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David Richmond, Denise HernandezProfessor Michael Frankel, Department of Statistics and Analytical Sciences, Kennesaw State University

ABSTRACT

Philanthropy, or the deed of voluntarily contributing to a charitable organization, is an action that is typicallyconsidered beneficial, though its execution can be controversial. While this subject raises debate, there are manyinstances where a non-profit organization has been audited and to another degree, accused of tax fraud. This analysisexamines the financial data of more than 950 charitable organizations and non-profits in order to discover commoncharacteristics and associations among different variables. This study focuses on four main variables: “Audited,”“Parent,” “Total Revenue,” and “TotalSupportRevenue.” The quantitative variables include “TotalRevenue,” the totalrevenue earned for a non-profit based on the tax year, and “TotalSupportRevenue,” the total revenue based solely onsupport, such as government support, contributions received, and foundation support for each non-profit. The othertwo variables are both categorical and include “Audited,” whether or not the organization has been audited, and“Parent,” whether or not the non-profit has a parent organization.

The primary target of this research is to exam trends and relationships between the four variables. After examining thetwo categorical variables, “Parent” and “Audited,” we notice that non-profits who do not have a parent organizationare more likely to be audited than a non-profit with a parent organization. For example, we see that for non-profitsthat have been audited, 495 (or about 98.21%) of 504 do not have a parent organization, while only 9 (or about 1.79%)of 504 were audited for non-profits with a parent organization. When testing the independence of each variable, wefound that the chi square value is 101.64 and that the p-value is less than .0001.

Taking a closer look at organizations who have been audited versus those who have not been audited, we examine itsrelationship with the organization’s total revenue. We see that of the 1000 observations, 496 (or about 50%) were notaudited. We also notice that the mean amount of total revenue for non-profits who were not audited is significantlylower ($548,113) than those who have been audited ($4,446,779). In this test, we obtain a p-value less than .0001.

When comparing both quantitative variables we see that the correlation coefficient is .83. Since the R-Square is .6910,we know that 69.10% can be explained by the linear model, while 30.90% can be explained by the residuals.

This data set was provided by culturaldata.org. It is called the cultural data profile and includes a detailed online surveyof financial, programmatic, and demographic information that cultural nonprofits fill out once annually, and use toapply for funding to multiple grant programs. I found this data set interesting because it deals specifically with non-profits in the arts—a non-profit that specializes in dance, music, or voice. The original data set contains close to 50,000observations. For the sake of this poster, 1,000 observations were randomly selected and examined.

Table 1: List of Variables and Attributes

Cultural Data of Non-Profits

Table 2:Descriptive statistics of the quantitative variables

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Department of Statistics and Analytical Sciences, Kennesaw State University

Table 3.1:Contingency table of Parent by Audited (left).

Table 3.2: Results of Chi Square Test (right).

Figure 2.2: The residuals for “TotalRevenue.”Table 4: SAS output of the mean difference in the total revenue of organizations who were audited by those who were not.

Figure 2.1: Scatter plot displaying the line of best fit between “TotalRevenue” and “TotalSupportRevenue.”

Cultural Data of Non-Profits

David Richmond, Denise Hernandez

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Figure 1: Distribution of “TotalRevenue.” Hypotheses Tests Table 6: SAS output displaying the regression model of “TotalRevenue” by “TotalSupportRevenue.”

Table 5: SAS output of the correlation coefficient.

Cultural Data of Non-Profits

David Richmond, Denise Hernandez

Department of Statistics and Analytical Sciences, Kennesaw State University

Chi Square TestThere is no relationship between being audited and having a parent organization (the variables are independent).There is a relationship between being audited and having a parent organization (the variables are dependent).

Since our P-value is less than .05, we can reject the idea that the two variables, “Audited” and “Parent” are independent (having no relationship) and assume that they are dependent (having a relationship). We can say that a non-profit with a parent organization is less likely to be audited than one without a parent organization. T-Test:

In the case of testing for the hypothesis, using a 5% significance level, to determine if there is a mean difference in the total revenue of organizations who were audited by those who were not audited, we are able to reject the notion that the means are the same. In fact, we are 95% confident that the mean difference of non-profits who were audited versus those who weren’t audited is between $1,988,384 and $5,808,948 higher, on average. Regression Analysis:

When analyzing the linear relationship between “TotalRevenue” and“TotalSupportRevenue,” we can reject the thought that the two variables are notlinearly related and assume that there is a strong, positive linear relationship.

#analyticsxCultural Data of Non-Profits

David Richmond, Denise Hernandez

Department of Statistics and Analytical Sciences, Kennesaw State University

Conclusion

In conclusion, we obtained a P-value that was less than .0001 and were able toreject the null in each test. There is no surprise that non-profits, who were audited,earned more money on average than those who were not audited. It was, however,interesting to find that having a parent company does relate to whether or not theorganization is audited or not. Overall, it makes since for the total revenue and thetotal support revenue to be linearly related since many non-profits depend heavilyon donations and contributions as a source of revenue.

In the future I could use this data set to look at time detail in order to predict andforecast based on time series. I could also further the analysis and look at the meandifference of government support based on state, city, or even the ethnic groupthat the non-profit caters to.

Appendix

*Importing Data Set;

proc import

datafile="\\Client\\C$\Users\DavidRichieRich\Desktop\CD

P.xls"

out = today dbms=xls replace;

sheet = "CDP";

getnames = yes;

run;

*Table 1;

data today2;

set today (keep = TotalRevenue TotalSupportRevenue

Audited Parent);

run;

ods rtf;

proc contents data=today2;

run;

ods rtf close;

*Table 2;

proc means data=today N min q1 mean median mode q3 max

std qrange maxdec=2;

var TotalRevenue TotalSupportRevenue;

run;

*Tables 3.1-3.2;

proc freq data=Today;

tables Audited*Parent/ nocol nopercent expected cellchi2

chisq;

run;

*Table 4 and Figure 1;

proc ttest data= Today H0=0 sides=2;

var TotalRevenue;

class Audited;

run;

*Table 5 and Figures 2.1-2.2;

proc reg data=today;

Model TotalRevenue = TotalSupportRevenue/r clm;

run;

*Table 6;

proc corr data=today;

var TotalRevenue TotalSupportRevenue;

run;