3Female 21 54 Bus 8Female 22 52 Combination 3Female 7 45 Bus 5Female 30 44 Tram 12Female 10 51 Bus 4Female 15 55 Bus 2Female 45 50 Bus 2Female 10 51 Car 2Female 26 42 Walk 6

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Gender Hours of TV watched per week Weight Means of Travel No. of PetsFemale 26 48 Bus 12Female 13 52 Tram 1Female 40 38 Walk 3Female 10 45 Bus 1Female 7 55 Walk 2Female 25 45 Tram 2Female 14 60 Walk 2Female 6 74 Bus 7Female 13 48 Bus 5Female 7 66 Walk 15

Outliers

Outliers are data ‘far away’ from most of the data. One way to define outliers is as numbers 1.5x above the higher quartile or below the lower quartile. I have highlighted all the data that falls into one of these two categories in red, above (with exception to a few outliers in orange, explained below) and showed the calculations below.

Median Lower Quartile Upper Quartile Lowest Allowed Highest Allowed

Male KS3 No Pets WeightMale KS3 No Pets TVFemale KS3 No Pets WeightFemale KS3 No Pets TVMale KS3 Pets WeightMale KS3 Pets TVFemale KS3 Pets WeightFemale KS3 Pets TVMale KS4 No Pets WeightMale KS4 No Pets TVFemale KS4 No Pets WeightFemale KS4 No Pets TVMale KS4 Pets WeightMale KS4 Pets TVFemale KS4 Pets WeightFemale KS4 Pets TV

49.5 40 57 20 85.515 8.5 25 4.25 37.5

47.5 44 56.5 22 84.7514 12 26 6 3950 44 57.5 22 86.2520 14 21 7 31.5

48.5 42 54 21 8114.5 10 24 5 36

58 52 70 26 10518 10 40 5 6051 45 55 22.5 82.5

18.5 6 28 3 4260 50 68 25 102

14.5 7 21 3.5 31.552 48 58 24 87

14.5 10 25 5 37.5

I have elected to keep the outliers marked in orange in the sample, as they are still statistically plausible outliers. For TV consumption, 70 hours a week is extreme, and hence unplausible, however around 50 hours a week of TV consumption is very plausible. The highest ‘allowed’ outlier for TV was under 60 hours a week, which works out at television consumption of 8.5 hours a day, which is possible for someone, considering the amount of free time for television consumption at weekends, and that they may watch TV while doing other things.

The most outrageous figure stood at 190 hours of television per week. This would calculate to 27.4 hours a day, an impossibility. A more modest outlier of 100 hours of television per week would still calculate to 14 and a quarter hours a day of television, which is impossible owing to school commitments, unless the student elected not to sleep or not to attend school regularly.

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For weight, there were no unreasonably high outliers, and all outliers were excluded. Most of the outliers were unreasonably low. This could be a typographical error on the part of the student, or a choice not to reveal their weight for the survey, and instead type in a bogus figure.

Looking at the lower and upper quartiles of the data, we can see patterns beginning to emerge to support the hypothesis that people with more pets weigh less. This is demonstrated, for example, in the blue highlighted upper quartile ranges for KS3 students.

It is, however, important to note that not all of the data currently supports the conclusion, and hence the graphs produced could result in weaker evidence for this hypothesis than would have been preferable.

Hypothesis One: Students who watch more TV will weigh more on average

The first hypothesis states that students who watch more hours of TV per week will weigh more on average. To further analyze the hypothesis and come to more conclusions, I have split my analysis into more acute categories supported by a variety of graphs.

For Key Stage 3 Male Students, the Hypothesis is shown to be true, although not particularly dramatically. Indeed, a lot of the lighter students are shown significantly below the line of best fit on the graph, and the difference between the lowest, and highest, point on the line of best fit is only around two kilograms. As there is no clear correlation in the data, I can calculate the correlation co-efficient of the data, which will give me a calculation showing me how strong the correlation of the data is, and whether it is a negative or positive correlation.

Below is a table showing what the numbers of the correlation coefficient suggest:

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Correlation Negative Positive

Small -0.3 to -0.1 0.1 to 0.3

Medium -0.5 to -0.3 0.3 to 0.5

Large -1.0 to -0.5 0.5 to 1.0

The correlation co-efficient of the data stands at 0.03237. As this is below 0.1, we can see that this data has no real correlation, confirming our previous expectations regarding the graph.

This demonstrates that KS3 male students TV consumption does not produce a statistically profound difference to their weights, despite what might be expected. In Key Stage 4 Male Students (over the page), the line of best fit suggests that students actually weigh less based on hours of TV viewed, however, the lightest students all watch less than 30 hours of TV a week, with the lightest student watching 20 hours of TV per week, and weighing just under 40kg.

The heaviest student weighs around 93kg, however, they watch around 17 hours of television, again showing a lack of statistical correlation for the first hypothesis.

The correlation co-efficient of the data stands at -0.08927. As this is below -0.1, we can see that this data has no real correlation, however, there is a stronger correlation to be found in this graph than the KS3 graph.

Looking at the KS3 female weight and TV hours information, this significant lack of proof for hypothesis one continues, with the lightest student watching just under 40 hours of television per week.

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The same dramatic line of best fit suggesting students weigh less if they watch more hours of television per week is shown in Key Stage Four female results, with one of the lighter students watching over 50 hours of television per week, however, other light students are all over the graph, suggesting this information is purely coincidental.

One exception to the downward best of fit line can be seen by taking the data for all students with pets across KS3 and KS4, which shows that students who watch less TV weigh less.

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However, this data is likely to be quite coincidental, and could simply be a result of students with pets watching less TV as they have to spend more time looking after their pets. The all students graph repeats the weak negative correlation in the line of best fit, and again, the heaviest students do not watch as much TV as some of the lighter students. The correlation co-efficient of the data stands at -0.848, once again showing no real difference in the data, making the data quite inconclusive.

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To put the information into context, the below graph utilizes mean and standard deviation bars, and a dot plot, to demonstrate the averages of the various data.

The mean average of the data is significantly higher than the mode (where most of the data falls) in both cases. Taking the mode data, we would expect the average student to watch less TV and weigh less than the mean averages suggest. A larger sample than that of over 100 students used could produce a more accurate conclusion, but my conclusion at this time for hypothesis one is that it is incorrect.

The data does not suggest that students who watch less TV weigh less, instead suggesting that they weigh more than those who watch more TV. However, I believe that in this case the evidence provided is irrelevant and anecdotal in nature, as there is lots of data to work against the lines of best fit, and the negative correlations are weak at best.

The correlations shown are not strong enough to make any serious suggestions regarding this first hypothesis.

Hypothesis Two: People with Pets will Weigh Less

I believe that people with pets will weigh less because pets need to be walked in many circumstances, and in other circumstances, pets need to be cared for in other ways, for example feeding them. This increased physical activity should result in lower weights overall for people with pets.

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The box plots above support the second hypothesis, showing a significantly lower maximum weight for people without pets, around 13kg lower. Furthermore, the median average weight (shown by the centre bar of the box plot), is a couple of kilograms lower, while the lowest weight remains the same.

Therefore, the graph proves that people with pets generally weigh less than those without, although the interquartile range for people with pets is larger than that for people without weights, suggesting that the correlation between owning pets and weights is weaker than one might assume from the difference in maximum weight.

This can be further proven by using a dot plot to look at the mode values of the data. As can be seen in the below data, the mode weight for people without pets is lower than the mode weight for people with pets.

To conclude, Hypothesis Two is true, however there is a weak correlation between weight and having pets, and there is no ‘real difference’ between the values. A real difference would require the interquartile range of the box plots to not overlap.

Hypothesis Three: People who walk to school will weigh less than people who get to school via some other method

People who walk to school will weigh less because they get more routine excersize during the morning and evening by walking to and from school. This extra excersize could amount to anything up to 5 hours of walking a week for people who live around half an hour from the school, for those who live less far away, for example 10 minutes, they still walk for 100 minutes per week that they wouldn’t if they went to school by car.

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Calorie Lab shows that someone at our samples average weight of 52kg can burn 78 kilograms per hour of walking, which would convert to:

Time Calories Burned10 minutes 13100 minutes (a week for a person with a 10 minute walk) 130300 minutes (a week for a person with a 30 minute walk) 390400 minutes (a month for a person with a 30 minute walk) 5201200 minutes (a month for a person with a 30 minute walk) 15605200 minutes (a year for a person with a 10 minute walk) 676015600 minutes (a year for a person with a 30 minute walk) 20280

20,280 calories is the equivalent of the number of calories in 4.1kg of Twix, so it’s a significant amount of calories to burn. Relating to the sample group, the below shows a breakdown of the number of people who walk, compared to those who do not:

This shows me that I have a large enough population of people who walk to school for the graphs I create for this hypothesis to be statistically accurate. I created some box and whisker diagrams to prove the hypothesis.

Walks to School Another Method

0

50

100

150

200

Method of Transport to School

172

59

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From the diagram, I can say a number of the things. Firstly, it is important to note that the heaviest pupils by far do not walk to school, with a difference of around 20kg between those who do walk to school, and those who do not. However, this could be based on just one person, and as such, I shall produce a stem and leaf diagram to collect more information, and see if this is the case. The stem and leaf diagram will show me the spread of the data, from which I can create a histogram for additional analysis.

The stem and leaf diagram shows that the data is spread fairly evenly in a pyramid structure, which suggests that the earlier analysis of the 20kg difference was fair and accurate. This can also be shown as a histogram with a frequency polygon, to further illustrate the even spread of the data, and continue to verify the results.

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0

1

2

3

4

5

6

7

8

9

9 9

1 6 7 8 8 8 8

0 0 0 0 0 0 0 2 2 2 2 2 2 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 6 6 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6 6 7 7 7 7 7 7 7 8 8 8 8 8 9 9

0 0 0 0 0 0 0 0 0 2 2 2 2 3 4 4 5 5 6 6 7 7 8 8 8 8

0 0 2 2 2 2 4 7

0 6

3

Key 2 | 9 = 29

This again shows the ‘pyramid’ spread of the data. The information for people who walk to school displays a far tighter range than that of the people who don’t walk to school, however, the mean value is marginally higher. The lower quartile value, however, is significantly lower.

Together, this means that the information to support hypothesis three is inconclusive, and it is impossible from the sample to say if the hypothesis is correct or incorrect. If a larger sample had been taken, a more definitive answer could have been made.

Hypothesis Four: Girls will weigh less, on average, than boys

I believe that girls will weigh more on average than boys due to the coverage of the obesity crisis in the media being more attributed towards boys. I accept that KS4 students will generally be taller than KS3 students, and hence weigh more, so I will look at key stage 3 weights separately to key stage 4 weights.

Furthermore, I appreciate that height may affect weight, but that is beyond the scope of the hypothesis, and hence will not be tested. I will use box plots to compare the data and analyse it to form a conclusion regarding the hypothesis.

I am hoping to see an increased difference in this final hypothesis to the others, and prove that I chose an appropriate sample by producing conclusive results

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Upon creating the graph, we can see that the hypothesis is most definitely true, perhaps more so for KS4 students than KS3 students. While some male KS3 students weigh less than girls, they are outside of the interquartile range.

In the case of KS3 students, the mean average for females is significantly lower than that of males, as is the maximum weight for the student, the difference is even more significant for KS4 students, with the maximum weight differing by just under 20kg, and the minimum weight also being lower for females.

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By looking at the totals of male and female students, we can see that the mean data value is lower in females than males, and that the highest weight is smaller in females, resulting in less ‘spread’ for the data. There is a strong positive skew to the female data, and a weak positive skew to the male data.

Overall, the data from the sample positively proves the hypothesis, so it can be stated fairly that on average, secondary school age men weigh less than women of an equivalent age.

Evaluation of Strategy and Results

I will now evaluate my work on this statistics project, focusing on the software used, and each of the hypotheses, and how pleased I am, or am not, with the results that came from the sample group. I will look at where I could have improved my work.

I am pleased with my decision to use the ‘Autograph 3.2’ graphing software to create the graphs for my statistics courswork, as I feel it has helped to streamline the production of my graphs and results by creating them automatically, and doing all the calculations for the data. Samples and data where originally selected using a combination of Microsoft Excel and Apple Numbers ’08, which was a good combination. Excel was more advanced at creating the sample, and Numbers presented it better for inclusion in the final documentation.

If I were to repeat the process, I would take larger samples, of around 50 students from each category, and I would automatically remove the ridiculous figures, such as ‘170 hours of television per week’, before taking samples, as this would make the outliers checks and eventual graphs and analysis more focused and accurate, based more on ‘actual’ results, rather than spending time removing ridiculous results obviously inputted into the survey by students as a joke.

Regarding my first hypothesis, I was glad with my decision to use scatter graphs with a line of best fit. The line of best fit made for a good way to analyse the trends in the data, and see how the data progressed as TV hours increased. I was interested to see that there was no obvious correlation between the TV hours and weight of the students in the sample.

My second hypothesis focused on the theory that people with pets will weigh more. I found that while the heaviest people did not own pets, there was no obvious correlation between people with pets, and people without pets. This was interesting, as you would expect that the additional activity would result in reduced weights.

The third hypothesis suggested that people who walked to school would weigh less. While the sample was tighter, the evidence was not conclusive. I would have liked to see what would have happened to this data with a larger sample.

The fourth hypothesis was proven more than adequately with the elected sample size, proving that a sample size of 30 students is adequate for statistically accurate information in many cases. I was most pleased with this hypothesis.

Overall, I am happy with the results I have had from my statistics coursework, despite some of the hypotheses not being proven. I feel that the first hypothesis regarding TV viewing would never be properly proven, and instead the line of best fit would ‘flatten’ with a larger sample.

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