1. 胡子陵 立德管理學院資環所副教授 Leader University
實驗設計與統計
胡子陵
立德管理學院資環所副教授
Leader University
design and analysis of experiments

2. design and analysis of experiments
實驗設計與統計課程綱要及進度
Week1：Statistics (Review)
Week2：Simple Comparative Experiments
Week3：Experiments with a Single Factor：The Analysis of Variance(1)
Week4：Experiments with a Single Factor：The Analysis of Variance(2)
Week5：Introduction to Factorial Designs (1)
Week6：Introduction to Factorial Designs (2)
Week7：The 2K Factorial Design(1)
Week8：The 2K Factorial Design(2)
Week9：-----Mid-Term Report
design and analysis of experiments

3. design and analysis of experiments
實驗設計與統計課程綱要及進度
Week10：Blocking and Confounding in the 2k Factorial Design(1)
Week11：Blocking and Confounding in the 2k Factorial Design(2)
Week12：Two-Level Fractional Factorial Design(1)
Week13：Two-Level Fractional Factorial Design(2)
Week14：Fitting Regression Models(1)
Week15：Fitting Regression Models(2)
Week16：Response Surface Methods and Other Approaches to Process Optimization(1)
Week17：Response Surface Methods and Other Approaches to Process Optimization (2)
Week18：----Final Examination
design and analysis of experiments

4. 實驗設計與統計 Part 1 – 前言 Chapter 1
課程目的
歷史回顧
一些基本原理(原則)和術語
實驗的策略
規畫、進行和分析實驗的方針
design and analysis of experiments

5. design and analysis of experiments
實驗設計介紹
An experiment is a test or a series of tests
Experiments are used widely in the engineering world
Process characterization & optimization
Evaluation of material properties
Product design & development
Component & system tolerance determination
“All experiments are designed experiments, some are poorly designed, some are well-designed”
design and analysis of experiments

6. design and analysis of experiments
解釋名詞
Response variable
Explanatory variable
Treatment
Lurking variable
Confounded
Statistical significance：我們的結論有統計顯著性，即證據或結果強到很少會光靠機遇(chance)而發生。
design and analysis of experiments

7. design and analysis of experiments
工程上之實驗要求
Reduce time to design/develop new products & processes
Improve performance of existing processes
Improve reliability and performance of products
Achieve product & process robustness
Evaluation of materials, design alternatives, setting component & system tolerances, etc.
design and analysis of experiments

8. design and analysis of experiments
實驗設計歷史發展的基本原則
Randomization
Running the trials in an experiment in random order
Notion of balancing out effects of “lurking” variables
Replication
Sample size (improving precision of effect estimation, estimation of error or background noise)
Replication versus repeat measurements?
Blocking
Dealing with nuisance factors
design and analysis of experiments

9. The meaning of Blocking
Blocking design：將個體隨機分派到各處理的此一步驟，是在每個Blocking裡個別執行的。
design and analysis of experiments

10. design and analysis of experiments
實驗策略
“Best-guess” experiments
Having a lot of technical or theoretical knowledge
More successful than you might suspect, but there are disadvantages…
One-factor-at-a-time (OFAT) experiments
Sometimes associated with the “scientific” or “engineering” method
Devastated by interaction, also very inefficient
Statistically designed experiments
Based on Fisher’s factorial concept
design and analysis of experiments

11. design and analysis of experiments
因子設計
In a factorial experiment, all possible combinations of factor levels are tested
The golf experiment:
Type of driver
Type of ball
Walking vs. riding
Type of beverage
Time of round
Weather
Type of golf spike
Etc, etc, etc…
design and analysis of experiments

12. One-factor-at-a-time設計
Using the oversized driver, balata ball, walking, and drinking water levels of the four factors as the baseline.
Optimal combination: regular-sized driver, riding, and drinking water
design and analysis of experiments

13. design and analysis of experiments
因子設計
design and analysis of experiments

14. design and analysis of experiments
多因子之因子設計
design and analysis of experiments

15. design and analysis of experiments
多因子之因子設計－部份因子
design and analysis of experiments

16. design and analysis of experiments
實驗設計指南
Recognition of & statement of problem
Choice of factors, levels, and ranges
Selection of the response variable(s)
Choice of design
Conducting the experiment
Statistical analysis
Drawing conclusions, recommendations
design and analysis of experiments

17. design and analysis of experiments
實驗設計歷史發展的四個時期
The agricultural origins, 1918 – 1940s
R. A. Fisher & his co-workers
Profound impact on agricultural science
Factorial designs, ANOVA (analysis of variance)
The first industrial era, 1951 – late 1970s
Box & Wilson, response surfaces
Applications in the chemical & process industries
The second industrial era, late 1970s – 1990
Quality improvement initiatives in many companies
Taguchi and robust parameter design, process robustness
The modern era, beginning circa 1990
design and analysis of experiments

18. design and analysis of experiments
實驗中使用統計技術
Get statistical thinking involved early
Your non-statistical knowledge is crucial to success
Pre-experimental planning (steps 1-3) vital
Think and experiment sequentially (use the KISS principle)
design and analysis of experiments