7-2 Final Project Milestone Two Statistical Methods and Data Mining - Copy

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7-2 Final Project Milestone Two: Statistical Methods and Data Mining QSO-510-X5374 Quantitative Analysis Laura Turgeon

Statistical Methods In the PVA scenario, the statistical methods that may be the most helpful are a regression model and analysis of the variance (ANOVA). Regression is a data mining process that is used to calculate the numeric values of a data set (JavaTPoint, 2023). The predictions are then used to create estimates that in turn describe the relationships of the data’s dependent and independent variables. The attached document contains linear regression results and the associated graph that show the different variables for this scenario and their relation to the donation amount. Justification of Statistical Methods The data, in this case study, illustrated patterns in the provided demographic data and the donation amounts that were received. This historical data gives us information that can help predict donations in future campaigns. Using the ANOVA statistical method would disseminate the provided data with descriptive statistics and support the dependent variable and independent variable relationship. The regression analysis would help point out which donor groups are more likely to contribute to the donation campaign. The PVA can then ensure that those are the ones that are focused on. Using the regression analysis will help the PVA save money and hopefully increase the donation revenue. It would be the most helpful statistical method as it would give them the most specific information about the data set. They would clearly be able to tell which group of demographics has the most regular donations, and which ones tend to donate more. Data-Driven Decisions Process The PVA uses a direct mail campaign where they reach out to prospective donors and solicit said donations. Using the demographic data and comparing it to the messages sent out, the PVA can understand where they receive the highest and more frequent donations. The data will be taken and put through regression analysis and ANOVA. These tools will help determine if there is a large difference between the key variables to be able to determine a better target audience for future mailings. The two variables that really stood out were:

 Higher donation rates in AGE(s) <20  Lower donations based on HIT. Data-Driven Decisions Data Mining According to Stedman (2023), data mining is used to sort through large data sets to help identify relationships and patterns that can aid in solving business problems by data analysis. The PVA would use data mining in order to create targeted mailing campaigns to request donations. The data would include the demographic data of donors and identifying information on future donors, which would help be able to target those that may have a higher interest rate in donating. Regressive analytics would provide identifying information on those that are more likely to donate to the PVA. Data-Driven Decisions Structured vs. Unstructured There are a few key differences between structured and unstructured data. Structured data is clearly defined, searchable, and standardized, while unstructured data is usually in its original format (Smallcombe, 2023). Additionally, structured is quantitative while unstructured is qualitative. The data set in this scenario is structured data as it is well organized and searchable, and each piece is clearly defined. Data-Driven Decisions Variables In the given historical data, there are 26 predictor variables and one response variable, which is the GIFTAMNT. This amount represents the donation in dollars that was most recently received. The organization wants to see an increase in their donation totals and keep current donors. To achieve better accuracy and thus increase donation totals, the mail-out campaign needs to have a better target. In reviewing the analysis that has been done up to this point, the variables that seem to stand out include:  Number of gifts given lifetime to date  Average amount of gift to date  Age of prospective donors

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