1. Nick Onopa, Charles Jones, Kathy Anderson
Business Location Analysis
regional, economic, labor factors Information Services industry DC/Arlington/Alexandria, VA Beach, Richmond regions
Group 1 –
Nick Onopa, Charles Jones, Kathy Anderson
BIT 5534 – Spring 2016
Artwork by Soniken

2. Agenda Identify Business Problem
Acquire, aggregate and pre-process data
Explore & model data (alternatives)
Pick best model and test
Predict from best model
Conclusion, Q & A

3. Business problem
Goal
Data driven approach to improve on corporate geo-location decisions.
Stakeholder
Client: Information Services Companies
Challenge
Find favorable unique regional factors conducive for new IS companies.
Opportunity
Fact-based decision making - analytics to open/maximize a new business.

4. Researched data regions in Virginia - USA

5. CRISP-DM – a standardized approach
Artwork by Hortonworks.com

6. DATA PREPARATION Acquired the following data
518 Industry Code (Data Processing, Hosting, and Related Services )
Regional Labor Costs
Regional Jobs Earnings
Regional and local US Census statistics
Aggregated the data into 36 predictor variables
Pre-processed dataset to reduce variables from 36 to 23

7. Explore & model data (alternatives)
Findings: graph of average annual earnings in data industry, and percentage of population with a graduate degree or above

8. Pick best model and test
Pick Best model type from all model types that were run.
Fit Least Squares model
Neural Network model
Stepwise Regression model
Validate goodness of fit of selected best model type.
K –Fold validation
Test the predictive performance of the best model to obtain an unbiased Rsquare value.

9. Predict from best model
Fit Least Square
- Loudoun County

10. Conclusion, Q & A Findings Higher Education Level
Average Annual Earnings
Fit Least Squares effect screening model had an RSquare value of .9492 

11. JMP Validation of Model

12. Excel Validation of Model