Gold Medal Height

18. Draw a best-fit line for the Tonga Trench data. A best fit line is a smooth line that shows the trend of the data; the line does not have to pass through the data points.

The Barbie Bungee lab was conducted in order to find the association between the amount of rubber bands and the distance the Barbie bungeed. Before performing the final experiment, the group conducted an initial investigation to get data that could be analyzed to examine the comparison from the amount of rubber bands to the length Barbie was able to bungee. In the investigation rubber bands would gradually be added one by one starting at two rubber bands. Each time a rubber band was added, three trial bungees were done and the lengths the barbie dropped were recorded. Using data collected from our background investigation, the group used excel to create a sheet displaying the data in a table, a graph showing the correlation constant, the line of best fit. The line of best fit was in slope-intercept form (y=mx+b) where y represents the length of the trial average; m represents the slope

Finally we got all our number and determine the slope, and the intercept in order to find out the forecast for the next

The slope of the linear fit of the data is 1.0049. What this tells me about the water is that it is increasing at a close to constant rate – while my results were not completely accurate because the slope of the line was not one it was fairly close to the target

For problems requiring computations, please ensure that your Excel file includes the associated cell computations and/or statistics output; this information is needed in order to receive full credit on these

Statistical results of the data analysis have been received by using the Gauss curve, as preferred distribution function, and the

Figure 1 Concentration of glucose relative to elution volume. Graph plotted using Excel. The equation of the line is represented by a 6th order polynomial (y = -4E-08x6 + 8E-06x5 - 0.0006x4 + 0.0184x3 - 0.2952x2 + 2.0705x - 4.6828) with a regression R² = 0.75191.

1) Which type of function (linear, exponential, or cubic) do you believe will best fit the data? Support your choice.

Knowing this information, you need to first tell me, and then show this in your graph:

2 3 175 0.080 951 0.43 195 245 658 335 170 3.4 0.9 57.0 9.2 0.21 61.4 1,718 530 1.1 2,520 92,000 583 14 22 6 19 15 927 765 12,280 22

Despite determining to achieve the “joy of effort” among the athletes, as well as the people who are interested in and watch the Games, the IOC constantly reminds the public of the significance and the power of winning the Games and receiving the medals. As found in many official Olympics websites from the Sochi Olympics 2014, London Olympics 2012, dated back to Beijing Olympics 2008, and also more, the ranking of the medals for each country could easily be located, while the Olympic Charter (2015) itself states that “the IOC and the OCOG [the Organising Committees for the Olympic Games] shall not draw up any global ranking per country” (as cited in Davis, 2014). Even though the instinct to win might already be innate or pre-constructed in humans, clearly, the act of the IOC in illustrating the medal rank of each country can influence the degree of awareness in the concept of winning and losing, as well as the physical and emotional aspects of an intense competition resulted from nationalism. Therefore, regardless of the goal of the IOC to uphold the ideals of the Olympics, the standards cannot be completely achieved because the IOC also violate the value of “joy of effort”

The line of best-fit is used to find the gradient, the T2/L value, if straight or linear it shows that the relationship between the two is directly proportional. Using the original equation, you can square both sides and rearrange it to make . Then you can input the gradient value (T2/L) and work out g. , where g equals 10.13 m/s2. This value is close to the

The trendline, known as the line of best fit or the least squares regression line, shows the linear equation which best explains the sums up the data’s trend. The formula on the right is the formula of the line of best fit.

In the graph here above f(x) represents the function that we are trying to approximate

This can be clearly seen in figure 6.8 (d) where the profiles are reconstructed using the data consisting 4 % and 5 % multi-crossing errors, which show much more accuracy than the profiles reconstructed using the replicated data with identical amounts of errors. One of the reasons that the multi-crossing errors produced better accuracy could be due to the constant distributions of the errors at each frequency points allowing the algorithm to fit the curves with reasonable degree of accuracy compared with the replicated data. Although these types of errors are not normally expected within the problem considered in this thesis, these results reveal that the accuracy of the reconstructed profiles depends upon both the amount of error and the error distributions on the data.