research paper

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I provided the Research paper posted below.Make sure you checked it.

Note:no plagarism and need citations and 2-4 pages with graphs reference and incitations

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Running Head: DATA SCIENCE 1 Olympic Medalist Research Paper You can, of course, repeat this exercise on any subject of your choice but here is an additional dataset about some contrasting Olympic medallist data. This exercise involves you working with an already acquired dataset to undertake the remaining three key steps of examining, transforming and exploring your data to develop a deep familiarisation with its properties and qualities. Complete the "Olympic Medalists" exercise located at the following link: Working With Data Provide at least a 10-page paper and a 10-15 slide presentation of your findings. For each dataset: Examination: Articulate the meaning of the data (its representativeness and phenomenon) and thoroughly examine the physical properties (type, size, condition) noting down your descriptions in each case. Compare what the datasets offer and contrast their differences. Transformation: What could you do/would you need to do to clean or modify the existing data? What other data could you imagine would be valuable to consolidate the existing data? Exploration: Use a tool of your choice (common recommendations would be Excel, Tableau, R) to visually explore the two datasets separately in order to deepen your appreciation of their physical properties and their discoverable qualities (insights) to help you cement your understanding of their respective value. 1. Make the data stand out. The focus here is on revealing the structure of the data. It includes discussion of how to fill the data region, transform data, choose an appropriate scale for an axis, eliminate chart junk and other superfluous material, and avoid having graph elements interfere with data, which includes topics such as over plotting, jittering, and transparency. 2. Add information. In addition to the usual conveyance of the importance of labeling axes and using legends, we also discuss how to: use color and plotting symbols to convey additional information; add context with reference markers and labels; and write comprehensive captions that are self-contained, describe the important features, and summarize the conclusions drawn from the graph. 3. Key Questions and Interpretations of Data Analysis... No. 1 Question What is the message? Interpretation Get past the presentation to the facts 2 Is the source reliable? Think about the information’s quality. 3 How strong is the evidence Understand how this overall? information fits with other evidence. 4 Does the information matter? Determine whether the information changes your thinking and leads you to respond. 5 What do the numbers mean? Remember that understanding the importance of risk requires that you understand the numbers. 6 How does the risk compare Put the risk into context. DATA SCIENCE 2 to others? 7 What actions can be taken Identify the ways you can to reduce risk? mitigate the risk to improve your situation. 8 What are the trade-offs? Make sure you can live with the trade-offs associated with different actions. 9 What else do I need to know? Focus on identifying the information that would help you make a better decision. 10 Where can I get more information? Find the information you need to make a better decision. ...
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School: Cornell University


Running Head: DATA SCIENCE


Olympic Medalist Research Paper
Name of Student
Institution Affiliation



What Numbers Mean in Dataset
Probabilistic risk valuation methods are undoubtedly useful in defining contingency numbers to
cover numerous process risk, computation methods are frequently as virtuous, as or even much
better than, multifaceted methods for the solicitations conferred here. Holder’s agents must be
adept in statistical methods for computing the risk probabilities, so that they can be capable to
check numbers given. While addressing the probabilistic risk valuation, one should comprehend
that the goal is to alleviate and manage risks and that numerical risk valuation is only a portion of
the procedure to help attain that goal. (Sham, P. C. 2012).
Numbers models are the computerized probabilistic virtual reality that, for the computational
solution, characteristically customs arbitrary number makers to pull variants from prospect
distributions. Since the computer virtual reality is performed with the random numbe...

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Tutor went the extra mile to help me with this essay. Citations were a bit shaky but I appreciated how well he handled APA styles and how ok he was to change them even though I didnt specify. Got a B+ which is believable and acceptable.

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