# Exercise 15. No.1  (Worse) Incomplete data is commonly referred to as censored data and often occurs when the response variable is time to failure, e.g.,

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Exercise 15

No.1  (Worse) Incomplete data is commonly referred to as censored data and often occurs when the response variable is time to failure, e.g., accelerated life testing.  (Better 1) Incomplete data, commonly referred to as censored data, often occurs when the response variable is time to failure, e.g., accelerated life testing.

No.1 (cont.)  (Better 2) Commonly referred to as censored data, incomplete data often occurs when the response variable is time to failure, e.g., accelerated life testing.

No.2  (Worse) Their method suggested either using iterative least squares (ILS) to analyze censored data or the initial fit is used to estimate the expected failure time for each censored observation.  (Better) Their method suggested using either iterative least squares (ILS) to analyze censored data or the initial fit to estimate the expected failure time for each censored observation.

No.3  (Worse) The TOPSIS value for each trial and the optimal factor/level combination can be determined in the following steps: Apply equations (4)~(8) to compute the relative closeness of each trial. The TOPSIS value in the ith trial is set to the designated value. The factor effects based on the TOPSIS value are estimated. Determine the optimal control factors and their levels.

No.3 (cont.)  (Better) The TOPSIS value for each trial and the optimal factor/level combination can be determined in the following steps: Apply equations (4)~(8) to compute the relative closeness of each trial. Set the TOPSIS value in the ith trial to the designated value. Estimate the factor effects based on the TOPSIS value. Determine the optimal control factors and their levels.

No.4  (Worse) The system manager is in no case responsible for combining the experimental design techniques with quality loss considerations and careful consideration of how the various factors affect performance variation.  (Better) The system manager is in no case responsible for combining the experimental design techniques with quality loss considerations and carefully considering how the various factors affect performance variation.

[Note 4.19]  Avoid wordiness by saying never instead of in no cases.

No.5  (Worse) Herein, TOPSIS is applied to reduce the computational complexity, satisfy Taguchi ’ s quality ’ s loss, and finding a performance measurement index for each trial.  (Better) Herein, TOPSIS is applied to reduce the computational complexity, satisfy Taguchi ’ s quality ’ s loss, and find a performance measurement index for each trial.

No.6  (Worse) The proposes procedure is employed for transformation of relative importance of each response, to compute the quality loss, determination of the TOPSIS value, to select the optimal factor/lever combination, and analysis of a confirmation experiment.  (Better) The proposes procedure is employed to transform relative importance of each response, compute the quality loss, determine the TOPSIS value, select the optimal factor/lever combination, and analyze a confirmation experiment.

No.7  (Worse) The engineer makes an adjustment of the processing parameters and that the shop floor layout is finalized.  (Better) The engineer adjusts the processing parameters and finalizes the shop floor layout.

No.8  (Worse) The proposed mechanism is adaptive, flexible, efficient, and can be applied in a factory setting.  (Better) The proposed mechanism is adaptive, flexible, efficient, and applicable in a factory setting.

No.9  (Worse) This section not only presents a numerical example, but also the effectiveness of the proposed GA-based procedure for cell formation problems is demonstrated.  (Better) This section not only presents a numerical example, but also demonstrates the effectiveness of the proposed GA-based procedure for cell formation problems.

No.10  (Worse) The censored data contain less information than complete data and analysis is made more difficult to perform.  (Better 1) The censored data contain less information than complete data and make analysis more difficult to perform.  (Better 2) The censored data is less than complete, making analysis difficult to perform.

No.11  (Worse) The proposed model not only performs diagnostic checking, but also the optimal factor/level combination is determined.  (Better) The proposed model not only performs diagnostic checking, but also determines the optimal factor/level combination.

No.12  (Worse) The procedure to determine the optimal factor/level combination in a multi-response problem is described as follows: Step 1: Estimate the factor effects.  A. The factor effects are plotted and the main effects on MRSN are tabulated.  B. Plot the factor efforts and tabulate the main effects on the mean response for nominal-the-best case. Step 2: The optimal control factors and their levels are determined.  A. Find the control factor that significantly affects MRSN.  B. The optimum level for each control factor is determined. Step 3: The optimal adjustment factors are determined.

No.12 (cont.)  (Better) The procedure to determine the optimal factor/level combination in a multi-response problem is described as follows: Step 1: Estimate the factor effects.  A. Plot the factor effects and tabulate the main effects on MRSN.  B. Plot the factor efforts and tabulate the main effects on the mean response for nominal-the-best case. Step 2: Determine the optimal control factors and their levels.  A. Find the control factor that significantly affects MRSN.  B. Determine the optimum level for each control factor is. Step 3: Determine the optimal adjustment factors.

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