DUNCAN, A.J., 1986. Quality control and industrial statistics. 5th ed. Chicago: Irwin. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Optimal SPRT and CUSUM Procedures using Compressed Limit Gauges - Geyer, Steiner (Correct)
.... variables based control chart is to use an attribute control chart (Stevens [16] Beattie [1] Elder, Provost and Ecker [5] and Lucas [9] The simplest attribute methods that classify units as conforming or nonconforming are inefficient when the proportion of non conforming units is rare (Duncan [4]) Ladany [7] suggests compensating for the loss in efficiency by using a compressed limit, or a narrow limit gauge when the underlying distribution of the quality characteristic of interest is known. When a compressed limit gauge is used the concept of nonconformity is replaced by one of ....
Duncan, A.J., Quality Control and Industrial Statistics (5th ed.), Homewood, IL: Richard D. Irwin (1986).
Quadratic Assignment Problems and M/G/C/C/ State Dependent.. - Smith, Li (1998) (Correct)
....found in a sample then the lot is accepted. Otherwise, the lot is rejected. The purpose of the sampling plan is simply to accept or reject lots in order to ensure that the output of a process conforms to requirements. The important concept involved in the acceptance sampling plan is Bayes theorem [5]. Because there exists a relationship between the sample and the remainder of the lot, the acceptance sampling plan works well. In this algorithm, the concept of an acceptance sampling plan has been adopted. If Equation 10 is regarded as a lot, then the computation of Equation 10 is 100 ....
Duncan, A.J., "Quality Control and Industrial Statistics," Richard D. Irwin, Homewood, II., 1986.
Course Summary and Bibliography - Preliminaries Reasons For (Correct)
....1979 (SF 6.5 MUR) G B Wetherill, Sampling Inspection and Quality Control, Chapman and Hall 1977 (SF 6.5 WET) W A Shewhart, Statistical Method from the Viewpoint of Quality Control, Graduate School, Department of Agriculture, Washington, D.C. 1939, reprinted New York: Dover 1986 (SF 6. 5 SHE) A J Duncan, Quality Control and Industrial Statistics (5th edn) Homewood, IL: Irwin Dorsey 1986 (SF 6.5 DUN) 13. Time Series. The global and local constant mean models, leading to exponential smoothing. The global and local linear trend models and extensions of exponential smoothing. Autoregressive ....
A J Duncan, Quality Control and Industrial Statistics (5th edn), Homewood, IL: Richard D Irwin 1986 (SF 6.5 DUN).
Mining Association Rules with Weighted Items - Cai (1998) (7 citations) (Correct)
....Control method 84 7.1 Introduction. 84 7.1.1 Motivation of using the qualitycontrol method . 85 vi 7.2 QualityControl Method . 85 7.2.1 Motivation of using Mil. Std. 105D . 86 7.2. 2 Military Standard 105D Procedure [12] . 87 7.3 Mapping the Database Maintenance to the QualityControl . 88 7.3.1 Algorithm for Database Maintenance. 89 7.4 Performance Evaluation . 93 7.5 Discussion . 94 7.6 Summary ....
....Sample size with the corresponding parameters . 81 6.2 The number of large itemsets for synthetic database . 81 6.3 The number of large itemsets for real database . 81 7.1 Results of the Database maintenance . 94 C. 1 Sample Size Code Letter [12] . 106 C.2 Table for normal inspection [12] 107 C.3 Table for tightenedinspection[12] 107 C.4 Table for reduced inspection [12] 108 viii List of Figures 1.1 KDD ....
[Article contains additional citation context not shown here]
Acheson J. Duncan. Quality Control and Industrial Statistics, 5th Edition. Homewood, Ill.: Irwin, 1986.
The Triangular Control Chart for Data Production - Massimiliano Giacalone Sergio (Correct)
.... procedures that solve the industrial production control problem by variables and attributes (Shewhart, 1931, 1939; Lieberman Resknikoff, 1955) The mean and the standard deviation are the most common references in order to evaluate the quality or the fractions of items out of tolerance limits (Duncan, 1965; Montgomery, 1991) The statistical quality assurance uses the sampling results in order to draw conclusions about the quality of a population of product units by means of mathematical statistical methods (Mittag Rinne, 1993) In the last years a few methods of SQC for the industrial ....
....of SQC for the industrial processes, have been applied to the particular area of survey processes (data selection, data validation, data coding, etc. The techniques of the SQC are: the Acceptance Sampling (Dodge Romig, 1959) the Skip Lot Sampling (Mittag Rinne, 1993) the Process Control (Duncan, 1965) and the Acceptance Control (Schilling, 1982) Our attention is devoted to the Process Control (survey in progress) since we consider the interviewers as machines and the questionnaires as products. The aim of the Process Control or the Control Chart method is to stop the process if the character ....
Duncan, D. J. (1965), Quality Control and Industrial Statistics, Richard Irwing Inc. Homewoods, Illinois.
Hunting of the Snark - Finding Data Glitches using Data Mining .. - Dasu, Johnson (1999) (1 citation) (Correct)
....complex due to the intractability of the sampling distributions. The statistics for individual samples are plotted to see if they fall within the control limits. Some famous examples are Shewhart charts, named after W. A. Shewhart who first proposed them in 1938 (X chart, R chart) See [12] and [5] for details. Cumulative Sum charts monitor the accumulated sum of the deviations from an expected value for the samples. It should hover around zero. If there is a trend or pre determined thresholds are crossed, a problem is indicated. The thresholds can be target values or computed from the data ....
....than Shewhart charts. There are many variations of the above charts as well as other charts such as the Operating Characteristics Curve, Average Run Length, p chart and others, designed for different situations including adjusting for trends over time. These are presented in great detail in [5]. 4 Quality Control for Large Data Sets The quality control methods described above are the current standard for quality control of large data sets. The methods are aimed at detecting a process that drifts out of control over time. Typically no action is taken unless there is a run of abnormal ....
Duncan, A. J. (1986). Quality Control and Industrial Statistics. Fifth Edition, Irwin.
Multivariate Autoregressive Analysis in Locating the Origin of.. - Saarela (2002) (Correct)
No context found.
DUNCAN, A.J., 1986. Quality control and industrial statistics. 5th ed. Chicago: Irwin.
Hellenic Complex Systems Laboratory - Nonlinear Componem Of (Correct)
No context found.
Duncan AJ. Tests of normality. Duncan AJ. Quality Control and Industrial Statistics. Homewood, Illinois: Irwin, 1986: 634-45.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC