Design of Experiments elearning course
Design of Experiments
This session is geared toward the technician, engineer, scientist or manager who wants to understand how to conduct simple (but powerful) designed experiments without unnecessary statistical rigor.
Finding time for a three to five day seminar is becoming increasingly difficult. Travel problems associated with seminar attendance have increased dramatically. On-Line learning provides a cost-effective means to obtain the design of experiments knowledge you need. The course utilizes audio, video, text and graphics to provide the student with the ultimate on-line learning experience.
Finding time for a three to five day seminar is becoming increasingly difficult. Travel problems associated with seminar attendance have increased dramatically. On-Line learning provides a cost-effective means to obtain the design of experiments knowledge you need. The course utilizes audio, video, text and graphics to provide the student with the ultimate on-line learning experience.
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Training
Provided by Launsby Consulting
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- T asked: Where can I take a class to become better at analyzing data generated from designed experiments (DOE's)
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Design of Experiments elearning course
Module 1 (The Simple Facts)
DOE Definition
Process Diagram
Inputs and Outputs
Responses
Factors
Example
Factor Levels
Tree Diagram
Full Factorial Design
Design Matrix
Orthogonal Design
Data Collection
Analysis
Pareto Chart
Main Effects Plot
Hitting a Target
Contour Plot
Confirmation Runs
Module 2 (Fractional Factorial Designs)
Interactions
Definition
Example
Aliasing
Definition
Example
Fractional Factorial Example
Factors
Worksheet
Data Entry
Pareto Chart
Analysis of Variance
Squared Multiple R
P(2 tail) values
F ratio
P value
Contour Plot
Confirmation Runs
Module 3 (Optimizing Several Responses)
Multiple Responses
Mt. Bike Tire Example
Factors and Responses
Factor Levels
Design Matrix
Potential Data Collection Problems
Measurement Errors
Errors In Conducting the Experiment
Wrong assumptions regarding interactions
Process Changes
Extrapolation
Desirability Functions
Definition
Steps
Tire Example (Continued)
Data Entry
ANOVA (Tensile Strength)
ANOVA (Hardness)
Prediction Equation
Response Surface Plot
Contour Plot (Tensile Strength)
Contour Plot (Hardness)
Contour Plot (Both)
Confirmation Runs
Summary
Module 4 (Other Design Types)
Plackett-Burman Design
Example Matrix
Taguchi Designs
L4
L8
L9
L18
L12
Signal To Noise Ratio
Smaller Is Better
Larger Is Better
Nominal Is Better
Modeling Designs
Box-Behnken
Central Composite
D-optimal Designs
Summary
Module 5 (Variance Reduction)
Types of Factors
Affect the average
Affect the variation
Affect average and variation
Have no effect
Example - "Hitting the Target"
Design Matrix
Pareto Chart
Hit a Target Screen
Confirmation Runs
Example - "Reducing Variation"
Pareto Chart
Main Effects Plot
Contour Plot
Confirmation Runs
Summary
Module 6 (Using DOE Wisdom software)
Opening Software
Design Definition Screen
Experimental Objective
Factor Definition
Response Definition
Design Types
Worksheet Window
Data Definition Window
Statistical Analysis
ANOVA
Hit a Target
Graphical Analysis
Pareto Chart
Scatter Plot
Main Effects Plot
Interaction Plot
Contour Plot
Response Surface Graph
Confirmation Runs
Summary
Module 7 (Using Minitab Software)
Opening Minitab Software
Starting A New Project
Experimental Objectives
Design Type
Factor Definition
Data Window
Response Definition
Printing a Worksheet
Data Entry
Analysis
Pareto Chart
Normal Probability Chart
Estimated Effects and Coefficients Table
Fit a New Model
Residual Plot
Main Effects Plot
Interaction Plot
Contour Plot
Surface Plot
Conclusion
DOE Definition
Process Diagram
Inputs and Outputs
Responses
Factors
Example
Factor Levels
Tree Diagram
Full Factorial Design
Design Matrix
Orthogonal Design
Data Collection
Analysis
Pareto Chart
Main Effects Plot
Hitting a Target
Contour Plot
Confirmation Runs
Module 2 (Fractional Factorial Designs)
Interactions
Definition
Example
Aliasing
Definition
Example
Fractional Factorial Example
Factors
Worksheet
Data Entry
Pareto Chart
Analysis of Variance
Squared Multiple R
P(2 tail) values
F ratio
P value
Contour Plot
Confirmation Runs
Module 3 (Optimizing Several Responses)
Multiple Responses
Mt. Bike Tire Example
Factors and Responses
Factor Levels
Design Matrix
Potential Data Collection Problems
Measurement Errors
Errors In Conducting the Experiment
Wrong assumptions regarding interactions
Process Changes
Extrapolation
Desirability Functions
Definition
Steps
Tire Example (Continued)
Data Entry
ANOVA (Tensile Strength)
ANOVA (Hardness)
Prediction Equation
Response Surface Plot
Contour Plot (Tensile Strength)
Contour Plot (Hardness)
Contour Plot (Both)
Confirmation Runs
Summary
Module 4 (Other Design Types)
Plackett-Burman Design
Example Matrix
Taguchi Designs
L4
L8
L9
L18
L12
Signal To Noise Ratio
Smaller Is Better
Larger Is Better
Nominal Is Better
Modeling Designs
Box-Behnken
Central Composite
D-optimal Designs
Summary
Module 5 (Variance Reduction)
Types of Factors
Affect the average
Affect the variation
Affect average and variation
Have no effect
Example - "Hitting the Target"
Design Matrix
Pareto Chart
Hit a Target Screen
Confirmation Runs
Example - "Reducing Variation"
Pareto Chart
Main Effects Plot
Contour Plot
Confirmation Runs
Summary
Module 6 (Using DOE Wisdom software)
Opening Software
Design Definition Screen
Experimental Objective
Factor Definition
Response Definition
Design Types
Worksheet Window
Data Definition Window
Statistical Analysis
ANOVA
Hit a Target
Graphical Analysis
Pareto Chart
Scatter Plot
Main Effects Plot
Interaction Plot
Contour Plot
Response Surface Graph
Confirmation Runs
Summary
Module 7 (Using Minitab Software)
Opening Minitab Software
Starting A New Project
Experimental Objectives
Design Type
Factor Definition
Data Window
Response Definition
Printing a Worksheet
Data Entry
Analysis
Pareto Chart
Normal Probability Chart
Estimated Effects and Coefficients Table
Fit a New Model
Residual Plot
Main Effects Plot
Interaction Plot
Contour Plot
Surface Plot
Conclusion
About The Training Provider: Launsby Consulting
Launsby Consulting - Since 1991, Launsby Consulting has provided applications oriented training, software, books, tools and consulting services in the areas of Design of Experiments, Process Validation, Statistical Process Control, Six Sigma, Neural Networks, Fuzzy Logic, Design for Six Sigma and Data Mining.
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