The project’s *Key Performance Indicators or KPIs* include the data analysis expectation of >=95% for the Classification Prediction Model. According to Work Group for Community Health and Development (2018), collecting and analyzing data (e.g., Qualitative and/or Quantitative) will reveal “relationships, patterns, trends” (para. 6) between the variables and help to determine if the various independent variables or intervention variables caused a significant change for the dependent variable’s result based on a reasonable level of significance or probability of having the correct result (e.g., .05 significance level or a 5% chance of having the wrong result or a 95% probability of having an accurate result).

The following are project KPIs that help to measure the success of the final data set and the predictive model:

- Total Frequency or Distribution Counts
- Sum Aggregate Amounts
- Average Aggregate Amounts
- Percentage Aggregate Amounts
- >=95% Significance Level (0.05)
- Predicted Target Value vs. Actual Target Value Accuracy
- Valid Average Squared Errors (Least)

*According to Shuttleworth (2008), an alpha of 5% or 0.05 (95%) confidence level or statistical significance criteria is comfortable for most research papers (lower for more precision) according to the Author, which the null hypothesis (H0) that is less than cut-off P-values of 0 – 1 which the variable “should” be rejected from the analysis based on the strength of the variable’s relationship with the target variable. *

**Correlation Significance Criteria**

The statistical significance criteria for determining the strength of a variable’s relationship with the target variable can be determined with the Correlation KPI, according to Wilson (2009): Variables are correlated or have a relationship with each other when a change in one quantifiable variable causes a change in another quantifiable variable and vice versa, which the correlation value helps determine the strength of the relationship and whether the relationship is a positive correlation which increases in the same direction or a negative correlation which decreases occur in an opposing direction in the quantifiable data (i.e., +1 indicates positive correlation and -1 indicates negative correlation, where r = 0). The following is a useful rule of thumb provided by the Autor for determining the strength of the correlation relationship (para. 10):

**Value of r = Strength of the Relationship**

- If r = -1.0 to -0.5 or 1.0 to 0.5 = Strong
- If r = -0.5 to -0.3 or 0.3 to 0.5 = Moderate
- If r = -0.3 to -0.1 or 0.1 to 0.3 = Weak
- If r = -0.1 to 0.1 = None or very weak

**References**

Shuttleworth, M., & Wilson, L. T. (2008, Mar 17). *Research Hypothesis.* Retrieved from, https://explorable.com/research-hypothesis

Wilson, L. T. (2009, May 2). *Statistical Correlation.* Retrieved from, https://explorable.com/statistical-correlation

Work Group for Community Health and Development. (2018). *Chapter 37: Section 5. Collecting and Analyzing Data.* Retrieved from, http://ctb.ku.edu/en/table-of-contents/evaluate/evaluate-community-interventions/collect-analyze-data/main