Title page for ETD etd-010207-171303

Document Type masters report

Author Name Jin, Yi

URN etd-010207-171303

Title Regression Analysis of University Giving Data

Degree MS

Department Mathematical Sciences

Advisors
Joseph D. Petruccelli, Advisor

Keywords
logistic regression
prediction
data mining
least squares regression

Date of Presentation/Defense 2006-12-05

Availability unrestricted

Abstract
This project analyzed the giving data of Worcester Polytechnic Institute’s
alumni and other constituents (parents, friends, neighbors, etc.) from fiscal
year 1983 to 2007 using a two-stage modeling approach. Logistic regression
analysis was conducted in the first stage to predict the likelihood of giving for
each constituent, followed by linear regression method in the second stage
which was used to predict the amount of contribution to be expected from
each contributor. Box-Cox transformation was performed in the linear
regression phase to ensure the assumption underlying the model holds.
Due to the nature of the data, multiple imputation was performed on the
missing information to validate generalization of the models to a broader
population.
Concepts from the field of direct and database marketing, like “score” and
“lift”, were also introduced in this report.

Files
Report.pdf

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Document Type	masters report
Author Name	Jin, Yi
URN	etd-010207-171303
Title	Regression Analysis of University Giving Data
Degree	MS
Department	Mathematical Sciences
Advisors	Joseph D. Petruccelli, Advisor
Keywords	logistic regression prediction data mining least squares regression
Date of Presentation/Defense	2006-12-05
Availability	unrestricted
Abstract This project analyzed the giving data of Worcester Polytechnic Institute’s alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due to the nature of the data, multiple imputation was performed on the missing information to validate generalization of the models to a broader population. Concepts from the field of direct and database marketing, like “score” and “lift”, were also introduced in this report.
Files	Report.pdf