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# Anthropometric Data

Extracts from this document...

Introduction

Coursework portfolio-Investigation 2

Using and applying statistics

Anthropometric Data

During this coursework I will be investigating the relationship between two of the data types. The main point of this coursework is focusing on the linear regression. Data was taken from Blackboard which contains anthropometric data from a large sample of children and young adults. This date result was taken in USA by the Consumer Product Safety Commission (CPSC).  From this information given I can observe that there may be some errors due to data input in the result taken, it may be that the data was collected in 1977 and during these times some techniques used in collect may be inefficient, some value may be incorrect due that it is a secondary data.  Has there will be some limitation in using the data again. From the data obtained I have decided to choose foot breadth and foot length has I feel that this information will be useful to design children soaks which could be sold in retail outlets. The chosen age range from 2-2 ½ years of age. This will be targeted at female in their gender group.

Table to showing 30 sample of foot length (mm) and foot breadth (mm)

 Female FOOT LENGTH (MM) FOOT BREADTH (MM) AGE (MONTHS) 131 62 24 132 60 24 123 53 24 134 53 24 135 59 24 135 56 24 156 58 24 131 53 25 140 60 25 137 60 25 137 56 25 119 52 26 142 63 26 140 57 26 154 69 28 136 52 28 138 58 28 146 61 28 136 60 29 148 61 29 141 56 29 153 61 29 150 60 29 128 58 30 161 60 30 133 54 30 137 59 30 147 54 30 138 53 31 138 55 31

Prediction 1

From this data I can predict that how wide your feet will depend on how long your foot is so in this case I will have a positive correlation. This may also be on the wider the child’s feet the longer the feet.  Positive correlation will occur has one of the variable increases so does the than the other. This is saying that has the children grow older the foot length tend to change and the breadth widen.

Middle

Scatter graph

A scatter it shows a relationship between two variables. On the scatter graph the (y) versus the corresponding values of (x). On the vertical axis which is (y) is usually responding variable and the horizontal axis which is (x) which relate to the response.

A scatter will be drawn to identify any outliers which mean any anomalous result that may look far out from the region. I will able to draw my line of best fit this will be drawn visually and also able to find the correlation coefficient in this case the value of r.

Prediction 2

Looking at the scatter graph I can visually predict that I will get a fairly fat ellipse has the points are quite spread out has. This also shows children with the foot breadth of about 52-55 (mm) are fairly grouped together where has foot breadth of 56 (mm) onwards or less are more spread out.

Diagram 1

After plotting the scatter graph I’m able to have a visual impression of how the points lie, it shows that all the points lie generally on an upward diagonal and also shows that there is an outlier.

Outlier

An outlier can occur due the basic linear relationship between (x) and (y), a single outlier occur in the (x) axis. The outlier may be defined as data point that emanates from a different model than do the rest of the data.

This outlier may be there due to the fact of data input error or may also explain that there is a medical problem with the child’s foot growth. Has it is not possible for a child who is 2 years and 3 months to have a foot length of 154 (mm) and 69 (mm) foot breadth.

Conclusion

Conclusion

• During this investigation I can say that knowing that the data is a secondary data there was an outlier within this chosen sample of foot length and foot breadth (mm). There are some limitation in checking if there is any errors.
• This was done in the US which is not relevant to those researches that what to produce socks in the U.K.
• The data was produced in the 70’s which mean is the not relevant to those research that want produce socks in this present time 2008.
• I’m able to make confident prediction on where the point seem to be more re-occurrence and where I can have a clear value of what my r is
• During the investigation I’m able to meet all my predictions which mean’s that my r value was showing a strong relationship between the variables.
• If I had the opportunity to this again I will take a large sample of the children within the age group and see if coefficient correlation will be measured  greater than what I have this will be a fairly strong positive correlation.
• Together I have weak positive correlation which means that the two variables that increases and make the ellipse broader slop upwards from bottom left to top right.
• As the r value is between +0.5 this is saying that is fairly weak positive as it may move to a strong positive correlation which is 1.

Index

1. Content pages

Number of pages

Label all the diagrams:

• Axes
• Units
• Title

Tables

• Title
1. 2 types of checking

Why? You are doing each graph/calculation commented on all work done

Intro

Conclusion

• Summary
• Limitaions
• Further ideas /improvements

Bibliography

http://www.itl.nist.gov/div898/handbook/eda/section3/scattera.htm

http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm

http://www.stats.gla.ac.uk/steps/glossary/paired_data.html

http://www.nvcc.edu/home/elanthier/methods/correlation.htm

http://cnx.org/content/m13446/latest/

http://hotmath.com/help/gt/genericalg1/section_11_3.html

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