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 Presentation

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Predicting FTEs

Predicting FTEsJason Vander Weele, AnalystLakeshore Technical CollegeApril 24, 2014Madison College IR State-Called MeetingDefineWhat are we talking about?Why are we modeling?Define: The ChallengeBudgeting for FTEs has been difficultProcess historically involves:College goalsMultiple meetings, reports, discussionsDefine: The OpportunityLTC Research and Planning asked to figure out a wayA real need to take goals out of the equation, to get closer to expected outcomesWant a baseline BUDGETARY FTE valueThere are disincentives for the college to suggest a declining enrollmentDefine: The GoalDevelop model to predict FTEsAbility to refresh model as data becomes availablePredict 15 months out (Predict in February for end of next school year)

Define: BeliefsYou cant predict enrollmentEvery day predictions are made the weather, credit risk, ball gamesWe should only use predictors we can controlIf controllable variables are the best predictors, then why not > 20,000 FTEs per college?People will stop trying if we put out a predictionThe predictions rely on people giving the same efforts theyve always givenDefine: 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 populationDepends on demographicsWho attend classes at our school what factors affect this?Depends on personal life (employment, kids, attitudes, beliefs)Depends on demographic (education, age, gender)

MeasureWhat are things we can measure to help us understand FTEs?Measure: Some VariablesIdentify data that we can use that may or may not be a good predictor of FTEGather population data (age, gender, ethnicity)Gather high school graduation numbersGather unemployment data

Analyze: The Steps1) 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 DataPlus, FTE Final Values

Analyze: Step 2 Multiple Linear Regression

Analyze: Key Variable Plots

Analyze: Multiple Linear Regression Contd

Analyze: Multiple Linear Regression Contd

This, after many iterations and assumption testing2012-13 FTEs = -326.959 + 131.104 X UnemploymentRateManitowoc+ 0.291 X PopulationManitowoc15to19YearOldsAnalyze: What do we get?!?2012-13 FTEs = -326.959 + 131.104 X UnemploymentRateManitowoc +0.291 X PopulationManitowoc15to19YearOldsAnalyze: 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 actualVariation 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 = +/-34In forecasting trend is described by bias. Text is uncomfortable calculating sigma because there is not a mean you are comparing to. Well known forecasting methods in industry. Mean abs. dev. Measures difference/error between 17Analyze: 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 ResultCollegePrediction ModelActual (Week 47)20152015Budget Projection (Set Sept. 2013)21862059College Goal2300Not Set by ModelActual BudgetOver by 171 FTEsOver by 44 FTEsActual % Error8.49%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 PredictorsWhat about variation of the predictors???Analyze: Sensitivity Analysis

Improve: Overview for FutureThis is our baselineBegin the forecasting processLeads into the budgeting processImprove: Get BetterTraining dataTesting dataBigger sampleExpand look at other variablesDeeper understanding, analysis, and interpretationControl: Process ConsistencyAutomate the analysis on demand?Look backwards and forwards to validate change over timeFocus on BIAS and MAD as a checkThank you!LTC would be happy to share more details about the model as requested.