# Data Handling Project

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Data Handling Project

This Data Handling Project is looking at a database based in Excel
where there is data from Key Stage 3 and 4 from High School.
This data consists of several columns containing both Quantative and
Qualitative Information. Examples of this data are:

Â· Year Group

Â· Name; Surname, Forename 1 and Forename 2

Â· Age in Months and Years

Â· Month of Birthday

Â· Gender

Â· Hair Colour

Â· Eye Colour

Â· Left/Right Handed

Â· Favourite Colour

Â· Average number of Hours TV Watched per week

Â· SATS Results etcâ€¦

In this project I am going to make up several Hypothesises that I will
use the data from the Data Base to help me prove. However I will not
use all of the data, and for each Hypothesis I will Random Sample
using the computer 30 entries which fit into certain restrictions
applying to that Aim.

The Random Sampling method that I am going to use is a computer
generated one. The method of doing this is as follows:

1. Filter or sort the necessary data, copy and paste into a new sheet.
Add 2 extra columns before this data. In Column 1 leave blank, and in
Column 2 type the numbers 1 to X.

2. In the top of Column 2 type =RAND()*X, this makes a number between
1 and X. In the top of Column 1 put SUM in. In Column 1 next to that
number put the number 1 and press enter, a new number will appear, put
a 1 next to that number etcâ€¦ The amount of 1's you have typed will in
the top of Column 1 - continue until 30.

3. Copy and paste the selected data into a new sheet so that you can
draw graphs and analyse it etcâ€¦

Do Female Brunettes have higher IQ's that Blonde Females?

The hypothesis of this Question that I want to use the data from the
Mayfield High School to prove is:

MLA Citation:
"Data Handling Project." 123HelpMe.com. 22 Jan 2020
<https://www.123helpme.com/view.asp?id=120304>.

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### Popular Essays

====================================================================

The Females from the Mayfield High School in key Stage 4 that have
Brown hair are more intelligent, as shown in their IQ Level, that
Females in the same age range and from the same School but with Blonde
coloured hair?

To get my data and graphs I am going to sort and filter data from the
key Stage 4 information. I am going to sort firstly by Hair Colour in
ascending order, then by 1q in ascending order. I will then random
sample 30 from each and put these into Categories in a tally table,
which I can then turn into 3 graphs:

Â· A Comparison between the Blonde and brunette females in each of the
IQ categories.

Â· Cumulative Frequency of Brunette Females IQ,

Â· Cumulative Frequency of Blonde Females IQ.

[See following sheets]

From sorting and filtering out my necessary data, and drawing up my
Graphs I am now going to put this information into Box and Whisker
Diagrams [ with graphs for this section ] also finding the average,
mean, and mode information. After I have done this I am then going to
analyse my findings.

Blonde Females IQ.

Median =92

Lower Quartile =87

Upper Quartile =95

Brunette Females IQ.

Median =88

Lower Quartile =85

Upper Quartile =90

The average IQ for:

Blonde Females is: 2679 / 30 = 89.3

Brunette Females is: 2556 / 30 = 85.3

The Mode IQ for:

Blonde Females is: 91 (5)

Brunette Females is: 90 (8)

From looking at all of the graphs and diagrams that I have drawn I
have come to the conclusion that there is no real significant change
in IQ Levels

between Blonde and Brunette Females from Key Stage 4 in the Mayfield
High School. This is because all of the data really evens itself out.

The differences between the Blonde and Brunette Females IQ Levels only
really showed that there were 2 more Brunettes that had between 96 -
100 IQ's than the Blondes. This is not really strong evidence to base
the statement of fact that Brunettes are more intelligent as they have
higher IQ's than the Blondes.

The Box and Whisker Diagram for the Blonde Females IQ has a larger
Lower Quartile, a smaller Median area, and a larger Upper Quartile
than the Brunette Females IQ.

The spacing of the sections in these diagrams shows to me that due to
the Blonde Females having a smaller Lower Quartile IQ space there are
less people in this section than in the Brunette Females section. As
the Upper Quartile is larger for the Blondes than the Brunettes this
agrees with the fact that the Blondes appear to be slightly more
intelligent and have higher IQ's. However as there is not a real
significant difference i.e., by 2 in the Lower Quartile I don't think
that this should really be deemed as a final conclusion as if I was to
choose different sample data my results may have been more evenly

There was one anomaly in this data that I used to answer this
Hypothesis. The piece of data that had the Anomaly was a girl in Yr.
11 with Blonde hair and an IQ Level of 11. I don't think that this is
a real piece of data as in the whole IQ Levels for Key Stage 4 there
was only 2 students with IQ Levels of under 76. These 2 levels were 11
and 14.

Therefore from conducting these graphs I can say that my Hypothesis is
not true and that I didn't really find any great significant change in
the IQ Levels.

Does the Average hours of TV Watched per week affect a persons Weight?

The hypothesis of this Question that I want to use the data from the
Mayfield High School to prove is:
====================================================================

Is the increase in the amount of television watched by pupils in Year
10 reflected by their size, in weight? E.g. does the heaviest person
in my sample watch the most TV? What are the most common hours of TV
Watched and is there a concentrated result of the same weight shown
here?

To get my data and graphs I am going to sort and filter data from the
key Stage 4 information. I am going to sort firstly by Age to filter
out all of the Year 10 Students. Then I will delete the irrelevant
columns and sample using Random sampling 30 entries. This data will
then be sorted into the amount of hours of TV Watched in decreasing
order, so that I can then draw up my Graphs. I am going to leave the
Gender of the people in my data selection so that I can use this to
help me make a more precise and accurate reading from my results in my
Conclusion and Analysis of data. The graphs that I am going to draw
are going to be:

Â· Frequency of the Hours of TV Watched on Average per week in %,

Â· Scatter Diagram to show the relationship.

Â· Bar Graph to show the Average Weight for each TV Hrs category.

[See following sheets]

[IMAGE][IMAGE] The graph of the Frequency of Hours watched showed me
that the most popular amount of hours of watching TV is between 11 and
15 hours, shown on the graph as 24%. From looking at my Data in my
Table the mean value is 14 with 4 entries. From these 4 entries of 14
Hrs of TV there are 4 different weights. If I average these weights
the average weight comes to [62+42+57+63=ans/4]=56 Kg.

To answer the question of "does the heaviest person in my sample watch
the most TV?" I am going to average all of the weights for each
different hrs of TV Watched and plot this in a bar graph.

[Bar Graph on the next page]

The results from this table and graph show me that there is no real
relationship between the heaviest person and the fact that they watch
the most television, as predicted in my Hypothesis. This is shown from
the fact that the heaviest person watches only 15 Hrs of TV and the
person who watches the most TV weighs only 68Kg.

Unfortunately from looking at my Graphs I think that there is once
again no real correlation between the size of someone and the amount
of TV that they watch.

However if I look at my Scatter Diagram I can see that, ignoring the
anomaly (in a yellow circle), there does seem to be some weak negative
correlation. However from looking at the Scatter Diagram even more I
can see that this weak positive correlation isn't coming from the fact
that the heaviest person watches the most TV but more like the less TV
that is watched means that they are of an average weight. This seems
to apply until we get to above 20 Hrs of watching TV a week where the
results seem to spread up and down. I think that this signifies that
when the hours of TV is increased some peoples weight does either
increase or decrease from the original concentrated area (average
weight).

There was one anomaly in my data and this was a male who watched 65
hours of TV a week and Weighed 68 Kg. The reason that I think this is
an anomaly is because it is literally impossible to watch this amount
of TV a week when the person has to attend school and complete
homework of an evening. Also the next person down in my sample only
watches 48 hours of TV, so there is a difference range of [68 - 48]
20Hours.

The Conclusion to my analysis of data is that my Hypothesis is not
true. However from conducting this research and graphs I think that it
is evident that the less TV is watched the more that person is of an
average weight.

Do Girls in general have a higher IQ than Boys in the same years?

The hypothesis of this Question that I want to use the data from the
Mayfield High School to prove is:
====================================================================

Girls in Key Stage 4 at the Mayfield High School have a higher IQ
Level than the Boys from the same years and school.

To get my data and graphs I am going to sort and filter data from the
key Stage 4 information. I am going to sort firstly by Gender to
filter out all of the Year 10 Female and Male Students and will then
put each into a different worksheet. I am then going to Random Sample
15 entries from the boys and 15 entries from the Girls to allow me to
compare them easily. In total I will now have 30 Random Sampled
entries. The graphs that I am going to draw to help me answer this
hypothesis are going to be:

Â· Bar Graph to show the comparison between the Girls and Boys IQ
Levels,

Â· Bar Graph to show the total IQ Comparison,

Â· Surface Area Graph to show the average for the Boy and Girls IQ,

Â· Cumulative Frequency Graph of both the Boys and Girls.

[See following Sheets]

From my Bar Graph that show the comparison between the Boys and Girls
IQ Levels I can see that overall the Girls do have higher IQ's.
Another point that proves this fact is that in the middle of this
diagram the Boys have higher amounts of people with Lower IQ's.

Looking at my Total IQ results plotted in a Bar Graph, and my Average
Results for both the Boys and Girls in my Surface Area Graph it is
evident that the Girls IQ levels are much higher. The rise is 40more
higher in IQ Levels for the Girls than the Boys. This works out for
each student as a rise in [40/15] = 2.66 IQ Levels. The very sharp
increase in my Surface Area diagram also show this off.

Therefore as a Conclusion in my Data Analysis I can say that my
Hypothesis of:

Girls in Key Stage 4 at the Mayfield High School have a higher IQ
Level than the Boys from the same years and school.

Is true.

This can be proved in all of graphs so therefore I think that these
are a reliable way of saying that in the Mayfield High School in key
Stage 4 the Girls have a much higher IQ in general than the Boys.

Evaluation.

From doing these hypothesises I have been able to gather some
information about Mayfield High School. The hypothesises that I
carried out during this coursework were:

1. The Females from the Mayfield High School in key Stage 4 that have
Brown hair are more intelligent, as shown in their IQ Level, that
Females in the same age range and from the same School but with Blonde
coloured hair?

2. Is the increase in the amount of television watched by pupils in
Year 10 reflected by their size, in weight? E.g. does the heaviest
person in my sample watch the most TV? What are the most common hours
of TV Watched and is there a concentrated result of the same weight
shown here?

3. Girls in Key Stage 4 at the Mayfield High School have a higher IQ
Level than the Boys from the same years and school.

Some other hypothesises that I could have used the data to help me
prove would be:

1. Is the favourite subject of a child mean that they score the
highest SAT result in this subject?

2. Pupils who are older than other pupils have a higher IQ.

3. Someone who lives closer to closer to school travels by bus than
someone who lives further away from school.

4. Is the amount of siblings you have, have any relationship with the
way in which you travel to school.

These would use data from the following sections:

Â· Favourite Subject

Â· SAT results

Â· Age (years and months)

Â· IQ

Â· Distance from school

Â· Means of travel

Â· Number of Siblings

The reason that I didn't use some of the other data was because of the
fact that the majority of it was qualitative rather than quantative. I
can plot quantative and qualitative data but it is not easy to plot 2
sets of quantative (numbers) data.

Throughout carrying out these hypothesises I have come across a few
anomalies - These I have identified in each section, and explained by
reasons behind them. Mainly the reasons were that the data was totally
im - practable and must have been mistakes in the entering of the
data.

If I were to repeat each of these hypothesises again I think that I
would do a few things differently. These would be:

Â· Use more amounts of data for each hypothesis.

This would provide a larger sample and should mean that I will be able
to produce a more strong result.

E.g. this could change the difference between a positive and a fairly
strong positive correlation in a Scatter Diagram of 2 pieces of
Quantative data.

Â· Work out the Mean, Mode, Medians for all sets of data within a
hypothesis.

This will provide more evidence on which to base my conclusion to my
hypothesis. Also using a larger sample it may produce more evidential
reasons in a simpler form.

Â· Select equal amounts of boys and girls to form my Sample.

E.g. 20 or 15 of each.

As I experienced choosing unequal amounts of both Boys and Girls can
cause a change in the result.

Â· Use a wider range of Interpreting my Data.

E.g., using more graphs and diagrams.

Although they may just repeat the same information some graphs may
show the same results in different and some clearer ways. Also I think
that it would also be better to show and perform more calculations
within my data.

E.g. Converting my data in Percentages. (%)