spreadsheet models for program enrollment planning
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University of Central Florida
Spreadsheet Models for Program Enrollment
PlanningRobert L. Armacost
Director, University Analysis and Planning SupportSandra Archer
Assistant Director, University Analysis and Planning SupportUniversity of Central Florida
2005 SAIR Conference October 24, 2005
Presentation available at http://uaps.ucf.edu
Spreadsheet Models for Program Enrollment Planning 2October 24, 2005
Goals for the Presentation
Understanding of challenge of program enrollment planning in a growth environment
Understanding of alternative modeling approaches Use of composite spreadsheet model to develop initial
projections Method for estimating degree production New insight into the use of SAS and Excel features to
manage data and create reports
Spreadsheet Models for Program Enrollment Planning 3October 24, 2005
The University of Central Florida
Established in 1963 in Orlando Florida (first classes in 1968), Metropolitan Research University
Grown from 1,948 to 45,000 students in 37 years 38,000 undergraduates and 7,000+
graduates 12 regional campus instructional sites 9th largest public university
Stands for Opportunity
Doctoral intensive 92 Bachelors, 94 Masters, 3 Specialist, and 25 PhD programs
Largest undergraduate enrollment in state Approximately 1,200+ faculty and 3,100 staff Nine colleges
Arts and Sciences, Biomedical Sciences, Business Administration, Education, Engineering and Computer Science, Health and Public Affairs, Honors, Optics and Photonics, and Hospitality Management
Spreadsheet Models for Program Enrollment Planning 4October 24, 2005
Why Do Enrollment Modeling? Predicting income from tuition Planning courses and curriculum Allocating resources to academic departments Long-term master planning Admissions policies
How accurate do these predictions have to be?
See Hopkins, David S. P. and Massy, William F., Planning Models for Colleges and Universities, Stanford University Press, Stanford, CA, 1981 for additional information on enrollment planning
Spreadsheet Models for Program Enrollment Planning 5October 24, 2005
Strategic Planning Florida SUS Board of Governors
10-year strategic plan http://www.flbog.org/StrategicPlan/pdf/StrategicPlan_05-13.pdf
Degree production for the State University System 11 universities Degrees by level Meet workforce needs
Targeted degrees Critical needs in education Critical needs in health care Emerging technologies High wage/high demand
Requires degree projections by 6-digit CIP UCF growth planning
Capacity limits on Orlando campus Determine program mix at campuses
Spreadsheet Models for Program Enrollment Planning 6October 24, 2005
Enrollment Models Objective: find simplest model that predicts future
enrollment based on past enrollment levels and new students enrolling
Methods Regression (REG) Grade progression ratio method (GPR) Markov chain models (MC) Cohort flow models (CF)
Notation Nj(t) = number of students in state j at time t
fj(t) = number of students enrolling in state j at time t j = state index—stands for class level
Spreadsheet Models for Program Enrollment Planning 7October 24, 2005
Regression Models
Student inventory = predicted returning students plus expected new students
Prediction of returning students estimated by multivariate regression
N(t) = F[ Nj(t-1), fj(t-1), Nj(t-2), fj(t-2), … ] + f(t)
Returning students
New students
Spreadsheet Models for Program Enrollment Planning 8October 24, 2005
Grade Progression Ratio Ratio of students in one class level at time t to students in
next-lower class level at time t-1 Assumes
Students follow an orderly progression form one state to another All students in each state move on to next state in one time period
or drop out of the system for good Very simple model good for year-to-year projections Data readily available Not usable in higher education Estimate the GPR from historical data
aj-1,j(t) = Nj(t)/ Nj-1(t-1) Apply GPR to current enrollment level to predict next time
period enrollment
Spreadsheet Models for Program Enrollment Planning 9October 24, 2005
Markov Chain
Stochastic process Fluctuate in time because of random events System can be in various states Markov property—each outcome depends only on the
one immediately preceding it Cross-sectional outlook Transition fraction
pij = fraction of students in class i in one period that can be found
in class j in the subsequent time period
Spreadsheet Models for Program Enrollment Planning 10October 24, 2005
Cohort Flow Models
Adopt a longitudinal outlook Take account of students’ origins Consider students’ accumulated duration of stay at the
university Students are grouped into cohorts at the time they enter
the university Could be viewed as a special case of Markov chain
model where states are expanded to include origin and length of stay
Spreadsheet Models for Program Enrollment Planning 11October 24, 2005
Cohort Flow Models Based on cohort survivor fractions Enrollment in a given level is sum of products of
survivor fraction and cohort size plus new students Estimate of returning students
Cohorts typically defined for fall semester Extensive data analysis required to determine
survivor fractions (retention) Combine with semester transition fractions to
generate annual estimate
Spreadsheet Models for Program Enrollment Planning 12October 24, 2005
Combined Cohort-Markov Model
New student
s
Summer term
Stopouts &
graduates
Current Fall
Previous Falls by Cohort
New student
s
Spring term
New student
s
Stopouts &
graduatesPrevious Summer
Survivors Transition
Transition
Transition
Cohort Markov
Spreadsheet Models for Program Enrollment Planning 13October 24, 2005
UCF Approach Continued growth assumption
Headcount growth at annual 5.7% rate for past four years Overall enrollment by level
Use combined cohort-Markov model for next five years Use combined population and high school graduate growth
rate projections for years 6-10 Enrollment and degrees by program
Conduct at HEGIS code level Develop initial enrollment projections and degree projections Programs conduct review of estimates and modify projections
Comparison with last year’s projections
Spreadsheet Models for Program Enrollment Planning 14October 24, 2005
Overall Enrollment ProjectionFall Headcount Projection
33,45336,013
38,79541,185 42,391
44,32246,540
48,16749,822 51,264 52,498 53,469 54,870 56,070 57,561
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15
Historical Projection
3.1% average annual growth
Spreadsheet Models for Program Enrollment Planning 15October 24, 2005
Enrollment and Degrees by Program Initial enrollment projections based on previous five years
enrollment by HEGIS code level Use average of three methods
Linear projection model Logarithmic projection model Overall university annual growth rates applied to previous year
enrollment Adjust for planned increased enrollment in targeted programs
Estimate degree production based on current enrollment Average annual degree production rate =
DO NOT USE AVERAGE OF ANNUAL RATES Adjust for planned increased productivity in targeted programs
Sum of degrees awarded over five yearsSum of fall enrollment over five years
Spreadsheet Models for Program Enrollment Planning 16October 24, 2005
Program Enrollment Projections
This process is repeated for each program and level combination:
Bachelors, Masters, Doctoral x HEGIS level (step 1, 2, and 3)
Certificate, Unclassified Undergraduate, Unclassified Graduate x HEGIS level (step 1 and 3 only)
5 years of historical fall headcount enrollment
Step 1:Step 1:3 Modeling Methods:1) Linear2) Log3) UCF Overall
Input Data Excel Process
2005 - 2014 predicted
enrollment headcount
Output Data
Average of 3 models
5 years of historical fall headcount enrollment
Step 2:Step 2:
Calculated ratio of
degrees to enrollment
headcount for past 5 years
Input Data
Excel Process
2005 - 2014 predicted degrees
Output Data
5 years of historical degrees awarded
Input Data X2005 - 2014
predicted enrollment headcount
Step 3:Step 3:
Excel sheets distributed to colleges for adjustments
2005 - 2014 predicted enrollment headcount
2005 - 2014 predicted degrees
Excel sheets returned from
colleges to UAPS
Consolidated returned data =
2005 - 2014 enrollment
headcount and degree predictions
SAS Process
Spreadsheet Models for Program Enrollment Planning 17October 24, 2005
Program Enrollment Projections
Average of 3 models produces one
program enrollment forecast by level
from 2005 - 20014
Ratio of program enrollment to
degrees by level from 2000 - 2004
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014B: HC-Predicted 2004 24 54 107 139 139 152 164 176 188 199 210 220 231 241 251B: D-Predicted 2004 23 3 15 24 34 37 40 43 46 48 51 53 56 58 61B: HC-linear 24 54 107 138 152 197 231 265 299 333 367 401 435 469 503B: HC-log 24 54 107 138 152 165 178 190 199 208 216 224 230 237 242B: HC-UCF 24 54 107 138 152 160 164 169 174 178 181 182 187 187 193B: HC-Average 24 54 107 138 152 174 191 208 224 240 255 269 284 298 313B: HC-Negotiated 24 54 107 138 152 174 191 208 224 240 255 269 284 298 313B: Degrees 23 3 15 24 16 30 33 35 38 41 43 46 48 51 53B: D-Negotiated 23 3 15 24 16 30 33 35 38 41 43 46 48 51 53
Historical Data Projection EstimatesBachelor Degrees for Program XYZ
Step 1Step 1:: Predict Future Enrollment
Step 2:Step 2:Predict Future
Degrees
Step 3:Step 3: Distribute Program Enrollment and
Degree Prediction to Colleges for Adjustment
Step 1: Predict Enrollment
Step 2: Predict Degrees
Program XYZ - BACCALAUREATE
0
100
200
300
400
500
600
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Fall
Hea
dcou
nt a
nd A
nnua
l Deg
rees
Spreadsheet Models for Program Enrollment Planning 18October 24, 2005
Demo: Model Projections File #1: Data File
Degrees tab: Contains the number of degrees awarded by major for historical years
Enrollment Data tab: Contains historical enrollment by major Enrollment tab: Contains a pivot table of the enrollment data Contains the macro that will run
File #2: Model template file Contains the Model Sheet
File #3: Receiving file Contains template tabs for the summary sheets
End Result: One Receiving File for each college with one Model Sheet per major and fully populated summary sheets per level
Spreadsheet Models for Program Enrollment Planning 19October 24, 2005
Demo: Model Sheet Template file (.xlt)
Chose to use a template to reduce errors in the development of the process
Can include formatting, styles, text, formulas, VBA macros and custom toolbars.
The default action of an .xlt file is “new”, as opposed to “open” Using a template (.xlt) instead of a normal excel file (.xls)
prevents corruption of this template by not allowing saved changes
LookUp function in Excel to populate the data The VB code will copy and paste in the major code to cell B5 The historical data populates via “lookups”
Spreadsheet Models for Program Enrollment Planning 20October 24, 2005
Demo: Model Sheet (cont.) Enrollment Projections:
Use index for x rather than calendar year Linear Model: F(x) =Ax + b
A = SLOPE(known_y’s,known_x’s) B = INTERCEPT(known_y’s,known_x’s) Future value = MAX(A * (Future Year) + B,0)
Log Model: F(x) =Alog(x) + B A = SLOPE(known_y’s,LN(known_x’s)) B = INTERCEPT(known_y’s,LN(known_x’s)) Future value = MAX(A * LN (Future Year) + B,0)
UCF Overall Growth Year over year university wide growth
Spreadsheet Models for Program Enrollment Planning 21October 24, 2005
Demo: Model Sheet (cont.) Weighted Average of these three enrollment models =
“HC-Average” Opportunity for adjustment = “HC-Negotiated”
Degree Projections = (total number of degrees / sum of yearly enrollments) * projected enrollment
Other Excel functionalities: ISERROR( ) function used if the lookup does not exist IF( ) function creates conditional expressions for error flagging
Spreadsheet Models for Program Enrollment Planning 22October 24, 2005
Demo: Data File Macro Outer Loop on i = run code for each college
Change the pivot table in the Data File to the “ith” college Save the Receiving File as the college name
Inner Loop on j = run code for each major code within each college Puts a copy of the Model Sheet into the Receiving File Copies the major code from the “jth” row of the Data File
Enrollment tab into the Model Sheet All values of the model sheet are now populated via “lookup”
functions Copies and pastes as values the model sheet lookup functions Copies and pastes as links the model sheet data onto the level
summary tabs
Spreadsheet Models for Program Enrollment Planning 23October 24, 2005
Demo: Data File Macro (cont.) Summary tab
Array function allows for a multiple criteria lookup Type formula then control-shift-enter to create an array
function: {=SUM(IF((range1 = criteria1 )*(range2 = critiera2),(multiple
values))}
10 Colleges = 10 Excel files Summary tab Level Summary tabs Model Sheet tabs for each program
Spreadsheet Models for Program Enrollment Planning 24October 24, 2005
Demo: Data Collection 10 Files with 442 major codes x 6 levels = 2,652
separate models! Files then distributed to the college representatives
Make updates on either the detailed model sheets or the level summary sheets
College representatives then sent their files back to us for consolidation
Base SAS used to consolidate the returned Excel files SAS DDE within a macro was applied Files were all in the same layout and format (some needed
slight revising) What is SAS DDE?
Spreadsheet Models for Program Enrollment Planning 25October 24, 2005
Projection Review Format Bachelor's Summary
Ty/LyEnrollment / Degrees 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ThisYear 000819 EXCEPTIONAL EDUCATIONEnrollment 0 0 0 0 51 147 152 158 163 168 173 179 179 179 179
LastYear 000819 EXCEPTIONAL EDUCATIONEnrollment 0 0 0 0 130 140 145 150 155 160 165 170 170 170 170
ThisYear 000819 EXCEPTIONAL EDUCATIONDegrees 0 0 0 37 54 64 66 69 72 76 78 80 80 80 80
LastYear 000819 EXCEPTIONAL EDUCATIONDegrees 0 0 0 37 56 60 62 64 66 69 71 73 73 73 73ThisYear 000802 ELEMENTARY EDUCATION (GENERAL)Enrollment 838 889 908 948 963 1046 1071 1096 1120 1143 1163 1178 1202 1216 1241LastYear 000802 ELEMENTARY EDUCATION (GENERAL)Enrollment 838 889 908 948 967 988 1010 1033 1055 1075 1095 1107 1127 1137 1146ThisYear 000802 ELEMENTARY EDUCATION (GENERAL)Degrees 378 360 377 386 381 437 452 467 482 497 505 512 522 529 539LastYear 000802 ELEMENTARY EDUCATION (GENERAL)Degrees 378 360 377 383 402 411 420 430 439 447 455 461 469 473 477ThisYear 000801 EARLY CHILDHOOD EDUCATIONEnrollment 138 128 145 179 224 234 246 259 271 283 294 304 315 325 336LastYear 000801 EARLY CHILDHOOD EDUCATIONEnrollment 138 128 145 179 174 179 205 210 220 225 230 235 240 245 250ThisYear 000801 EARLY CHILDHOOD EDUCATIONDegrees 72 61 79 52 82 100 107 113 120 126 131 135 141 145 150LastYear 000801 EARLY CHILDHOOD EDUCATIONDegrees 72 61 79 52 79 82 94 96 101 103 105 108 110 112 114ThisYear 000848 ENGLISH LANGUAGE ARTS EDUEnrollment 62 64 68 67 94 96 100 104 108 111 115 118 122 125 128LastYear 000848 ENGLISH LANGUAGE ARTS EDUEnrollment 62 64 68 67 65 65 65 65 65 65 65 65 65 64 63ThisYear 000848 ENGLISH LANGUAGE ARTS EDUDegrees 18 17 21 17 24 26 28 29 31 32 33 34 35 36 37LastYear 000848 ENGLISH LANGUAGE ARTS EDUDegrees 18 17 21 17 20 20 20 20 20 20 20 19 19 19 19ThisYear 000833 MATHEMATICS EDUCATIONEnrollment 55 51 48 61 67 70 72 74 76 78 80 82 84 85 87LastYear 000833 MATHEMATICS EDUCATIONEnrollment 55 51 48 61 63 65 67 69 71 73 75 77 80 82 84ThisYear 000833 MATHEMATICS EDUCATIONDegrees 18 15 18 10 11 18 19 19 20 21 21 22 22 23 23LastYear 000833 MATHEMATICS EDUCATIONDegrees 18 15 18 10 19 20 20 21 21 22 23 23 24 25 26
Hegis
ENROLLMENT PROJECTIONSHistorical Data Projection Estimates
Spreadsheet Models for Program Enrollment Planning 26October 24, 2005
Consolidated Projections by CIPCIP Code CIP Title
Targeted (Institutional Priority Programs are indicated as such) 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13
05.0000 Area, Ethnic, Cultural and Gender Studies - - - - - 2 4 6 8 09.0102 Mass Communication/Media Studies. 125 118 118 119 119 120 120 121 121 09.0401 Journalism. 15 29 29 30 30 30 30 31 31 09.0701 Radio and Television. 89 126 134 136 137 140 140 143 143 09.0903 Advertising. 90 122 122 123 124 125 126 127 128 11.0101 Computer and Information Sciences, General.Emerging Technologies: Computer Science and Information Technology103 100 95 90 85 85 90 95 100 11.0103 Information Technology. Emerging Technologies: Computer Science and Information Technology95 93 102 112 119 126 134 141 154 13.1001 Special Education and Teaching, General.Critical Needs: Education 48 64 66 69 72 76 78 80 80 13.1202 Elementary Education and Teaching. Economic Development: High-Wage/High Demand Jobs342 437 452 467 482 497 505 512 522 13.1210 Early Childhood Education and Teaching.Economic Development: High-Wage/High Demand Jobs83 100 107 113 120 126 131 135 141 13.1302 Art Teacher Education. 18 14 14 14 15 15 16 16 17 13.1305 English/Language Arts Teacher Education.Economic Development: High-Wage/High Demand Jobs26 26 28 29 31 32 33 34 35 13.1306 Foreign Language Teacher Education. Critical Needs: Education 3 3 3 2 3 3 3 3 3 13.1311 Mathematics Teacher Education. Critical Needs: Education 13 18 19 19 20 21 21 22 22 13.1312 Music Teacher Education. Economic Development: High-Wage/High Demand Jobs9 5 6 6 7 8 8 9 10 13.1314 Physical Education Teaching and Coaching.Economic Development: High-Wage/High Demand Jobs48 41 43 48 50 52 54 55 55 13.1316 Science Teacher Education/General Science Teacher Education.Critical Needs: Education 13 16 16 16 16 16 16 16 16 13.1317 Social Science Teacher Education. Economic Development: High-Wage/High Demand Jobs39 34 34 35 36 36 37 37 37 13.1320 Trade and Industrial Teacher Education.Critical Needs: Education 15 20 21 24 26 29 31 33 35 14.0201 Aerospace, Aeronautical and Astronautical Engineering.Emerging Technologies: Mechanical Science and Manufacturing24 38 40 43 45 47 49 51 53 14.0801 Civil Engineering, General. Emerging Technologies: Design and Construction90 85 90 96 101 106 108 110 112 14.0901 Computer Engineering, General. Emerging Technologies: Computer Science and Information Technology85 97 102 107 112 118 120 122 125 14.1001 Electrical, Electronics and Communications Engineering.Emerging Technologies: Mechanical Science and Manufacturing81 102 106 111 115 119 122 125 128 14.1401 Environmental/Environmental Health Engineering.Emerging Technologies: Natural Science and Technology20 19 19 20 21 21 21 22 22 14.1901 Mechanical Engineering. Emerging Technologies: Mechanical Science and Manufacturing77 109 116 123 130 138 143 148 154 14.3501 Industrial Engineering. Emerging Technologies: Mechanical Science and Manufacturing32 33 34 35 37 38 40 41 43 15.0303 Electrical, Electronic and Communications Engineering Technology/Technician.Emerging Technologies: Mechanical Science and Manufacturing29 22 17 17 18 18 18 18 18 15.0899 Mechanical Engineering Related Technologies/Technicians, Other.Emerging Technologies: Mechanical Science and Manufacturing23 25 27 30 32 34 35 36 36 15.1202 Computer Technology/Computer Systems Technology.Emerging Technologies: Computer Science and Information Technology38 40 45 50 56 61 66 70 75 16.0101 Foreign Languages and Literatures, General. 7 4 5 5 5 5 5 5 5 16.0901 French Language and Literature. 3 7 8 8 9 10 10 11 11 16.0905 Spanish Language and Literature. 14 13 14 14 15 15 15 16 16 22.0302 Legal Assistant/Paralegal. 207 221 234 247 260 272 284 294 306 23.0101 English Language and Literature, General. 147 168 169 171 173 175 176 179 180 23.1001 Speech and Rhetorical Studies. 97 104 105 106 107 108 109 110 111 24.0101 Liberal Arts and Sciences/Liberal Studies. 500 497 521 546 570 591 613 632 654
Spreadsheet Models for Program Enrollment Planning 27October 24, 2005
SAS DDE DDE (Dynamic Data Exchange) allows the dynamic
exchange of data between Windows applications such as spreadsheets or databases
Establish a client/server relationship: SAS System acts a client by requesting data, sending data, or
sending commands to the server application Excel 2002 is the server application (any application that
supports DDE as a server can communicate with the SAS System)
Spreadsheet Models for Program Enrollment Planning 28October 24, 2005
SAS DDE (cont.)
To use DDE in SAS, issue the file name statement:
FILENAME filref DDE ‘DDE-triplet’ <DDE-options>
The DDE-triplet argument refers to the DDE external file in the following form: ‘application-name|topic!item’
(SAS Institute, 1999)
Spreadsheet Models for Program Enrollment Planning 29October 24, 2005
SAS DDE (cont.) Filename statement used to establish a DDE link to the
Excel application This will allow us to later issue commands to Excel, using the
fileref “commands” In the DDE triplet, the application-name is Excel, the
topic is SYSTEM and the item is not specifiedFILENAME commands DDE 'EXCEL|SYSTEM'; %macro importfile(dir,file,abr,College); data _null_; file commands; put "[open(""C:\&dir.\&file..xls"")]"; run;
Spreadsheet Models for Program Enrollment Planning 30October 24, 2005
SAS DDE (cont.) Once the Excel file is opened Establish a DDE link to the specified range in Excel In the DDE triplet, the application-name is Excel, the
topic is the file “&file” with the tab name “BACC-SUMMARY” and the item is the range of data from Row4, Column 1 to Row 1000, Column 25.
filename BACC1 dde "Excel|[&file..xls]BACC-SUMMARY!R4C1:R1000C25";
Spreadsheet Models for Program Enrollment Planning 31October 24, 2005
SAS DDE (cont.) Infile statement reads the data in the specified range
into a SAS data set Infile statement options:
missover specifies that SAS should continue to read in a record, even if some value are missing
notab prevents SAS from converting tabs in Excel to blanks dlm = ’09’x specifies that the file is tab delimited dsd specifies that two delimiters represent a missing value LRCL = option specifies the record length (in bytes)
data BACC2; infile BACC1 MISSOVER NOTAB LRECL=5000 dlm='09'x dsd; informat VarList $char50. YR2000-YR2014 10.0; input VarList $ YR2000-YR2014 ; run;
Spreadsheet Models for Program Enrollment Planning 32October 24, 2005
SAS DDE (cont.)
SAS can then close the file Macro calls are written for each college file that was
sent and returneddata _null_; file commands; put "[close(""C:\&dir.\&file..xls"")]"; run; %mend;
%importfile(Sent,Arts & Sciences 18May2005,CAS_sent,CAS); %importfile(Returned,Arts & Sciences 1June2005,CAS_returned,CAS);
Spreadsheet Models for Program Enrollment Planning 33October 24, 2005
SAS DDE (cont.) SAS code combines the data:
Modeled projection data that was sent to the colleges Revised projections collected from the colleges Updated data (for example, recent degrees conferred)
Comparisons are now easy to make between last year’s, this year’s, modeled, and revised projections
Exported to excel for reports Conditional formatting, Pivot tables, Charts
Further development Remove “zero” data lines Rounding issues
Spreadsheet Models for Program Enrollment Planning 34October 24, 2005
References
SAS version 8.02 is used, along with MS Excel 2002 SP3 in a Windows XP Professional V5.1 SP2 operating system.
SAS Institute Inc., SAS OnlineDoc®, Version 8, Cary, NC: SAS Institute Inc., 1999. http://v8doc.sas.com/sashtml/
Walkenbach, John, Microsoft Excel 2000 Power Programming with VBA, IDG Books Worldwide, Inc 1999.
Microsoft Office Online Assistance: Assistance > Excel 2003 > Startup and Settings > Managing Files http://office.microsoft.com/en-us/assistance/CH062527921033.aspx
Spreadsheet Models for Program Enrollment Planning 35October 24, 2005
Summary Significant variability in projections in growth mode Need to use multiple projection methods Detailed review by programs is essential Importance of comparison with historical and previous
projections Evolution from program projections to program PLAN Excel is powerful tool
Create projections Display
SAS provides excellent capability for managing data and creating reports
All plans and models available at http://uaps/ucf/edu/enrollment
Spreadsheet Models for Program Enrollment Planning 36October 24, 2005
Questions
???
Ms. Sandra ArcherAssistant Director, University
Analysis and Planning SupportUniversity of Central Florida12424 Research Parkway, Suite
215Orlando, FL 32826-3207407-882-0287archer@mail.ucf.edu http://uaps.ucf.edu
Dr. Robert L. ArmacostDirector, University Analysis and
Planning SupportUniversity of Central Florida12424 Research Parkway, Suite
215Orlando, FL 32826-3207407-882-0286armacost@mail.ucf.edu http://uaps.ucf.edu
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