Modul kuliah desain eksperimen - Teknik Industri - pertemuan 3 - desain acak sempurna

1. Completely
Randomized Design
ARIF RAHMAN
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2. Experiments
Hypothesis/
Conjecture
Design of
Experiment
Conduct
Experiment
Analysis of
Data
Discussion &
Conclusion
Refine
the Model
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3. Elements of Design of
Experiments
1. Conjecture or hypothesis
2. Response variable
3. Factors, levels and ranges
4. Treatments of factors
5. Blockings
6. Tools and methods for experiments and
measurements
7. Effect models (independent or interaction factors)
8. Replication, randomization and local factor
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4. P Diagram
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Signal
Factors
(m)
Noise
Factors
(x)
Control
Factors
(z)
Scaling
Factors
(r)
Response
Variables
(y)
F(x,m,z,r)
Inputs
Controllable Factors
(x)
Uncontrollable Factors
(z)
Output
(y)
F(x,z)

5. Experiment with a Single Factor
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6. Experiment with a Single Factor
Experiment with a single factor is an
experiment conducted by deliberately
changing a single factor of interest at an
arbitrary a levels of the factor (a treatments).
Simple comparative experiment is a single
factor experiment to compare 2 levels of the
factor (2 treatments).
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7. Experiment with a Single Factor
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8. Empirical Model
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9. Empirical Model
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10. Completely Randomized Design
Completely Randomized Design is a type of
experimental design in which the treatments
or design factors are assigned to the
experimental units in a random manner. In
designed experiments, a completely
randomized design results from running all of
the treatment combinations in random order.
So that the environment in which the
treatments are applied is as uniform as
possible.
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11. Fixed or Random Factor?
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12. Coding Data
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13. Randomization Test
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Arrangemen
Possible
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n
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14. Randomization Test
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15. Conjecture or Hypothesis
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16. Analysis of Variance
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17. Analysis of Variance
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18. Analysis of Variance
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19. Analysis of Variance
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20. Analysis of Variance
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21. Analysis of Variance
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22. Analysis of Variance
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23. Analysis of Variance
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24. Analysis of Variance
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25. Analysis of Variance
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26. Estimation of the Model
Parameters
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27. Estimation of the Model
Parameters
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28. Model Adequacy Checking
Normal Probability Plot of the Residuals
Plot of Residuals in Time Sequence
Plot of Residuals versus Fitted Values
Plot of Residuals versus Other Variables
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29. Model Adequacy Checking
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30. Model Adequacy Checking
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31. Model Adequacy Checking
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32. Model Adequacy Checking
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33. Practical Interpretation of Results
A Regression Model
Comparisons Among Treatment Means
Graphical Comparisons of Means
Contrasts
Orthogonal Contrasts
Scheffé’s Method for Comparing All Contrasts
Comparing Pairs of Treatment Means
Tukey’s Test
Tukey–Kramer procedure
The Fisher Least Significant Difference (LSD) Method
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34. an Example: Etching Process
Experiment
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35. an Example: Etching Process
Experiment
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36. an Example: Etching Process
Experiment
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37. an Example: Etching Process
Experiment
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Power Replication Random
160 1 57102
160 2 29337
160 3 24621
160 4 63548
160 5 40062
180 1 32318
180 2 71834
180 3 84675
180 4 77216
180 5 43289
200 1 49271
200 2 36481
200 3 94037
200 4 89323
200 5 12417
220 1 49813
220 2 21238
220 3 52286
220 4 67710
220 5 18369
Test Sequence Random (Sorted) Power
1 12417 200
2 18369 220
3 21238 220
4 24621 160
5 29337 160
6 32318 180
7 36481 200
8 40062 160
9 43289 180
10 49271 200
11 49813 220
12 52286 220
13 57102 160
14 63548 160
15 67710 220
16 71834 180
17 77216 180
18 84675 180
19 89323 200
20 94037 200

38. an Example: Etching Process
Experiment
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39. an Example: Etching Process
Experiment
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40. an Example: Etching Process
Experiment
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41. an Example: Etching Process
Experiment
41

42. an Example: Etching Process
Experiment
42

43. an Example: Etching Process
Experiment
43

44. an Example: Etching Process
Experiment
44

45. an Example: Etching Process
Experiment
45

46. an Example: Etching Process
Experiment
46

47. an Example: Etching Process
Experiment
47

48. 48
That’s all
... Any Questions ???