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Enrollment Forecasting Approaches for Open Admission Institutions

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Enrollment Forecasting Approaches for Open Admission Institutions. R. Ty Jones Director of Institutional Research Columbia Basin College PNAIRP Annual Conference Portland, Oregon November 7, 2012. Links.

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slide1

Enrollment Forecasting Approaches for Open Admission Institutions

R. Ty Jones

Director of Institutional Research

Columbia Basin College

PNAIRP Annual Conference

Portland, Oregon November 7, 2012

slide2

Links

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

slide3

Overview

  • Approximate 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!!!!
slide4

Philosophy

  • 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!
slide5

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.

slide6

Linear Regression

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
slide7

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.
slide8

Multivariate Linear Regression

  • 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.
slide9

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.
slide10

Imputation

  • 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.
slide11

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
slide12

Other Issues

  • Other forecasting methods
    • Bayesian estimate models
    • Hot-Decking
    • Random Wandering Models
  • Forecasting weighting
slide14

Finish

Conclusion, questions and maybe some answers…

Thank you for participating!

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