Artifact 2: Quantitative Analytical Objective Your Work Can

Artifact 2 Quantitativeanalyticalobjectiveyour Work Can Always Be

Artifact #2 - Quantitative/Analytical/Objective Your work can always be improved. Using submitted work from a previous class, please locate an analysis, problem set (preferably in Excel), model, flowchart, or other objective-type items to submit as your next artifact. This week your assignment is to critique and revise your item and submit it by Sunday night, along with a one-paragraph narrative in a Word document that explains the origin, course, and purpose of the artifact in your master's program, and describes the changes you have made to improve the artifact. Please contact your instructor for approval if you are unsure whether your item fits into this category this week. You should correct at least the following in your artifact: all mechanical errors, all content errors, and any other improvements you think needed. Any other feedback received by your faculty member should be incorporated.

Example Documents: As you have progressed through your program, portfolio assignments have been identified for you to save for this item: BUSN603 - Chase Manhattan Bank Case Study. The assignment selected must exceed 3 pages or equivalent. If you do not have an assignment pre-selected from a prior course, consider the selection criteria for e-Portfolio artifacts listed in the attached document.

Paper For Above instruction

The purpose of Artifact 2 is to demonstrate the continuous improvement in my analytical and problem-solving skills acquired throughout my master's program. Specifically, the artifact I have selected for critique and revision is a detailed financial analysis problem set I completed in an earlier course, prepared initially in Excel. This artifact exemplifies my ability to apply quantitative methods to real-world scenarios, emphasizing objective evaluation and precision. Through the revision process, I aimed to address mechanical errors such as formula inaccuracies, formatting inconsistencies, and data misentries, ensuring mathematical correctness and clarity. Additionally, I reviewed the content comprehensively to correct any interpretative errors or miscalculations, and I incorporated feedback received from my faculty regarding the clarity and presentation of the model.

The origin of this artifact traces back to the BUSN603 course, where I analyzed a corporate financial situation involving cash flow forecasting and risk assessment for Chase Manhattan Bank. The course’s focus on financial modeling strengthened my skills in quantitative analysis, which I now seek to refine further by revising this artifact. Its purpose was to demonstrate competency in financial modeling, Excel proficiency, and data interpretation, all crucial in my overarching goal of developing managerial decision-making skills through objective analysis.

In revising this artifact, I made several significant adjustments to enhance its academic and professional quality. First, I corrected all mechanical errors identified during my review, including formula errors, inconsistent cell formatting, and broken links in the Excel model. I also improved the clarity of labels, titles, and explanations within the model to ensure it is self-explanatory and accessible for future reviewers. Furthermore, I refined the content by rechecking the data inputs and calculations to eliminate inaccuracies, enhancing the analysis’s reliability. I added detailed annotations and a summary section to make the insights more actionable for decision-makers. Lastly, I incorporated feedback from my instructor, who suggested better structuring of the model and clearer presentation of the assumptions involved.

This process of critique and revision embodies my commitment to continuous learning and professional development. It reflects the evolving nature of my analytical capabilities and underscores the importance of objective evaluation in refining work outputs. By systematically addressing errors and improving clarity, I have heightened the artifact's relevance and usability. This exercise not only enhances my technical proficiency but also sharpens my critical thinking and attention to detail—skills essential for effective decision-making in the business context. My goal remains to develop a robust portfolio of quantitative artifacts that showcase progressive mastery and the application of learned techniques to real-world problems, aligning with my career aspirations of managerial roles requiring sound analytical judgment.

References

  • Epstein, R. A., & Schneider, M. (2016). Business Analytics: Methods, Models, and Practice. Wiley.
  • Heizer, J., Render, B., & Munson, C. (2020). Operations Management. Pearson.
  • Higgins, J. M. (2018). Quantitative Methods in Business. Routledge.
  • Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. Wiley.
  • Shim, J. K., & Siegel, J. G. (2012). Financial Data Analysis with Microsoft Excel. Routledge.
  • Turban, E., Sharda, R., & Delen, D. (2018). Decision Support and Business Intelligence. Pearson.
  • Weygandt, J. J., Kieso, D. E., & Kimmel, P. D. (2019). Financial Accounting. Wiley.
  • McKinney, P. (2019). Data Analytics for Business. Chapman & Hall/CRC.
  • Silver, N. (2012). The Signal and the Noise. Penguin Books.
  • Vose, D. (2008). Risk Analysis: A Quantitative Guide. Wiley.

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