Jason Vander Weele, Analyst Lakeshore Technical College April 24, 2014 Madison College IR State-Called Meeting

What are we talking about? Why are we modeling?

Budgeting for FTEs has been difficult Process historically involves: College goals Multiple meetings, reports, discussions

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 value

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)

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

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)

What are things we can measure to help us understand FTEs?

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

1) Collect data 2) Run Multiple Linear Regression with all variables 3) Identify variables with highest importance to fit line 4) Check validity 5) Conduct simulation 6) Perform sensitivity analysis

Plus, FTE Final Values

FTEs = X UnemploymentRateManitowoc X PopulationManitowoc15to19YearOlds

FTEs = X UnemploymentRateManitowoc X PopulationManitowoc15to19YearOlds

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) = (an approximation of sigma) Therefore, there is a 98% probability that the next actual value will fall within 3*MAD = +/-34

Thus, considering the bias and the MAD we can state that the model will predict FTEs within the range of to 36.2 with a probability of 98% Therefore, we should expect to observe an error range of to 1.64% for any actual value when compared to the model. Over the period analyzed the actual error rate range was to 0.40%

Assuming Week 47 ResultCollegePrediction Model Actual (Week 47)2015 Budget Projection (Set Sept. 2013) College Goal2300Not Set by Model Actual – BudgetOver by 171 FTEsOver by 44 FTEs Actual % Error8.49%2.18% Expected % Error (from model)1.64% Actual Error – Expected Error-6.85%-0.54% Dollar Value Difference between College Projection and Model Prediction Assuming: 1 FTE = 30 credits X $ = 171 – 44 = 127 FTEs = $465,582 = financial impact known in advance In September/October 2013:

What about variation of the predictors???

This is our baseline Begin the forecasting process Leads into the budgeting process

Training data Testing data Bigger sample Expand look at other variables Deeper understanding, analysis, and interpretation

Automate the analysis – on demand? Look backwards and forwards to validate change over time Focus on BIAS and MAD as a check

LTC would be happy to share more details about the model as requested.