Height vs handspan
Max Gee
1.0 Introduction
Height and hand span are two essential physical characteristics that can provide useful information
about a person's overall body size and structure. In this report, we aim to explore the relationship
between height and hand span and evaluate the accuracy of the measurements taken and their
relation to 17 – 18-year-old males. Additionally, we will examine the various procedures,
mathematical concepts, and techniques used to obtain the results, and analyse the use of
technology in the process. The reason behind this research project is to explore the correlation of
the two variable which could lead to improvements in forensic identifications E.G. break ins, this
idea was fir explored by … and to improve the model which was used instead of using both males
and females as variables , it was chosen to use males only as according to jrank crimes committed by
men are disproportionate to crimes committed by female counterparts.
Height Vs Handspan
y = 5.6353x + 58.316
R² = 0.1552
195
190
Height(cm)
185
180
175
170
165
160
17
18
19
20
21
22
23
24
Handspan(cm)
2.0 Considerations
2.6 Measurement Accuracy
The accuracy was held to a high importance in this study and to remedy any measuring inaccuracy ,
we used a 1-metre-high ruler and a marker to measure the height of students in grade 12 and a ruler
to measure hand span. The mark on the board was made with assistance of the 1 metre ruler to
allow more accurate results, and the ruler was verified to ensure that it was straight. The
participants were asked to stand against a wall with their head touching the wall and their feet flat
on the ground while their height was measured. The hand span was measured from the tip of the
thumb to the tip of the little finger with the hand stretched out. The participants were instructed to
stand up straight and keep their hand flat while the measurement was taken.
2.1 Variables and Key Terms
In this study, height and hand span are the two variables being analysed. Height refers to the
measurement of the distance from the floor to the top of the head but since shoes were not
instructed to be taken off so that may have an impact on the results, while hand span refers to the
measurement of the distance from the tip of the thumb to the tip of the little finger with the hand
stretched out. While some of the other values were taken from people online, where the accuracy
couldn’t be directly influenced.
2.2 Observations
The data collected from this study showed that there was a correlation between height and hand
span. The results showed that as height increased, so did hand span. The participants with the tallest
height had the largest hand span, while those with the shortest height had the smallest hand span.
It's also important to note that correlation does not imply causation, so while there appears to be a
relationship between height and the length of a persons hand in this study, it does not mean that
one causes the other but only gives a suggestion. Additionally, it's possible that other factors not
measured in this study could be contributing to the observed correlation such as bmi or back
conditions.
2.4 Procedures, Mathematical Concepts & Techniques
In this study, we used a simple mathematical formula to calculate the correlation between height
and hand span. The formula used was Pearson's correlation coefficient (r), which is a measure of the
strength of the relationship between two variable or subjects of interest. This formula was
calculated using Microsoft Excel through a scatter plot.
2.5 Use of Technology
2.5 Use of Technology
The use of technology was essential in this study, as it allowed us to collect, analyse, and present the
data in a more organized and efficient manner. Microsoft Excel was used to calculate the Pearson's
correlation coefficient and create a scatter plot, which showed the relationship between height and
hand span. This technology allowed us to visualize the data in a clear and concise way, and make it
easier to understand the results.
Using technology in data analysis is indeed beneficial in many ways. Not only does it allow for more
efficient and accurate data processing, but it also provides better visualization tools for easier
interpretation and communication of results. In this study, using Microsoft Excel for data analysis
and visualization proved to be a helpful tool for identifying the correlation between height and hand
span. By using technology it, allowed for finding to be produced in a clear and concise manner,
making it easier for others to understand and potentially build upon their work.
3.0 Developing a Solution
To verify the results of the study it was integral that the results were verified, to do this the hand
span of 3 people was randomly & put on the trend line and see were the result would fit according
to the formula.
To determine a more visible correlation a trendline was put on the graph with it’s correlating r value
which suggested a weak positive trend, in lamens terms the graph suggested that people with bigger
hand span, were taller in a small sample of cases.
4.0 Evaluation of Results
4.1 Improving the Model
(Use prediction line to verify accuracy of results).)(calculate height of student according to handspan
and verify with actual result.)
Using a prediction line to verify the integrity of the trend line, the formula which will be used is
(Y = mx + c) this formula allows for the prediction of a value and in this case, it will be used to verify
the accuracy of the correlation of the values.
For this evaluation the first second and third values will be used which are:
Handspan(cm)
20.5
21
21.2
Height (cm)
178
183
175
y = 5.6353x + 58.316
The equation y = 5.6353x + 58.316 relates the values of y and x. To find the value of y when x = 20.5,
we simply substitute 20.5 for x in the equation and solve for y:
y = 5.6353(20.5) + 58.316
y = 115.568 + 58.316
y = 173.884
that leaves a difference of 4cm
for the second value
y = 5.6353 x + 58.316
y = 5.6353 (21)x + 58.316
y = 118.3413 + 58.316
y = 176.6
for the last value
y = 5.6353(21.2)+58.316
y = 119.46836 + 58.316
y = 177.77
4.2 strengths and limitations
The linear regression model used in this study can be improved in several ways. One way is to
include more data points in the model, as this would increase the accuracy of the predictions.
Another way is to include additional variables, such as weight and body mass index, which could also
impact the height of a person, to have more concise data. While overall the correlation between
height and handspan was weak, the values provided from the formula was somewhat accurate.
5.0 Conclusion
In this report, we analysed the correlation between height and hand span in 17-18-year-old males.
The results showed a positive correlation between the two variables, with taller individuals having
larger hand spans. While correlation does not necessarily imply causation, the relationship observed
in this study may have implications for forensic identification in cases where hand span and height
are the only available measurements.
The accuracy of measurements was held to a high standard in this study, with a 1-meter ruler and a
marker used to measure height and a ruler used to measure hand span. The use of technology,
specifically Microsoft Excel, allowed for the efficient analysis and visualization of data.
To evaluate the accuracy of the results, a prediction line was used to compare the predicted heights
based on hand span with the actual heights of three individuals. While there were some
discrepancies, overall, the predicted heights were within a reasonable range of the actual heights.
Overall, this study provides insights into the relationship between height and hand span and
demonstrates the importance of accurate measurements and the use of technology in data analysis.
Further research could explore the relationship between height and hand span in different age
groups, genders, and populations.
Appendix
Data:
Name
Mark
James
David
John
Michael
Brian
Robert
Handspan(cm)
20.5
21
21.2
21.5
20.8
21.7
20.9
Height (cm)
178
183
175
187
173
182
176
William
Christopher
Joseph
Kevin
Steven
Matthew
Daniel
Andrew
Joshua
Timothy
Charles
Richard
Brandon
Jonathan
Ryan
Nicholas
Jacob
Thomas
Scott
Jason
Eric
Adam
Justin
Paul
Gary
Tyler
Jeremy
Travis
Nathan
Max
Daniel
Aji
Jay
Bailey
Harri
Ben
Alex
21.3
21.1
21.6
20.7
21.4
21.5
20.8
21.2
21.3
21.6
20.9
21.1
21.5
20.7
21.3
21.2
21.1
21.4
21.6
20.8
21.7
20.9
21.5
21.2
21.1
21.3
20.7
21.4
21.6
23
23.5
18
20
21
22
21
22
180
174
185
179
177
181
175
186
173
182
176
180
174
185
179
177
181
175
186
173
182
176
180
174
185
179
177
181
175
183
192
176
113
180
175
182
185
Forensic study:
https://law.jrank.org/pages/1250/Gender-Crime-Differences-between-male-female-offendingpatterns.html