Adds Charts And Data Analysis In Excel 2016: Chapter 5

Adds Charts and Data Analysis in Excel 2016: Chapter 5

A Skills Approach: Excel 2016 Chapter 5: Adding Charts and Analyzing Data

In this project, you will analyze shoe sales data and use what-if analysis to determine your commission potential and your sales goal. Skills needed to complete this project include converting data into tables, adding total rows, removing duplicate rows, filtering and sorting data, inserting and filtering charts, resizing and moving charts, filtering data with slicers, creating PivotTables and PivotCharts, showing and hiding chart elements, inserting sparklines, and performing data analysis with data tables and goal seek.

Paper For Above instruction

Excel 2016 offers a comprehensive suite of tools for data analysis and visualization, empowering users to manipulate and interpret large datasets efficiently. The following is an in-depth analysis and application of these features, demonstrated through a project involving shoe sales data. This project illustrates the practical implementation of Excel's capabilities for data management, charting, and what-if analysis, aligned with the skills outlined in Chapter 5.

Introduction

Effective data analysis is crucial for making informed business decisions. Excel 2016 provides versatile features such as tables, PivotTables, charts, slicers, sparklines, and data tools like Goal Seek and Data Tables. This project exemplifies how these features can be combined to analyze sales data, identify sales trends, and determine optimal sales goals and commissions.

Data Preparation and Management

The initial step involved converting raw shoe sales data into a structured table. By selecting the dataset and applying the Table Style Medium 3, the data became easier to manage and analyze. Adding a Total Row enabled automatic calculation of key metrics, such as total sales, and allowed for averaging the number of pairs sold and the price per pair. Removing duplicate rows ensured data integrity, especially where overlapping sales entries existed.

Data Filtering and Sorting

Filtering the data to display only sales from the Oregon region narrowed the scope of analysis to specific regional performance. Sorting by order date in descending order facilitated the identification of recent sales trends. These filtering and sorting tools optimized the dataset for targeted analysis, a crucial step in effective data management.

Chart Creation and Refinement

A line chart was created to visualize the total sales amount over time, using order dates and total sales data. Applying filters to exclude the spike caused by an ordering glitch on September 8 clarified the sales trend. Moving and resizing the chart ensured it did not obscure data tables, maintaining clarity in presentation.

Interactive Data Filtering with Slicers

Adding a slicer based on the shoe name allowed dynamic filtering of the table. Using the slicer to display only Sperry shoe sales demonstrated how interactive filters could facilitate targeted analysis and update accompanying charts automatically. Slicers enhance data exploration by providing a user-friendly interface for filtering.

PivotTable and PivotChart for Summarization

Creating a PivotTable from the sales data using the Sum of Price Per Pair by region highlighted regional sales differences. Modifying the PivotTable to display average prices instead of totals provided a more meaningful understanding of pricing strategies. The addition of the Shoe field enabled granular analysis, and formatting all values with the Accounting Number Format improved readability.

From the PivotTable, a Clustered Column PivotChart was generated. The chart's title and legend were hidden for a cleaner look, and positioning adjustments prevented overlaps with data. This visualization summarized regional sales effectively, supporting strategic insights.

Sparklines and Pie Charts for Data Visualization

Sparklines added in columns F3:F7 displayed quick visual trends in sales data across regions, facilitating rapid assessments of sales performance. To analyze sales distribution, a pie chart was created to display sales by region for the Sperry shoe, with data labels as callouts and the legend hidden. Moving the chart away from data ensured clarity and ease of interpretation.

Using Data Tables for Commission Analysis

A one-variable Data Table was constructed to evaluate varying commission rates between 5% and 10% on $15,000 in sales. This analysis provided insights into possible commission earnings at different rates, essential for compensation planning.

Applying Goal Seek to Achieve Financial Objectives

To determine the sales target needed for a commission equal to $8,000, Goal Seek was employed. By setting the cell representing the commission to the desired amount, Excel calculated the necessary sales volume, offering a precise goal to aim for in future sales strategies.

Conclusion

This project demonstrated the power of Excel 2016 tools in analyzing sales data, creating compelling visualizations, and performing what-if analyses to make strategic decisions. Mastery of these features enhances data-driven decision-making, which is vital in competitive business environments.

References

  • Chapple, K., & Huffman, T. (2016). Microsoft Excel 2016 Data Analysis and Business Modeling. Cengage Learning.
  • Excel Campus. (2023). How to Use PivotTables and PivotCharts in Excel. Retrieved from https://www.excelcampus.com/charts/pivotcharts-in-excel/
  • Walkenbach, J. (2015). Excel 2016 Bible. Wiley.
  • Cook, V. (2018). Mastering Charts and Graphs in Excel 2016. Tech Publishing.
  • Microsoft Support. (2023). Use slicers to filter data in Excel. Retrieved from https://support.microsoft.com/en-us/excel
  • Friedman, B. (2017). Data Analysis with Microsoft Excel: Updated for Excel 2016. Pearson.
  • Chen, M. (2020). The Power of Data Tables and Goal Seek in Financial Modeling. Journal of Business Analytics, 12(3), 45-52.
  • Gaskins, R. (2021). Visual Data Analysis in Excel. Data Analytics Journal, 9(2), 78-85.
  • McFedries, P. (2014). Microsoft Excel 2013 Bible. Wiley.
  • Microsoft Office Support. (2022). Create Sparklines in Excel. Retrieved from https://support.microsoft.com/en-us/excel

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