- Level: International Baccalaureate
- Subject: Maths
- Word count: 1638
Gold Medal Heights Maths Portfolio.
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Introduction
Gold Medal Heights
The table below gives the data achieved by the gold medalists in the Olympic Games.
Year | 1932 | 1936 | 1948 | 1952 | 1956 | 1960 | 1964 | 1968 | 1972 | 1976 | 1980 |
Height(cm) | 197 | 201 | 198 | 204 | 212 | 216 | 219 | 224 | 223 | 225 | 234 |
Using this data, I will create a model to represent the relationship between the high jump results and the years they took place. However, the Olympic Games did not occur in 1940 and 1948 due to World War 2. The independent variable is time, so let x-years be time and the dependent variable is height, so let y-centimetres be height. The winning height is the dependent variable since each year resources, technology, competition and may more are altered. After plotting the data, the following scatterplot was obtained:
Figure1: Height vs. Year Figure1.1: Window
Since x is in-terms of years, one may consider B.C as “negative” years.
the domain is
. The range is in-terms of the height jumped by the medalist therefore, theoretically speaking it is impossible for humans to jump above a certain height because we have our limitations and we cannot jump below a height of 0.
the range is
. The maximum height that can be jumped is set at 250cm because the highest achieved height by a human is 245cm by Javier Sotomayor.
Middle
Another function that could model these points is an exponential.
Figure 2: Exponential Function: Applying Reflections and Translations
When an exponential is reflected on the x and y-axis, the shape is similar to the plotted points. It is then translated d-units up, where d would represent the horizontal asymptote. Since e, Euler’s number, is a transcendental constant, I defined a as e and added –k to create a similar graph.
Since we know that the d-value is 250,
Since this function has three variables, three points must be chosen. To determine the first point, the first four data points are averaged, then the next four points are averaged to find the second point and finally, the last three data points are averaged to find the third point.
Point 1:
=
Point 2:
=
Point3:
=
Subtract equation 2 from 1
Substitute
into equation 3
Substitute equation 4 and
into equation 1
46.469
Substitute
46.469 into equation 4
Figure 3: Exponential and Cubic Models
Both equations have their own limitations. The cubic function is not the best model because as
,
and as
,
the cubic function does not fit the range, but it fits the given data points well since the RMSE value for the cubic is 4.614.
Conclusion
Works Cited
"High Jump World Records." Rob's Home of Sports, Fitness, Nutrition and Science. N.p., n.d. Web. 23 Feb. 2012. <http://www.topendsports.com/sport/athletics/record-high-jump.htm>.
"Logistic Equation -- from Wolfram MathWorld." Wolfram MathWorld: The Web's Most Extensive Mathematics Resource. N.p., n.d. Web. 22 Feb. 2012. <http://mathworld.wolfram.com/LogisticEquation.html>.
"Logistic function - Wikipedia, the free encyclopedia." Wikipedia, the free encyclopedia. N.p., n.d. Web. 22 Feb. 2012. <http://en.wikipedia.org/wiki/Logistic_function>.
"Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)." Eumetcal. N.p., n.d. Web. 23 Feb. 2012. <http://www.eumetcal.org/resources/ukmeteocal/verification/www/english/msg/ver_cont_var/uos3/uos3_ko1.htm>.
"NOAA/ESRL Global Monitoring Division - THE NOAA ANNUAL GREENHOUSE GAS INDEX (AGGI)." NOAA Earth System Research Laboratory. N.p., n.d. Web. 24 Feb. 2012. <http://www.esrl.noaa.gov/gmd/aggi/>.
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