Data Handling Project looking at a database based in Excel where there is data from Key Stage 3 and 4 from Mayfield High School

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:

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

* Scatter Diagram to show the relationship.

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

[See following sheets]

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:

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

* Bar Graph to show the total IQ Comparison,

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

* 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:

. 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:

. 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:

* Favourite Subject

* SAT results

* Age (years and months)

* IQ

* Distance from school

* Means of travel

* 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:

* 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.

* 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.

* 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.

* 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. (%)

Rachel Butterfield. 10B.

Mayfield High Data Handling Coursework.

Page 1 of 8