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# The investigation that I am going to conduct is to see if peoples Height is related to their Age

Extracts from this document...

Introduction

Data Handling

Introduction

The investigation that I am going to conduct is to see if peoples Height is related to their Age.

Plan

1. Outline my goals and what I am trying to prove.  This will enable me to predict my results (hypothesize)
2. Collect data from various resources.
3. Review the data, making sure it is unbiased data.
4. Put the raw data into tally charts.
5. Then break down into separate ages (11 through to 17)
6. Produce cumulative frequency graphs for each age (inc. quartile ranges and median)
7. Calculate the mean for each age group and find out the standard deviation for the data.

My Hypothesis:

I believe the older you are, the taller you are in height.  I believe this because during the ages 11-17 there is a definite increase in height and the person is most likely to continue to grow further, whilst he or she’s gets older.

Collecting Data

Middle

15

17

14

For continuous data (Height) however, I will need to group my height data in to equal groups.

 Height (cm) Tally Frequency 110 – 119 1 120 – 129 1 130 – 139 3 140 – 149 29 150 – 159 72 160 – 169 59 170 – 179 42 180 – 189 13 190 – 199 1

This has allowed me too see a very vague picture of how the data is spread and of the age range involved, also the number of people used .It also shows me a rough idea of overall growth, however it does not show me what it is in relation to.  In other words it is just two sets of raw data.  It can be used to show much more which I will endeavor to do!

I am going to break the data into individual ages and height ranges and use various data representations and analyzing techniques to show what can be done.

Conclusion

Remembering!

The higher the standard deviation, the more spread out the data is, i.e. the further it is from the mean.

Age 13 shows this.  It has the highest value (of 18.3) for the standard deviation.

Previously, I had worked out the quartile values and inter quartile value. Whilst it is a good measure of the middle half of data and is not affected by extreme values (extreme height values or lower height values).  It does not show how the data is actually spread, i.e. where the regions of higher or lower values are situated.

Standard deviation is a good way of showing this.

The other advantage standard deviation uses the all the individual values of data and not just a measure of the difference between upper and lower quartiles.

This concludes my investigation

This student written piece of work is one of many that can be found in our GCSE Height and Weight of Pupils and other Mayfield High School investigations section.

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