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.