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統計學 ( 二 ) 朝陽科技大學工業工程與管理系副教授洪弘祈89.02.28. Statistics II2 企業與統計之關係 n 品質管制 n 預測統計與市場調查 n 績效與人事管理 n 例行報告之方案評估與決策參考 n 製程改善 n 研發能力之提昇 n 產品可靠度 n 生產管制.

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Presentation on theme: "統計學 ( 二 ) 朝陽科技大學工業工程與管理系副教授洪弘祈89.02.28. Statistics II2 企業與統計之關係 n 品質管制 n 預測統計與市場調查 n 績效與人事管理 n 例行報告之方案評估與決策參考 n 製程改善 n 研發能力之提昇 n 產品可靠度 n 生產管制."— Presentation transcript:

1 統計學 ( 二 ) 朝陽科技大學工業工程與管理系副教授洪弘祈89.02.28

2 Statistics II2 企業與統計之關係 n 品質管制 n 預測統計與市場調查 n 績效與人事管理 n 例行報告之方案評估與決策參考 n 製程改善 n 研發能力之提昇 n 產品可靠度 n 生產管制

3 Statistics II3 分段主題 n 統計之基礎理論與觀念 n 基礎統計在品管與製程上之應用 ( 含可靠度 ) n 進階統計之一 ~ 實驗計劃法與田口式品質工程 n 進階統計之二 ~ 反應曲面技術 n 進階統計之三 ~ 迴歸分析 n 進階統計之四 ~ 時間序列 n 進階統計之五 ~ 多變量分析

4 Statistics II4 數學 Vs. 統計 n 在統計上未必為真 n 在統計上未必為假 統計之基礎理論與觀念 數學數學 統計統計 社會 科學

5 Statistics II5 THE ENGINEERING METHOD AND STATISTICAL THINKING Engineering or S cientific Method Engineering or S cientific Method Develop a clear and concise description of the problem. Develop a clear and concise description of the problem. Identify, at least tentatively, the important factors that affect this problem or that may play a role in its solution. Identify, at least tentatively, the important factors that affect this problem or that may play a role in its solution. Propose a model for the problem, using scientific or engineering knowledge of the phenomenon being studied. State any limitations or assumptions of the model. Propose a model for the problem, using scientific or engineering knowledge of the phenomenon being studied. State any limitations or assumptions of the model. Conduct appropriate experiments and collect data to test or validate the tentative model or conclusions made in steps 2 and 3. Conduct appropriate experiments and collect data to test or validate the tentative model or conclusions made in steps 2 and 3.

6 Statistics II6 THE ENGINEERING METHOD AND STATISTICAL THINKING Refine the model on the basis of the observed data. Refine the model on the basis of the observed data. Manipulate the model to assist in developing a solution to the problem. Manipulate the model to assist in developing a solution to the problem. Conduct an appropriate experiment to confirm that the proposed solution to the problem is both effective and efficient. Conduct an appropriate experiment to confirm that the proposed solution to the problem is both effective and efficient. Draw conclusions or make recommendations based on the problem solution. Draw conclusions or make recommendations based on the problem solution.

7 Statistics II7 The Engineering or Scientific Method

8 Statistics II8 Statistics n Deals with the collection, presentation, analysis, and the use of data to make decisions, solve problems, and design products and processes. n Describe and understand variability n Apply statistical thinking n Find out the sources of variability

9 Statistics II9 機率 Vs. 統計 統計之基礎理論與觀念 抽樣 推論 樣本 樣本統計量母體參數 母體

10 Statistics II10 統計之專有名詞 n 母體 (Population) n 樣本 (Sample) n 抽樣 (Sampling) n 隨機變數 (Random Variable) n 參數 (Parameter) n 統計量 (Statistics) n Examples 統計之基礎理論與觀念

11 Statistics II11 Dot Diagram n Location and variability

12 Statistics II12 Sample Mean

13 Statistics II13

14 Statistics II14 Some Statistical Terms n Sample and Sampling n Population n Population Mean n Sample Variance n Sample Standard Deviation

15 Statistics II15 Sample Variance and Standard Deviation

16 Statistics II16

17 Statistics II17

18 Statistics II18 Computation of s 2

19 Statistics II19 Some Statistical Terms n Population Variance n Population Standard Deviation n Degrees of Freedom independent terms in the equationindependent terms in the equation unknown population mean (  ), using sample mean insteadunknown population mean (  ), using sample mean instead

20 Statistics II20 Statistical Thinking n Which one has the smaller variation?

21 Statistics II21

22 Statistics II22

23 Statistics II23 Collecting Engineering Data n observational study n designed experiment n hypothesis testing

24 Statistics II24 Mechanistic and Empirical Model n Mechanistic Model: built from our underlying knowledge of the basic physical mechanism. n Empirical Model: adding the nature variability, or built from observed data.

25 Statistics II25

26 Statistics II26

27 Statistics II27

28 Statistics II28 Designing Experimental Investigation n factorial experiment n replicates n interaction n fractional factorial experiment

29 Statistics II29

30 Statistics II30

31 Statistics II31

32 Statistics II32

33 Statistics II33 Observing Processes Over Time ~

34 Statistics II34

35 Statistics II35

36 Statistics II36

37 Statistics II37

38 Statistics II38

39 Statistics II39

40 Statistics II40


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