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  • Level: GCSE
  • Subject: Maths
  • Word count: 2652

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

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Introduction. This Data Handling Project is looking at a database based in Excel where there is data from Key Stage 3 and 4 from Mayfield High School. This data consists of several columns containing both Quantative and Qualitative Information. Examples of this data are: * Year Group * Name; Surname, Forename 1 and Forename 2 * Age in Months and Years * Month of Birthday * Gender * Hair Colour * Eye Colour * Left/Right Handed * Favourite Colour * Average number of Hours TV Watched per week * SATS Results etc... 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 etc... ...read more.


[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. ...read more.


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 ...read more.

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