# Data Handling Project

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More ↓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:

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### Related Searches

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

spread for both hair colours.

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. (%)