Why Should You Attend:
Participants will gain a solid understanding of important concepts and methods in statistical experiments. Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes. Several practical examples and case studies will be presented to illustrate the application of technical concepts. This webinar will prepare attendees to begin designing and conducting experiments. Attendees will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.
Design of Experiments has numerous applications, including:
Fast and Efficient Problem Solving (root cause determination)
Shortening R&D Efforts
Optimizing Product Designs
Optimizing Manufacturing Processes
Developing Product or Process Specifications
Improving Quality and/or ReliabilityAreas Covered in the Webinar:
Introduction to Experimental Design
What is DOE?
Definitions and Concepts
Sequential Experimentation
When to Use DOE
Common Pitfalls in DOE
Steps for Planning, Implementing and Analyzing an Experiment
Case Study 1
Two Level Factorial Designs
Design Matrix and Calculation Matrix
Main and Interaction Effects
Testing for Statistical Significance
Interpreting Effects
Using Center Points
Developing Mathematical Models
Developing First Order Models
Residuals /Model Validation
Solving Models
Optimizing Responses
Case Study 2
Fractional Factorial Designs (Screening)
Structure of the Designs
Confounding/Aliasing
Resolution
Analysis of Fractional Factorials
Other Designs
Participants will gain a solid understanding of important concepts and methods in statistical experiments. Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes. Several practical examples and case studies will be presented to illustrate the application of technical concepts. This webinar will prepare attendees to begin designing and conducting experiments. Attendees will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.
Design of Experiments has numerous applications, including:
Fast and Efficient Problem Solving (root cause determination)
Shortening R&D Efforts
Optimizing Product Designs
Optimizing Manufacturing Processes
Developing Product or Process Specifications
Improving Quality and/or ReliabilityAreas Covered in the Webinar:
Introduction to Experimental Design
What is DOE?
Definitions and Concepts
Sequential Experimentation
When to Use DOE
Common Pitfalls in DOE
Steps for Planning, Implementing and Analyzing an Experiment
Case Study 1
Two Level Factorial Designs
Design Matrix and Calculation Matrix
Main and Interaction Effects
Testing for Statistical Significance
Interpreting Effects
Using Center Points
Developing Mathematical Models
Developing First Order Models
Residuals /Model Validation
Solving Models
Optimizing Responses
Case Study 2
Fractional Factorial Designs (Screening)
Structure of the Designs
Confounding/Aliasing
Resolution
Analysis of Fractional Factorials
Other Designs
Speakers
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty.
