Webinar: Regression Model Building for Six Sigma and Data Science. Differences and Synergies.

The presentation of regression modeling and forecasting is analyzed using two approaches:

  1. Parsimonious Statistical Modeling, and
  2. 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.

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Ernesto L. Garcia C.
Ernesto L. Garcia C.

About Ernesto Luis Garcia Contreras

Ernesto Luis Garcia Contreras has ample expertise delivering value through applied process improvement, optimization, and statistics. Ernesto has consulted and supported many large and small organizations in North America, South America, Asia and Europe.

In the academic field, Ernesto has taught quality engineering, linear algebra, mathematical optimization, statistics, and process improvement technologies at universities in the USA, Mexico and South America.

At present, Ernesto is Managing Director of Appaloosa Engineering, a consulting firm specialized in applied optimization, data science, and process improvement. He has held executive positions in agile project management and process engineering leading teams of scrum masters, data scientists, master black belts, statisticians, business architects, and industrial engineers.

Ernesto graduated from Monterrey Tech (ITESM) in Monterrey Mexico with a BS in Mechanical and Electrical Engineering. He also holds a Masters in Business Administration, and a Masters in Managerial Economics from ITESM. As a Fulbright scholar, he received a PhD in Industrial Engineering from the University of Missouri-Columbia.

Ernesto has been chair of ASQ section 1506 at Jacksonville, FL, a member of the Industrial and Manufacturing Engineering Advisory Council for Florida State and Florida A&M Universities, and member of the Supply Chain Council, and the Management Council at the University of North Florida’s Business School (Coggin College of Business).