The presentation of regression modeling and forecasting is analyzed using two approaches:
- Parsimonious Statistical Modeling, and
- Data Science Model Building.
Forecasting and model adequacy are then compared using real examples with R and Python. Suggestions and implications on the use of Data Science Regression Modeling for Six Sigma projects are discussed.