Enrollment Forecasting Approaches for Open Admission Institutions
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Transcript of Enrollment Forecasting Approaches for Open Admission Institutions
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Enrollment Forecasting Approaches for Open Admission Institutions
R. Ty JonesDirector of Institutional ResearchColumbia Basin College
PNAIRP Annual ConferencePortland, Oregon November 7, 2012
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Links
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If you would like to follow along with the data and techniques and presentation, here are the links.
http://dl.dropbox.com/u/9234919/2012_3Way.xlsx
http://dl.dropbox.com/u/9234919/2012_MP.xlsx
http://dl.dropbox.com/u/9234919/SPSSEnrollment.sav
http://dl.dropbox.com/u/9234919/SPSSMLR.sav
http://dl.dropbox.com/u/9234919/2012_Forecast_Data.xlsx
http://dl.dropbox.com/u/9234919/20121106_Forecast_Workshop.pptx
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OverviewApproximate Timeline
Rational and pragmatic philosophy to enrollment forecasting (5 Minutes)Forecasting basics (5 Minutes)Linear Regression approaches (SLR) (15 minutes)Fitted Curve approaches (CLR) (10 Minutes)Multivariate Linear Regression (MLR) (20 minutes) Autoregressive–moving-average models (ARIMA) (20 minutes)Data imputation (10 minutes)Mixed methods (10 minutes)Other approaches (5 minutes)Forecast weighting (5 minutes)Presenting the data (5 minutes)Conclusion, questions and answers (10 minutes)
That’s 120 minutes plus a break to fit into 90 minutes! So, lets go!!!!
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Philosophy
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• Predicting the future is hard! • Forecasting is easy.• There is no such thing as a perfect forecast.• A forecast is only as good as the data that goes into it.• All forecasting methods have strengths.• All forecasting methods have weaknesses.• E pluribus unum!• If it doesn’t make sense, don’t use it.• If you can’t explain it, don’t use it.• Prepare, prepare, prepare!
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Basics
“Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be an estimation of some variable of interest at some specified future date.” - Wikipedia
Forecasting requires process and estimation. Anything else is WAG!
The processes chosen by institutional research must be founded on statistical and/or mathematical principles. That means data must be at its core to have any validity.
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Linear Regression
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Linear regression uses the process of least squares to model the relationship between a dependent variable and an explanatory variable.
Strengths:• Robust• Minimal data requirements• Easily explained
Weaknesses:• Variances make short and long
term estimates difficult• Tends to over simplify trends
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Fitted Curve
Fitted curve regression operates on a similar basis as linear regression. Instead, transformations to the data optimize the least square process to fit an equation line dictated by the transformation.
Strengths:• In many cases, curve fitting better fits time series
data.• Provides stronger explanation than linear models.Weaknesses:• Variances can force large margins of error in
making estimates.• Some curve fitting may be significant, but not make
actual sense.
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Multivariate Linear Regression
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Multivariate linear regression uses the process of least squares to model the relationship between a dependent variable and multiple explanatory variables.
Strengths:• Robust• High explanatory value• Once model is established, allows a lot of different “what if”
scenarios to be looked at.Weaknesses:• Extending the model for significant independent variables into the
future can be difficult.• Interactions can make model interpretation difficult.• Resulting models can be very complex.
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ARIMA
An autoregressive–moving-average model uses a combination of data smoothing and regression in time series data. Unlike true regression approaches, uses only dependent data to estimate future outcomes.
Strengths:• Often better reflects cyclical dependent data.• Lack of dependence on explanatory factors allows
sbetter long term projections.Weaknesses:• Getting the correct model can be very difficult• Explaining the model can be difficult.• Sometimes, no model can be generated.
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Imputation
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There are a variety of data imputation techniques. All aim at filling holes or extending estimates. All use various formulae to look for patterns in existing data to estimate missing data.
Strengths:• Not as effected by variances so short term and long term
estimates are more consistent.• Mathematically more straight forward.Weaknesses:• Can miss cyclic patterns.• Using the wrong imputation for the data can result in
large out of range errors.
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Mixed Methods
Mixed method models use a combination of forecasting approaches to arrive at estimations.
Strengths:• Mixing methods may provide data smoothing to highly
variable data.• May allow access to estimates that a single model
approach would not allow.Weaknesses:• Can result in amplified error and variance of estimates.• Explaining the model can be difficult.• Measuring confidence in the model is difficult
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Other Issues
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Other forecasting methods•Bayesian estimate models•Hot-Decking•Random Wandering Models
Forecasting weighting
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Presenting The Data
2012 2013 2014 2015 2016 2017 2018 2019 2020 20217000
7500
8000
8500
9000
9500
77707913 7960
80188050
81348220 8247
83158286
2012 Longterm Forecast
Enro
llmen
t
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Finish
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Conclusion, questions and maybe some answers…
Thank you for participating!