This presentation is for a Regression Analysis class complete for an MMR degree at the University of Georgia. The project is to find key attributes of a university or college that can be helpful in explaining or predicting tuition.

1. Predicting College Tuition
A Multiple Regression Study by Michael Bystry

2. Problem Statement
 Numerous publications provide information on
various institution factors/metrics that students use
to compare colleges/universities and their tuitions.
 Given the number of publically available factors, it
can difficult to compare the factors and tuitions
across institutions.
 By knowing which factors are may influence
tuition, colleges/universities can determine if their
tuition is fairly valued as compared to their
competition.
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3. Research Objectives
 To use publicly available data on universities to
determine which are important predictors of tuition
rates
 To determine the how changes in the predictors may
reflect increases/decreases in tuitions
 To construct a model that universities may use to
competitively set tuition
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4. Research Design
 Sources: 1,238 colleges from U.S. News & World Report's 1995
Guide to Americas Best Colleges and AAUP's (American Association
of University Professors) 1994 Salary Survey.
 Variables included in the data set include:
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5. Executive Summary
 The base price declines as the category of university changes from
small, medium, and high new student enrollment.
 The higher the value in the following, the higher the tuition:
 Faculty salaries
 Graduation Rate
 Percent of faculty with Ph.Ds
 Percent of full time students enrolled
 Percent of alumni giving (for public colleges only)
 The higher the value in the following, the lower the tuition:
 Student faculty ratio
 Unimportant predictors of tuition are:
 Percent of new students from the top 25% of high school class
 Fraction of applicants accepted for admission
 Private colleges are able to increase tuition at a higher rate than public ones
based on improvements in Student/faculty ratio, Graduation Rate, and % of
faculty with Ph.D.
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6. Some factors affect tuitions at private
institutions more than at public ones
Private institutions see greater tuition benefits for improvement in
graduation rates, faculty with Ph.D’s, and student faculty ratio as
compared to public institutions.
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7. Families are willing to pay higher tuition for
factors believed to offer a better education
 Factors for willingness to pay higher tuition in the parents
mind
 Higher salaries, more Ph.D’s - - > better instruction in the
classroom
 Higher alumni giving - - > alumni valued the experience of
attending the college, so my child will too
 Higher graduation rate - -> professors work hard to ensure
students pass
 Higher full time students percentage - - > high quality of
student life
 Factors that detract from willingness to pay higher tuition in
the parents mind
 Higher enrollment - -> my child is just a number
 Higher student/faculty ratio - -> my child will not get the attention
he/she needs
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8. A model can help evaluate when tuition is
out of sync with the competition
By applying the dollar figures on the previous
slide, tuition prices can be estimated and compared to
other institutions
Given how well this institution compares to the
others within a similar predicted price range, this
college’s tuition is underpriced - a good value for
students
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9. Limitations and Conclusions
 Limitations
 Model is time sensitive – uses 1994/95 data
 Others factors that could be important to evaluating tuition were
not in the data set
 Regional cost of living differences
 Reputation
 Model could be improved by limiting data to schools of great
similarity
 Small liberal arts school only; Law schools only
 Allows for a more direct comparison against close competitors
 Conclusions
 Higher enrollment and high student/faculty ratio are
consideration for lower tuition.
 Higher faculty salaries and credentials, higher graduation
rates, higher percentage of full time students, and higher
alumni donations are consideration for higher tuition.
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10. Appendix
Background on Methodology
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11. Methodology
 All work performed in SAS
 Data cleaned of incomplete records
 Tests for normality and independence conducted; no data transformation need
 Test multicollinearity conducted; none found
 Data then randomly split into training set and validation set
 Model was trained using the training set. Best set of factors found using “all
possible regression” option
 Best 3 models selected based on Adjusted R-squared and Mean Square Error
(MSE)
 Final model selected based on the lowest MSE of the best three models using the
validation set
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