predicting ftes
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Predicting FTEs. Jason Vander Weele, Analyst Lakeshore Technical College April 24, 2014 Madison College IR State-Called Meeting. Define. What are we talking about? Why are we modeling?. Define: The Challenge. Budgeting for FTEs has been difficult Process historically involves: - PowerPoint PPT PresentationTRANSCRIPT
Predicting FTEsJason Vander Weele, AnalystLakeshore Technical CollegeApril 24, 2014Madison College IR State-Called Meeting
Define
•What are we talking about?•Why are we modeling?
Define: The Challenge
•Budgeting for FTEs has been difficult•Process historically involves:•College goals•Multiple meetings, reports, discussions
Define: The Opportunity
• LTC Research and Planning asked to “figure out a way”• A real need to take “goals” out of the
equation, to get closer to “expected” outcomes• Want a baseline BUDGETARY FTE
valueThere are disincentives for the college to
suggest a declining enrollment
Define: The Goal
•Develop model to predict FTEs•Ability to refresh model as data becomes available• Predict 15 months out (Predict in February for end of next school year)
Define: Beliefs
• You can’t predict enrollment•Every day predictions are made – the weather, credit risk, ball games
• We should only use predictors we can control• If controllable variables are the best predictors, then why not > 20,000 FTEs per college?
• People will stop trying if we put out a prediction•The predictions rely on people giving the same efforts they’ve always given
Define: What are FTEs, anyway?• At a basic level, FTEs are a function of the people
in a district who attend classes at our school
• “People in a district” – who are they?•Depends on the population•Depends on demographics
• “Who attend classes at our school” – what factors affect this?•Depends on personal life (employment, kids, attitudes, beliefs)•Depends on demographic (education, age, gender)
Measure
•What are things we can measure to help us understand FTEs?
Measure: Some Variables
• Identify data that we can use that may or may not be a good predictor of FTE•Gather population data (age, gender, ethnicity)•Gather high school graduation numbers•Gather unemployment data
Analyze: The Steps
1) Collect data2) Run Multiple Linear Regression with all variables3) Identify variables with highest importance to fit line4) Check validity5) Conduct simulation6) Perform sensitivity analysis
Analyze: Step 1 Collect Data
• Plus, FTE Final Values
Analyze: Step 2 Multiple Linear Regression
Analyze: Key Variable Plots
Analyze: Multiple Linear Regression Cont’d
Analyze: Multiple Linear Regression Cont’d
This, after many
iterations and assumption
testing2012-13 FTEs = -326.959
+ 131.104 X UnemploymentRateManitowoc+ 0.291 X PopulationManitowoc15to19YearOlds
Analyze: What do we get?!?
2012-13 FTEs = -326.959 + 131.104 X
UnemploymentRateManitowoc +0.291 X
PopulationManitowoc15to19YearOlds
Analyze: How Precise are We?
• Trend is described by Bias = -20 or Ave Bias = -2.2 •The trend is negative, meaning over the observed period of 9 years, the model was 20 FTEs higher than actual
• Variation is described by the Mean Absolute Deviation (MAD) = 11.33 (an approximation of sigma)•Therefore, there is a 98% probability that the next actual value will fall within 3*MAD = +/-34
Analyze: How Precise are We?
• Thus, considering the bias and the MAD we can state that the model will predict FTEs within the range of -31.8 to 36.2 with a probability of 98%
• Therefore, we should expect to observe an error range of -1.44 to 1.64% for any actual value when compared to the model.
• Over the period analyzed the actual error rate range was -1.24 to 0.40%
Analyze: Is That Good? (PRELIMINARY)
Assuming Week 47 Result College Prediction Model
Actual (Week 47) 2015 2015
Budget Projection (Set Sept. 2013) 2186 2059
College Goal 2300 Not Set by Model
Actual – Budget Over by 171 FTEs Over by 44 FTEs
Actual % Error 8.49% 2.18%
Expected % Error (from model) 1.64% 1.64%
Actual Error – Expected Error -6.85% -0.54%
Dollar Value Difference between College Projection and Model PredictionAssuming: 1 FTE = 30 credits X $122.20= 171 – 44 = 127 FTEs= $465,582 = financial impact known in advance
In September/October 2013:
Analyze: Variation of Predictors
•What about variation of the predictors???
Analyze: Sensitivity Analysis
Improve: Overview for Future
•This is our baseline•Begin the forecasting process•Leads into the budgeting process
Improve: Get Better
• Training data• Testing data•Bigger sample• Expand look at other variables•Deeper understanding, analysis, and interpretation
Control: Process Consistency
•Automate the analysis – on demand?•Look backwards and forwards to validate change over time•Focus on BIAS and MAD as a check
Thank you!
LTC would be happy to share more details about the model as requested.