Kristina Deneve 3 Posts Dissertation Topic Elizabeth Based O

Kristina Deneve3 Postsdissertation Topicelizabethbased On Your Person

Kristina Deneve has inquired about choosing a doctoral research topic based on personal experiences and the research paradigm to use in investigating that topic. The context includes instructions related to analyzing shoe sales data using Excel, involving data conversion, filtering, sorting, chart creation, pivot tables, sparklines, data tables, and goal seek analysis. The overall assignment appears to focus on conducting a detailed data analysis project using Excel tools, culminating in a comprehensive report or presentation of findings.

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Paper For Above instruction

The task involves a detailed, multi-faceted analysis of sales data using Excel 2013, focusing on practical application of data management, visualization, and analytical tools to derive meaningful insights. The project challenges students to harness skills such as converting raw data into structured tables, performing data cleaning by removing duplicates, filtering data sets based on specific criteria, and sorting data for chronological analysis.

The initial steps require converting the shoe sales data into an Excel table with a specified style, adding a total row that computes sums and averages for key columns, and ensuring data integrity through duplicate removal. Filtering the data by the California region and ordering the data by the most recent sales provide an organized foundation for subsequent analysis.

A core component involves creating visual representations such as line charts that depict total sales over time, with specific filters applied to handle irregularities—such as excluding anomalous spikes caused by data entry errors. Additionally, inserting a slicer allows dynamic filtering of data by shoe type, providing interactive analysis options and updating the associated charts dynamically.

PivotTables are a critical analytical tool within this exercise. Creating a recommended PivotTable with summed data organized by region, and adjusting it to display averages rather than totals for specific metrics, enhances the interpretability of regional sales performance. Incorporating the 'Shoe' field into the PivotTable further refines the analysis, creating a layered view of sales distribution.

A PivotChart, designed to visually compare regional data, complements the PivotTable analysis, with modifications including hiding titles and legends for clarity and adjusting placement for unobstructed visibility. Additionally, sparklines—miniature charts—are added for regional data ranges to facilitate quick visual interpretation of trends across regions.

The financial aspect of the analysis involves constructing a one-variable data table to explore potential commission earnings based on variable commission rates from 5% to 10%, providing a scenario analysis tool for predicting income under different commission structures. The final analytical component employs Goal Seek, a sensitivity analysis technique, to determine the necessary sales volume to fully repay a $12,000 student loan with commissions earned, thus integrating financial planning into the data analysis.

Throughout, the focus remains on integrating multiple Excel functionalities to conduct a comprehensive sales and financial analysis. Students are expected to produce a professional, well-organized report that synthesizes data insights, visualizations, and financial calculations, supported by appropriate interpretations and conclusions.

References:

  • Gaskin, J., & Gaskin, M. (2016). Excel 2016 for Beginners: The Step-by-Step Guide to Mastering Excel. Amazon Digital Services.
  • Walkenbach, J. (2013). Excel 2013 Bible. John Wiley & Sons.
  • Hancock, P. (2015). Data Analysis with Excel Dashboards and Reports. Que Publishing.
  • Chamberlin, R., & Hoth, M. (2017). Data Analysis and Visualization Using Excel. CRC Press.
  • Reynolds, G. (2017). Data Analysis in Excel: A Complete Guide. Excel University.
  • Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
  • Few, S. (2013). Information Dashboard Design: The Effective Visual Communication of Data. O'Reilly Media.
  • Gordon, L. (2014). Financial Modeling Using Excel. Pearson Education.
  • Clark, H. (2018). Mastering Data Analysis with Excel. Packt Publishing.
  • Microsoft Support. (2015). Create a PivotTable to analyze worksheet data. Microsoft Office Support.

Kristina Deneve3 Postsdissertation Topicelizabethbased On Your Person

The core challenge of this assignment is to leverage Excel's robust suite of data analysis tools to examine shoe sales data comprehensively, deriving insights that intertwine sales performance with financial considerations such as commissions and loan repayments. This process begins with organizing raw data into structured tables, which facilitates efficient analysis and visualization. Converting the data into an Excel table, applying styles, and adding total rows for summative insights creates a clear data foundation.

Subsequently, filtering essential data by region and date allows for targeted analysis. Sorting the data from newest to oldest orders enables chronological assessments of sales performance, while the use of filters helps isolate specific trends or anomalies, such as spikes in sales due to data entry errors. For instance, filtering out the spike on April 26 ensures that analyses reflect typical sales patterns. Creating line charts based on these filtered data points visualizes sales trends over time, aiding in identifying peak periods and irregularities.

Enhancing interaction with the data, inserting a slicer for shoe names provides dynamic filtering capabilities. This interactivity allows users to quickly observe how sales of specific shoe models contribute to overall performance and visualizes the effect on related charts, fostering a deeper understanding of product-specific trends. The integration of PivotTables enables summarization of sales data by key categories such as region and shoe type. Adjusting PivotTable calculations from totals to averages offers a more accurate view of typical sales performance across different regions.

Adding a PivotChart derived from the PivotTable facilitates visual comparison of regional sales. Adjusting chart elements, such as hiding titles and legends, creates a clean visual focus on data trends. Moving the chart to an unobstructed location ensures clarity and enhances interpretability. Furthermore, sparklines — miniature charts embedded in cells — provide quick visual cues about regional sales trends, facilitating rapid assessment of performance across multiple regions.

The financial analysis is particularly critical for understanding potential income based on variable commissions. Constructing a one-variable data table enables scenario analysis, illustrating how different commission rates (ranging from 5% to 10%) influence potential earnings. This enables informed decision-making regarding commission strategies. Employing Goal Seek further enhances the financial planning aspect by determining the required total sales to pay off existing student loans of $12,000, using the computed commissions per sale.

Overall, this project synthesizes data management, visualization, and financial modeling tools within Excel to produce a comprehensive sales and income analysis. The process demonstrates mastery of core Excel features and enables data-driven decision-making. The final report should interpret the visual and numerical findings, emphasizing key insights such as sales patterns, regional performance, profit potential, and financial goals achievement. Clear, well-organized presentation of analyses supports better strategic decisions and highlights Excel’s capacity as a powerful analytical tool.

References

  • Gaskin, J., & Gaskin, M. (2016). Excel 2016 for Beginners: The Step-by-Step Guide to Mastering Excel. Amazon Digital Services.
  • Walkenbach, J. (2013). Excel 2013 Bible. John Wiley & Sons.
  • Hancock, P. (2015). Data Analysis with Excel Dashboards and Reports. Que Publishing.
  • Chamberlin, R., & Hoth, M. (2017). Data Analysis and Visualization Using Excel. CRC Press.
  • Reynolds, G. (2017). Data Analysis in Excel: A Complete Guide. Excel University.
  • Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
  • Few, S. (2013). Information Dashboard Design: The Effective Visual Communication of Data. O'Reilly Media.
  • Gordon, L. (2014). Financial Modeling Using Excel. Pearson Education.
  • Clark, H. (2018). Mastering Data Analysis with Excel. Packt Publishing.
  • Microsoft Support. (2015). Create a PivotTable to analyze worksheet data. Microsoft Office Support.

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