The purpose of this project is to analyze the events of Kickstarter's plays by month starting in 2010 till 2017.

The analysis of Outcomes Based on Launch Dates was performed by creating a pivot table and pivot chart that demonstrated the successful, failed and canceled Kickstarter plays throughout the months to display visually the peaks and downs of each type of outcome.

The analysis of Outcomes Based on Goals was performed by creating a table and a line chart that shows the total number of successful, failed and canceled Kickstarter plays based on goal ranges, as well as the overall total amount of projects and the percentage of each type of outcomes represented from the total of projects of a specific range.

Some difficulties were encountered while using the IFS formula and adding the range/criteria, especially when I had to add 3 different of them. The obstacle was overcome by trying quite a few times, using google for examples of the formulas, watching a couple of videos, and reaching out to colleagues from the course. Also when trying out the IFS formula, an attempt was made to filter the subcategory 'Plays' directly from the dataset, which didn't work quite well, so the solution was to filter it by adding it as a range/criteria inside the IFS formula.

1. What are two conclusions you can draw about the Outcomes based on the Launch Date?

• No matter which month, successful Kickstarter' plays were higher than failed and canceled ones.

• For the successful Plays there is a high peak in May and then a low peak in December. While the failed Plays had very little change throughout the year. In addition, the canceled ones also had little to no change and were always on very low numbers.

2. What can you conclude about the Outcomes based on Goals?

• By the point where the goal surpasses $20,000, the rate of successful Plays started to decline and get unbalances where it then goes up again on the $35,000 range but then it falls to 0% on the $45,000 mark. So it seems like to have constant successful Kickstarter results, the goal shouldn’t be above $20,000.

3. What are some limitations of this dataset?

• One limitation could be the fact that the dataset is 5 years old already could be a problem, because in this case where the subject of analysis is about Kickstarters, outdated data could implicate ending on a result that doesn't represent the present scenario.
• The fact that only a set of countries is been used could be another limitation as it is unknown if it is just a subset or the complete set of data available.
• In addition, there is a limitation where the currency is not consistent throughout the dataset, possibly making them unrealistic to compare considering that the real value is different for each country.

4. What are some other possible tables and/or graphs that we could create?

• The same analysis could be added for each country separately, so they can be analyzed individually. Also, a table could be added with the conversion of the currencies and how much value they hold for each country.