Monitoring process data with seasonal time series model
碩士 === 國立政治大學 === 統計研究所 === 101 === Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many proces...
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ndltd-TW-101NCCU53370222016-07-02T04:20:16Z http://ndltd.ncl.edu.tw/handle/49031987940557216857 Monitoring process data with seasonal time series model 追蹤季節性時間數列模型之流程資料 王儀茹 碩士 國立政治大學 統計研究所 101 Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many processes. To solve this problem, it is becoming more common to obtain profiles at each time period. Profile monitoring is the use of control charts for cases in which the quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. For the data with seasonal time series model, we propose several monitoring approaches to detect the out-of-control profiles. After considering both Phase I and Phase II schemes, a real example is given to illustrate the results. 楊素芬 鄭宗記 學位論文 ; thesis 64 en_US |
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碩士 === 國立政治大學 === 統計研究所 === 101 === Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many processes. To solve this problem, it is becoming more common to obtain profiles at each time period. Profile monitoring is the use of control charts for cases in which the quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. For the data with seasonal time series model, we propose several monitoring approaches to detect the out-of-control profiles. After considering both Phase I and Phase II schemes, a real example is given to illustrate the results.
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楊素芬 |
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楊素芬 王儀茹 |
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王儀茹 |
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王儀茹 Monitoring process data with seasonal time series model |
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王儀茹 |
title |
Monitoring process data with seasonal time series model |
title_short |
Monitoring process data with seasonal time series model |
title_full |
Monitoring process data with seasonal time series model |
title_fullStr |
Monitoring process data with seasonal time series model |
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Monitoring process data with seasonal time series model |
title_sort |
monitoring process data with seasonal time series model |
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http://ndltd.ncl.edu.tw/handle/49031987940557216857 |
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AT wángyírú monitoringprocessdatawithseasonaltimeseriesmodel AT wángyírú zhuīzōngjìjiéxìngshíjiānshùlièmóxíngzhīliúchéngzīliào |
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1718331400180989952 |