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DOE
1. Introduction to Design of Experiments
1. What is Design of Experiment
2. Step 1 of DOE Introduction Hypothesis Research Question
3. Step 2 of DOE Method
4. Step 3 of DOE Results by Analyzing the Data
5. Step 4 of DOE Contextual Conclusion
6. Reference for Module 1 Intro to DOE
2. Hypothesis Testing/ Inferential Statistics/ Analysis of Variance ANOVA
0. All Data Module 2 Hypothesis Testing
1. What is Hypothesis Testing
2. Single Population Testing
3. Single Sample Z-Test
4. Single Sample T-Test
5. Population Proportion Test Single Sample
6. Comparing Two Populations Hypothesis Testing
7. Two Sample Z-Test
8. Two Sample T-Test Equal Variance
9. Two Sample T-Test Unequal Variance
10. Paired T-Test (Matched Pair/Repeated Measure)
11. Two Sample Population Proportion Test
3. One Way/Single Factor ANOVA
0. All Data Module 3 CRD Single One-Way ANOVA
1. What is One Way/Single Factor ANOVA
2. Fixed Effect Model Analysis Basics for One-Way ANOVA
3. Example One-Way/Single-Factor Fixed Effect Completely Randomized Design
4. Diagnostic, Adequacy & Data Quality Check Fixed Effect One Way ANOVA
5. Random Effect Model Analysis Bacis for One-Way ANOVA
6. Example Problem Random Effect Model
7. Diagnostic, Adequacy, & Data Quality Check Random Effect One Way ANOVA
8. Reference
4. Randomized Complete Block, Latin Square, and Graeco-Latin Design
0. All Data Module 4 RCBD Graeco Latin Square Design
1. What is Randomized Complete Block Design (RCBD)?
2. Randomized Complete Block Design Example Problem
3. Randomized Complete Block Design (RCBD) vs Completely Randomized Design
4. Why Randomized Complete Block Design is so Popular?
5. Latin Square Design of Experiments
6. Latin Square Example Problem
7. Graeco-Latin Square Design of Experiments
8. Graeco-Latin Square Example Problem
9. Reference
5. Factorial Design of Experiments
0. All Data Factorial Design of Experiment
1. What is a Factorial Design of Experiment?
2. Understanding Main Effects?
3. Understanding Interaction Effects?
4. How to Develop the Regression Equation from Effects?
5. How to Fit a Response Surface?
6. How to Construct the ANOVA Table from Effects?
7. Practice Problem
6. 2K Factorial Design of Experiments
1. What is 2K Design
2. Layout/Graphical Representation 22 Design
3. Understanding Factor Effects
4. Contrast, Effect, Estimate, Sum of Square, and ANOVA Table 22
5. Practice Problem 22
6. How to Design 2k Experiment
7. Develop Treatment Combinations 2K Design
8. Develop Generic Formulas 2K Design
9. Manual Analysis Using MS Excel 2K Experiments
10. MS Excel, Minitab, SPSS, and SAS
11. Practice Problem 2k
12. 2K Factorial Design of Experiments References
7. Blocking and Confounding in 2K Design
1. What is Blocking
2. What is Confounding
3. Confound an Effect Using -1/+1 Coding System
4. How to Replicate
5. Confound Two Effects Using -1/+1 Coding System
6. Confound Three Effects Using -1/+1 Coding System
7. Confounding and Blocking Using Linear Combination Method 0/1 Coding
8. Confound Two Effects Using 0/1 Coding System
9. Confound Three Effects with Eight Blocks Using the o/1 Coding System
10. General Blocking and Confounding Scheme for 2k Design in 2p Blocks
11. Complete versus Partial Confounding
12. Reference Blocking and Confounding in 2K Design
8. Fractional Factorial Design of Experiments
1. What is it
2. Primary Basics
3. Design Resolution
4. One-Quarter Fraction Design
5. Alias structure
6. One-Eighth Fraction Design
7. Lowest Runs Design
8. Analysis Example
9. Plackett-Burman Design
10. Reference Fractional Factorial Design of Experiments
9. Applied Regression Analysis
1. What is Regression Analysis
2. Steps in Regression Analysis?
3. Perform Regression Analysis
4. Results Explained Regression Analysis
4.1. Significance Test Regression Analysis
4.2. Practical Test r-square: The Coefficient of Determination
4.3. Functional Relationships Explained
4.4. Diagnostics Regression Analysis
4.4.1. Linearity Assumption Check
4.4.2. Outlier, Leverage, and Influential Points Unusual Observations Check
4.4.3. Residuals Analysis
5. Lack-of-fit Test
6. Practice Problem Regression
7. Reference Regression
10. Response Surface Methodology
1. What is Response Surface Methodology
2. Design Response Surface Methodology
3. Analyze and Explain Response Surface Methodology
4. Box-Behnken Response Surface Methodology
5. Multiple Response Surface Design and Analysis
6. Reference Response Surface Modeling
11. Expected Mean Square EMS Basics to Advanced Design of Experiments
11.1 Are You Performing the Correct ANOVA?
11.2 EMS for All Fixed Factors Design
11.3 EMS for All Random Factors Design
11.4 Approximate or Pseudo F-Statistics/Tests
11.5 EMS for Two Fixed and One Random Factors Design
11.6 EMS for Fixed, Random and Nested Factors Design
11.7 Expected Mean Square Using an Alternative Shortcut Method
11.8 Restricted vs Unrestricted Models, Which is the Best One?
11.9 References for EMS Module
12. Mixed Factors Design of Experiments Nested Repeated Measure Split Plot
12.1. Nested Hierarchical Design
12.2. Repeated Measure Design
12.3. Split-Plot Design
12.4. Are Partially Nested, Repeated Measure and Split-Plot Designs differ
12.5. Reference for Mixed Model Designs
13. Taguchi Robust Parameter Design of Experiments
Econ
Econ Ch2
Cost Classifications
Price Demand Relationship
Revenue Function
Breakeven
Demand is a Function of Price
Demand Is Not a Function of Price
Ergo
Ergonomic Toolbox
Fluid
Fluid Power Lab Demo
Mechanics
Operations
Project
Quality
Statics
Assessment
Assessment of Student Learning Certificate
Program-Level Student Learning Assessment Certificate Training
CV/Resume
The Open Educator
Home
DOE
1. Introduction to Design of Experiments
1. What is Design of Experiment
2. Step 1 of DOE Introduction Hypothesis Research Question
3. Step 2 of DOE Method
4. Step 3 of DOE Results by Analyzing the Data
5. Step 4 of DOE Contextual Conclusion
6. Reference for Module 1 Intro to DOE
2. Hypothesis Testing/ Inferential Statistics/ Analysis of Variance ANOVA
0. All Data Module 2 Hypothesis Testing
1. What is Hypothesis Testing
2. Single Population Testing
3. Single Sample Z-Test
4. Single Sample T-Test
5. Population Proportion Test Single Sample
6. Comparing Two Populations Hypothesis Testing
7. Two Sample Z-Test
8. Two Sample T-Test Equal Variance
9. Two Sample T-Test Unequal Variance
10. Paired T-Test (Matched Pair/Repeated Measure)
11. Two Sample Population Proportion Test
3. One Way/Single Factor ANOVA
0. All Data Module 3 CRD Single One-Way ANOVA
1. What is One Way/Single Factor ANOVA
2. Fixed Effect Model Analysis Basics for One-Way ANOVA
3. Example One-Way/Single-Factor Fixed Effect Completely Randomized Design
4. Diagnostic, Adequacy & Data Quality Check Fixed Effect One Way ANOVA
5. Random Effect Model Analysis Bacis for One-Way ANOVA
6. Example Problem Random Effect Model
7. Diagnostic, Adequacy, & Data Quality Check Random Effect One Way ANOVA
8. Reference
4. Randomized Complete Block, Latin Square, and Graeco-Latin Design
0. All Data Module 4 RCBD Graeco Latin Square Design
1. What is Randomized Complete Block Design (RCBD)?
2. Randomized Complete Block Design Example Problem
3. Randomized Complete Block Design (RCBD) vs Completely Randomized Design
4. Why Randomized Complete Block Design is so Popular?
5. Latin Square Design of Experiments
6. Latin Square Example Problem
7. Graeco-Latin Square Design of Experiments
8. Graeco-Latin Square Example Problem
9. Reference
5. Factorial Design of Experiments
0. All Data Factorial Design of Experiment
1. What is a Factorial Design of Experiment?
2. Understanding Main Effects?
3. Understanding Interaction Effects?
4. How to Develop the Regression Equation from Effects?
5. How to Fit a Response Surface?
6. How to Construct the ANOVA Table from Effects?
7. Practice Problem
6. 2K Factorial Design of Experiments
1. What is 2K Design
2. Layout/Graphical Representation 22 Design
3. Understanding Factor Effects
4. Contrast, Effect, Estimate, Sum of Square, and ANOVA Table 22
5. Practice Problem 22
6. How to Design 2k Experiment
7. Develop Treatment Combinations 2K Design
8. Develop Generic Formulas 2K Design
9. Manual Analysis Using MS Excel 2K Experiments
10. MS Excel, Minitab, SPSS, and SAS
11. Practice Problem 2k
12. 2K Factorial Design of Experiments References
7. Blocking and Confounding in 2K Design
1. What is Blocking
2. What is Confounding
3. Confound an Effect Using -1/+1 Coding System
4. How to Replicate
5. Confound Two Effects Using -1/+1 Coding System
6. Confound Three Effects Using -1/+1 Coding System
7. Confounding and Blocking Using Linear Combination Method 0/1 Coding
8. Confound Two Effects Using 0/1 Coding System
9. Confound Three Effects with Eight Blocks Using the o/1 Coding System
10. General Blocking and Confounding Scheme for 2k Design in 2p Blocks
11. Complete versus Partial Confounding
12. Reference Blocking and Confounding in 2K Design
8. Fractional Factorial Design of Experiments
1. What is it
2. Primary Basics
3. Design Resolution
4. One-Quarter Fraction Design
5. Alias structure
6. One-Eighth Fraction Design
7. Lowest Runs Design
8. Analysis Example
9. Plackett-Burman Design
10. Reference Fractional Factorial Design of Experiments
9. Applied Regression Analysis
1. What is Regression Analysis
2. Steps in Regression Analysis?
3. Perform Regression Analysis
4. Results Explained Regression Analysis
4.1. Significance Test Regression Analysis
4.2. Practical Test r-square: The Coefficient of Determination
4.3. Functional Relationships Explained
4.4. Diagnostics Regression Analysis
4.4.1. Linearity Assumption Check
4.4.2. Outlier, Leverage, and Influential Points Unusual Observations Check
4.4.3. Residuals Analysis
5. Lack-of-fit Test
6. Practice Problem Regression
7. Reference Regression
10. Response Surface Methodology
1. What is Response Surface Methodology
2. Design Response Surface Methodology
3. Analyze and Explain Response Surface Methodology
4. Box-Behnken Response Surface Methodology
5. Multiple Response Surface Design and Analysis
6. Reference Response Surface Modeling
11. Expected Mean Square EMS Basics to Advanced Design of Experiments
11.1 Are You Performing the Correct ANOVA?
11.2 EMS for All Fixed Factors Design
11.3 EMS for All Random Factors Design
11.4 Approximate or Pseudo F-Statistics/Tests
11.5 EMS for Two Fixed and One Random Factors Design
11.6 EMS for Fixed, Random and Nested Factors Design
11.7 Expected Mean Square Using an Alternative Shortcut Method
11.8 Restricted vs Unrestricted Models, Which is the Best One?
11.9 References for EMS Module
12. Mixed Factors Design of Experiments Nested Repeated Measure Split Plot
12.1. Nested Hierarchical Design
12.2. Repeated Measure Design
12.3. Split-Plot Design
12.4. Are Partially Nested, Repeated Measure and Split-Plot Designs differ
12.5. Reference for Mixed Model Designs
13. Taguchi Robust Parameter Design of Experiments
Econ
Econ Ch2
Cost Classifications
Price Demand Relationship
Revenue Function
Breakeven
Demand is a Function of Price
Demand Is Not a Function of Price
Ergo
Ergonomic Toolbox
Fluid
Fluid Power Lab Demo
Mechanics
Operations
Project
Quality
Statics
Assessment
Assessment of Student Learning Certificate
Program-Level Student Learning Assessment Certificate Training
CV/Resume
More
Home
DOE
1. Introduction to Design of Experiments
1. What is Design of Experiment
2. Step 1 of DOE Introduction Hypothesis Research Question
3. Step 2 of DOE Method
4. Step 3 of DOE Results by Analyzing the Data
5. Step 4 of DOE Contextual Conclusion
6. Reference for Module 1 Intro to DOE
2. Hypothesis Testing/ Inferential Statistics/ Analysis of Variance ANOVA
0. All Data Module 2 Hypothesis Testing
1. What is Hypothesis Testing
2. Single Population Testing
3. Single Sample Z-Test
4. Single Sample T-Test
5. Population Proportion Test Single Sample
6. Comparing Two Populations Hypothesis Testing
7. Two Sample Z-Test
8. Two Sample T-Test Equal Variance
9. Two Sample T-Test Unequal Variance
10. Paired T-Test (Matched Pair/Repeated Measure)
11. Two Sample Population Proportion Test
3. One Way/Single Factor ANOVA
0. All Data Module 3 CRD Single One-Way ANOVA
1. What is One Way/Single Factor ANOVA
2. Fixed Effect Model Analysis Basics for One-Way ANOVA
3. Example One-Way/Single-Factor Fixed Effect Completely Randomized Design
4. Diagnostic, Adequacy & Data Quality Check Fixed Effect One Way ANOVA
5. Random Effect Model Analysis Bacis for One-Way ANOVA
6. Example Problem Random Effect Model
7. Diagnostic, Adequacy, & Data Quality Check Random Effect One Way ANOVA
8. Reference
4. Randomized Complete Block, Latin Square, and Graeco-Latin Design
0. All Data Module 4 RCBD Graeco Latin Square Design
1. What is Randomized Complete Block Design (RCBD)?
2. Randomized Complete Block Design Example Problem
3. Randomized Complete Block Design (RCBD) vs Completely Randomized Design
4. Why Randomized Complete Block Design is so Popular?
5. Latin Square Design of Experiments
6. Latin Square Example Problem
7. Graeco-Latin Square Design of Experiments
8. Graeco-Latin Square Example Problem
9. Reference
5. Factorial Design of Experiments
0. All Data Factorial Design of Experiment
1. What is a Factorial Design of Experiment?
2. Understanding Main Effects?
3. Understanding Interaction Effects?
4. How to Develop the Regression Equation from Effects?
5. How to Fit a Response Surface?
6. How to Construct the ANOVA Table from Effects?
7. Practice Problem
6. 2K Factorial Design of Experiments
1. What is 2K Design
2. Layout/Graphical Representation 22 Design
3. Understanding Factor Effects
4. Contrast, Effect, Estimate, Sum of Square, and ANOVA Table 22
5. Practice Problem 22
6. How to Design 2k Experiment
7. Develop Treatment Combinations 2K Design
8. Develop Generic Formulas 2K Design
9. Manual Analysis Using MS Excel 2K Experiments
10. MS Excel, Minitab, SPSS, and SAS
11. Practice Problem 2k
12. 2K Factorial Design of Experiments References
7. Blocking and Confounding in 2K Design
1. What is Blocking
2. What is Confounding
3. Confound an Effect Using -1/+1 Coding System
4. How to Replicate
5. Confound Two Effects Using -1/+1 Coding System
6. Confound Three Effects Using -1/+1 Coding System
7. Confounding and Blocking Using Linear Combination Method 0/1 Coding
8. Confound Two Effects Using 0/1 Coding System
9. Confound Three Effects with Eight Blocks Using the o/1 Coding System
10. General Blocking and Confounding Scheme for 2k Design in 2p Blocks
11. Complete versus Partial Confounding
12. Reference Blocking and Confounding in 2K Design
8. Fractional Factorial Design of Experiments
1. What is it
2. Primary Basics
3. Design Resolution
4. One-Quarter Fraction Design
5. Alias structure
6. One-Eighth Fraction Design
7. Lowest Runs Design
8. Analysis Example
9. Plackett-Burman Design
10. Reference Fractional Factorial Design of Experiments
9. Applied Regression Analysis
1. What is Regression Analysis
2. Steps in Regression Analysis?
3. Perform Regression Analysis
4. Results Explained Regression Analysis
4.1. Significance Test Regression Analysis
4.2. Practical Test r-square: The Coefficient of Determination
4.3. Functional Relationships Explained
4.4. Diagnostics Regression Analysis
4.4.1. Linearity Assumption Check
4.4.2. Outlier, Leverage, and Influential Points Unusual Observations Check
4.4.3. Residuals Analysis
5. Lack-of-fit Test
6. Practice Problem Regression
7. Reference Regression
10. Response Surface Methodology
1. What is Response Surface Methodology
2. Design Response Surface Methodology
3. Analyze and Explain Response Surface Methodology
4. Box-Behnken Response Surface Methodology
5. Multiple Response Surface Design and Analysis
6. Reference Response Surface Modeling
11. Expected Mean Square EMS Basics to Advanced Design of Experiments
11.1 Are You Performing the Correct ANOVA?
11.2 EMS for All Fixed Factors Design
11.3 EMS for All Random Factors Design
11.4 Approximate or Pseudo F-Statistics/Tests
11.5 EMS for Two Fixed and One Random Factors Design
11.6 EMS for Fixed, Random and Nested Factors Design
11.7 Expected Mean Square Using an Alternative Shortcut Method
11.8 Restricted vs Unrestricted Models, Which is the Best One?
11.9 References for EMS Module
12. Mixed Factors Design of Experiments Nested Repeated Measure Split Plot
12.1. Nested Hierarchical Design
12.2. Repeated Measure Design
12.3. Split-Plot Design
12.4. Are Partially Nested, Repeated Measure and Split-Plot Designs differ
12.5. Reference for Mixed Model Designs
13. Taguchi Robust Parameter Design of Experiments
Econ
Econ Ch2
Cost Classifications
Price Demand Relationship
Revenue Function
Breakeven
Demand is a Function of Price
Demand Is Not a Function of Price
Ergo
Ergonomic Toolbox
Fluid
Fluid Power Lab Demo
Mechanics
Operations
Project
Quality
Statics
Assessment
Assessment of Student Learning Certificate
Program-Level Student Learning Assessment Certificate Training
CV/Resume
Demand is a Function of Price
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