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The Average Student Plan.

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Introduction

The Average Student Plan

Having looked at various other methods of data collection I have come to the conclusion that I will base my work on the average student. Firstly I think it is appropriate that I define what the average student actually is. There are different interpretations as to what an average student is, for adults perhaps teachers they may base their ideas as to what the average student is on exam results. Other people may believe the average student is based on what sports they play, what genre of music they listen to or what type of books they read. However for the data collection I will be carrying out I will base my data on a student’s physical attributes, as this will enable me to use higher-level maths techniques, which will be later explained.

Due to limitations in working time I have decided to focus on recording just the height and weight of males and females. I have decided to focus on height and weight because they are continuous data therefore it will be possible to apply higher-level maths techniques. Another reason for selecting this data collection is that the data is easily accessible.

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Middle

Information taken from ‘OCR Graduated Assessment Stages 9&10 Textbook.’

Attribute Sampling

The selection of the sample is made by choosing attribute such as head size and height from a list of people on the basis of their birthday being the first of the month and trying to identify any relationship between the two.

Stratified or quota sampling

‘The population is divided into strata or subgroups and the sample chosen to reflect the properties of these subgroups. An example of this would be if a population contained three times as many people under the age of 25 as over 25 then the sample should also contain three times as many people under 25.'

Information taken from ‘OCR Graduated Assessment stages 9&10 Textbook.’

Random Sampling

This is when there is no knowledge about a population’s characteristics, for example any knowledge about the ages and gender in the population. In this case a sample has to be selected on the basis that all items are equally likely to be chosen. To ensure the sample is random and as accurate as possible the sampling must be repeated several times and then have the results averaged.

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Conclusion

The appropriate average differentiates with graph work for example with Cumulative Frequency I will be working with median however I can compare this value with the mean data and comment on the graph distributions. Once I have the collected data I will then look back to my hypotheses and determine whether or not I was correct. As I mentioned earlier I will attempt to use higher-level maths techniques. I will be using Pearson’s Product Movement Correlation Coefficient to test for any relationships between data. I will also include evidence of Cumulative Frequency diagrams and Box Plots as an attempt to prove any relationships between sets of data. I will also be using Histograms and Standard Deviation to find certain things such as the spread about the mean and using these values to compare with each set of results.

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