1. GLINA ARTS A FORECAST ON FALL SESSION ENROLLMENT ALA AL-LOZI ANTHONY ALEXANDER MATTHEW COKER TOREY HERZOG

2. PROBLEM  Apply statistical techniques to the Glina Arts data provided to predict demand for courses and course media types for Fall2016  Agenda 1. How the problem was approached 2. What was considered 3. Final 2016 Fall enrollment prediction 4. Recommendations 5. Conclusions

3. APPROACH Step One: Decide what forecasting method to predict the demand of Fall session 2016  Exponential Triple Smoothing  Time Series – Registration Date  Demand – Fall Session Enrollment

4. APPROACH Step Two: Evaluated how registration date connected to fall session enrollment Adult Dataset: August 15, 2014 to November 18, 2014 Camps Only Dataset: August 18, 2014 to November 21, 2014 Conclusion: Registration from 3 rd week of August to 3 rd week in November indicates count of Fall Session enrollees

5. APPROACH Step Three: Create a pivot table to obtain a total number of individuals enrolled in each course media type  Organize pivot table were a count of enrollees per week is indicated by course media type

6. APPROACH Step Four: Enter the enrollee count to its corresponding week in a new dataset that includes every week of the original data range to each course media type of Adult Classes and Camps Only

7. APPROACH Step Five: Run a Exponential Triple Smoothing Forecast to each new course media type worksheets from Adults Classes and Camps Only in Excel 2016

8. WHAT WAS CONSIDERED Models  Linear Regression  Would not have given the total number of people registered by course or course media type  Exponential Single Smoothing  Suitable for forecasting data with no trend or seasonal pattern  Exponential Triple Smoothing  Time series forecast – appropriate for dataset  Considers Seasonality  Easy to use one Excel 2016

9. TRIPLE EXPONENTIAL SMOOTHING This method is comprised of several statistics that are required for accurate forecasting:  Alpha  Data Smoothing Factor  Desired Level: ≤0.05  Probability of predicting Type I error  Beta  Trend Smoothing Factor  Desired Level: ≤0.20  Probability of predicting Type II error  Gamma  Seasonal change smoothing factor  Ordinal data correlation

10. This method is comprised of several statistics that are required for accurate forecasting:  Mean Absolute Scaled Error (MASE)  Measures the accuracy of the forecasts  Symmetric Mean Absolute Percentage Error (SMAPE)  Measures the accuracy based on the error percentage  Mean Absolute Error (MAE)  Measures how close the forecasts are to the eventual outcomes  Root Mean Square Error (RMSE)  Measures the average of the square roots of the errors and deviations TRIPLE EXPONENTIAL SMOOTHING

11. WHAT WAS CONSIDERED Demand  Course  Only small numbers of people enrolled by course - no trend would have been seen using ETS by course  Course Media Type  More appropriate way to see a trend compared to evaluating each course

12. FINAL PREDICATION Adult Classes – Woodturning

13. FINAL PREDICATION Camps Only – Metals

14. FINAL PREDICATION

17. CONCLUSION  Adults Classes  No courses had an Alpha ≤ 0.05  Camps Only  “Camps” is the only course with an Alpha ≤ 0.05 The data is not very reliable. It is difficult to make an accurate prediction.

18. RECOMMENDATIONS  Provide Current Data  Current data will allow a more accurate prediction  Dataset is of low quality  Significant cleaning was made in past data project.  Can conclude there are possible data entry errors that may have effected the results of the forecasting analysis  Implement more stringent governance of data  Train data collectors on how to properly gather clean data  Consider creating a locked Excel document with dropdowns  Clarify “No Season Specified” entry under “Season”  Majority of “No Season Specified” entries fell under the second week of August 2013 to the third week of November 2013  Entries appear to be Fall Session data, but the group felt uncomfortable making this assumption

19. QUESTIONS