Determining an Appropriate Parabolic Model
In this procedure, I am trying to determine an appropriate parabolic model that fits the data I collected of whirlybird wing length vs. time, doing so by using first principles. Also to find out which wing length would produce longest flight time.
Method:
Firstly, I made a whirlybird model and timed how long it took to reach the floor from a certain height. This procedure was repeated several times, each time lessening the wing length and keeping the same height. For each wing length, the bird was dropped three times for maximum accuracy. Once this data was collected it was transferred into a graph, and strange points were excluded. After this an appropriate
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As a gets closer to 0 the parabola becomes wider. The result was still too thin.
The next function was:
Y= -0.05(X-8)2+2.33
I changed -0.1 to -0.05. As a gets closer to 0 the parabola becomes wider. The result was still too thin.
The next function was:
Y= -0.03(X-8)2+2.33
I changed -0.05 to -0.03. As a gets closer to 0 the parabola becomes wider. The result was off centre to the left.
The next function was:
Y= -0.03(X-8.5)2+2.33
I changed -8 to -8.5. As b decreases the turning point moves to the right. The result was a little too wide.
The next function was:
Y= -0.035(X-8.5)2+2.33
I changed -0.03 to -0.035. As a gets farther from 0 the parabola becomes thinner. The result was a little too thin.
My final function was:
Y= -0.033(X-8.5)2+2.33
I changed -0.035 to 0.033. As a gets closer to 0 the parabola becomes wider.
Below the final quadratic function, Y= -0.033(X-8.5)2+2.33 is superimposed onto my original data points.
[IMAGE]
In the tables below, the results when the different lengths are substituted into the function, can be compared with our initial
I expect my graph to look like it does on the next page because I
0.96, 0.96, 0.97, 0.98, 1.01, 1.01, 1.02, 1.03, 1.03, 1.03, 1.03, 1.04, 1.04, 1.04, 1.04, 1.05, 1.05, 1.06, 1.07, 1.07, 1.08, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.10, 1.10, 1.10, 1.10, 1.11, 1.11, 1.11, 1.11, 1.12, 1.16, 1.17, 1.17, 1.18, 1.18, 1.20, 1.21, 1.21, 1.21, 1.23, 1.26, 1.29, 1.31, 1.32, 1.66
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The observation points, days of the week, are marked on the x axis and the frequency of PBA episodes is plotted on the y axis.
. I decided to conduct my investigation in this way because the graphs backed up my evidence and it help me out with my argument because everything that I was arguing was proved right by the
466 compared to .399. The residual plot for the linear regression is on page 5
“Hell-raiser, razor-feathered risers, windhover over Peshawar,”(Majmudar Lines 1-3) That was the first stanza of the poem “Ode to a Drone” by Amit Majmudar. This poem is written by a Muslim American who grew up in Cleveland, Ohio. The first stanza is the beginning to an ode to a drone, but immediately, you know he is not praising the drone for being powerful, he is explaining how it is unnecessarily destructive. A drone is an unmanned flying vehicle that shoots missiles or drops bombs on targets in modern warfare. Modern warfare today is taking place in the Middle East which is a transcontinental region centered on Western Asia and Egypt. Many terrorist groups such as ISIS, which is the largest threat right now.
In this sub-section, we present a brief overview of the Armington model that has been used by other researchers to find the relationship between exchange rate volatility and trade. In some trade models, such as neo-classical trade models, we assume that goods are homogeneous even though they are produced in different countries. Indeed, the prices of goods produced in different countries do not move in the same direction. However, it was first pointed out by Armington (1969) that goods that are produced in different countries need to be treated differently. An overview of Armington (1969) is that the change in demand for goods depends on the growth of the market in which firms compete and the growth of
Our predicted points for our data are, (13, -88.57) and (-2, -29.84). These points show the
As predicted, the higher the concentration of groundwater pollution, the higher the number of reported mutations and disease cases over the last 10 years was. To add on, the plot points move in an upward direction, which supports my conclusion. In addition, when I conducted my linear regression test on Graph 2, the line moved in an upward direction which reveals a positive correlation. This proves that the higher the groundwater pollution, the higher the number of reported mutations and disease cases will be. Consequently, the correlation coefficient of the graph was 0.99, which is a very strong correlation since it is only 0.01 away from 1. Ultimately, when I created my residual plot graph, the points were closely scattered and close to the X axis. This means that the linear model that I made for the graph was
Below the graph consist of the intersections of the line ‘y = x’ , ‘y = 2x’ with the curve y = x4
0.000 7 63 106 55 74.7 1.245 9 70 135 90 98.3 1.638 11 85 135 70 96.8 1.613 [ IMAGE ] [ IMAGE ] Conclusion = = = =
When looking at the data from the following chart, one can appreciate how many different analyses can be made.
There is a very small risk that some of the data I collected could be
This graph shows the result that I expect to get, I expect to see a