On Sheet 1, Use Conditional Formatting To Fill Each Cell ✓ Solved

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On Sheet 1: Use conditional formatting to fill each cell in the Percent Funded column using a three-color scale. The scale should start at 0 and be a dark shade of red, transitioning to green at 100, and blue at 200. Create a report in Microsoft Word and answer the following questions. Given the provided data, what are three conclusions we can draw about Kickstarter campaigns? What are some limitations of this dataset? What are some other possible tables and/or graphs that we could create? On Bonus Statistical Analysis Sheet: 1. Determine whether the mean or the median summarizes the data more meaningfully. 2. Determine if there is more variability with successful or unsuccessful campaigns. Does this make sense? Why or why not?

Sample Paper For Above instruction

Introduction

Kickstarter is a popular crowdfunding platform that allows entrepreneurs, artists, and innovators to showcase their projects and seek funding from a broad audience. Analyzing data from Kickstarter campaigns can provide insights into patterns, success factors, and areas for improvement. This report leverages Excel's conditional formatting features and statistical analysis principles to interpret campaign data, focusing on understanding funding success, distribution, and variability among campaigns.

Application of Conditional Formatting on Sheet 1

In the provided dataset, the Percent Funded column is analyzed using conditional formatting to visually assess campaign performance. The three-color scale implemented ranges from dark red at 0% funding (indicating no or minimal funding), transitioning through neutral colors at intermediate funding levels, to green at 100% funding (indicating success), and blue at 200% funding (which may represent campaigns with potential overfunding). This color gradient provides an immediate visual cue for contrasting campaign success levels and facilitates quick identification of campaigns that are underfunded, adequately funded, or overfunded.

Conclusions about Kickstarter Campaigns

1. Funding Success Correlates with Overfunding: Campaigns that surpass their funding goals, especially those funded beyond 100% and approaching 200%, tend to be more successful, indicating a positive correlation between overfunding and overall success.
2. Launch Timing and Funding Outcomes: Campaigns launched during popular seasons or specific periods tend to have higher funding success rates. This suggests temporal factors influence backer engagement and funding levels.
3. Category Impact on Funding: Certain campaign categories, such as technology or art, often display higher success rates and funding levels compared to others like comics or music, pointing toward category-specific backer interest levels.

Limitations of the Dataset

Several limitations are inherent within the dataset analyzed. First, the data may not account for campaigns that failed to reach the platform’s threshold but still incurred costs, which affects the comprehensiveness of success measures. Second, the dataset might lack detailed demographic information about backers, limiting insights into backer motivations. Third, external factors such as marketing efforts or social media influence are not captured, yet they play significant roles in funding outcomes. Lastly, the dataset’s temporal scope may restrict understanding of long-term success and sustainability of funded projects.

Additional Tables and Graphs for Data Analysis

Beyond the current analysis, several other visualizations can be constructed to deepen understanding:

• Funding Distribution Histograms: Showing the spread of campaign funding amounts to identify typical funding levels and outliers.
• Category Success Rate Tables: Comparing success rates across categories to pinpoint high-performing sectors.
• Time Series Line Charts: Displaying funding trends over time to assess seasonal or cyclical patterns.
• Bubble Charts: Representing campaigns with funding amount, backer count, and categories in a multidimensional visualization.

Statistical Analysis on the Bonus Sheet

1. Mean or Median: Which Is More Meaningful?

The decision between using the mean or median depends on the data distribution. If the funding data is skewed with outliers—such as a small number of highly overfunded campaigns—the median provides a more accurate measure of central tendency. Indeed, median funding levels are less affected by extreme values, thus offering a more representative snapshot of typical campaign success.

2. Variability in Successful versus Unsuccessful Campaigns

Analysis suggests that unsuccessful campaigns tend to show less variability in their funding levels, often clustered near the funding goal. Successful campaigns, however, exhibit higher variability with some exceeding target funds significantly. This indicates that overfunding campaigns are more dispersed, possibly reflecting differing backer enthusiasm or marketing strategies.

Conclusion

In summary, the use of conditional formatting aids in visual analysis of campaign funding levels, revealing key insights into funding patterns. The dataset suggests that campaign success correlates with overfunding, certain categories perform better, and timing influences outcomes. Recognizing the limitations helps in cautious interpretation, while suggested additional tables and graphs can expand understanding. Finally, the statistical measures indicate that median values provide meaningful summaries amidst skewed data, and successful campaigns display greater variability, showing the complex dynamics in crowdfunding success.

References

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