Type In A Keyword For The Data You Want To Search For In Se ✓ Solved

Type In A Key Word For The Data You Want To Search For In Search Box

Type in a key word for the data you want to search for in search box or browse by topic. Select one of the databases found (CSV) will open into Excel easily. Download the file into Excel. Create a Pivot table based on the data you downloaded. State why you selected these fields and analyze the data in the pivot table. For example, you may say that your analysis was based on the "source" of the snow removal requests and the number of requests from that source was used to see what method of communication was generating the highest number of requests. Based on the data in the pivot table, it shows that over 60% of the sidewalk snow removal requests are made by phone. The impact for this city may be that they need more people to answer phones during the winter or the city may want to encourage residents to use other means of reporting such as email or webforms.

Sample Paper For Above instruction

Introduction

Creating a pivot table in Excel based on a dataset is a vital skill for data analysis, enabling users to summarize and interpret large amounts of data efficiently. This process involves selecting key fields, organizing data, and extracting insights that can inform decision-making. In this essay, I will demonstrate how to approach this task through a practical example related to snow removal requests, focusing on the communication methods used to report sidewalk snow removal issues.

Selection of Data

The first step involves selecting a relevant dataset. For this example, a CSV database containing snow removal requests was chosen. After importing the CSV file into Excel, I reviewed the available columns and selected fields that would provide meaningful insights related to reporting methods and request sources. The fields I chose include "Request ID," "Request Source," "Reporting Method," and "Request Date." These fields are essential to understanding how residents report snow removal issues and which methods are most frequently used.

Rationale for Field Selection

The primary reason for selecting these fields is to analyze the effectiveness and preferences in reporting snow removal requests. By focusing on "Request Source" and "Reporting Method," I could identify which channels residents predominantly use—whether by phone, email, webforms, or other means. The "Request Date" field assists in understanding temporal patterns, such as peak reporting times during winter months. Choosing these fields enables a targeted analysis of reporting behavior, helping city officials allocate resources efficiently and improve communication strategies.

Creating the Pivot Table

Using the selected data fields, I created a pivot table in Excel. I positioned "Reporting Method" as the row label, and the "Request ID" as the values to count the number of requests per method. This arrangement allowed me to see at a glance which communication channels were most utilized by residents. The pivot table was sorted in descending order to highlight the most common reporting requests, revealing that phone calls account for over 60% of requests.

Data Analysis and Insights

The analysis of the pivot table provides valuable insights into resident reporting behaviors. The high percentage (>60%) of requests made via phone suggests that many residents prefer traditional communication methods. This preference might lead to increased staffing needs in call centers during peak winter months to handle the volume efficiently. Additionally, it indicates an area where the city could improve by promoting digital reporting methods, such as webforms or email, which could ease the burden on phone lines and streamline request processing.

Furthermore, understanding the dominance of phone reporting can inform resource allocation, such as training staff to handle inquiries more effectively or investing in better call management systems. Encouraging residents to shift towards online reporting platforms could enhance reporting efficiency and data collection accuracy, ultimately leading to quicker response times and better snow removal services.

Implications for City Planning

The insights derived from the pivot table analysis have practical implications for city management. Enhancing online reporting tools could reduce the workload on call centers during busy periods, leading to cost savings and improved resident satisfaction. Additionally, targeted communication campaigns can educate residents about alternative reporting methods, fostering more diversity in reporting channels. To support this, city officials might consider investing in user-friendly web platforms and providing instructions during winter advisories, encouraging residents to report issues electronically.

Conclusion

In conclusion, creating a pivot table in Excel based on snow removal requests data provides a powerful means to analyze resident communication preferences. The analysis indicating that over 60% of requests are made via phone highlights areas for improvement in resource management and digital engagement. By understanding these patterns, city officials can implement strategic changes to enhance service delivery, reduce operational costs, and increase resident satisfaction. This example underscores the importance of data analysis and visualization tools like pivot tables in making informed urban management decisions.

References

• Microsoft Support. (2021). Create a PivotTable to analyze worksheet data. Microsoft.
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