Multiattribute acceptance sampling plans - Library(ISI Kolkata ...

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J., 8, 613-631; reprinted in Dodge, H. F. and Romig, H. G. (1959). “Sampling Inspection Tables”, 2nd edn, Wiley, New York. Dodge, H.F. and Romig, H. G. (1941). Single sampling and double sampling inspection tables. Bell Syst. Tech. J., 20, 1-61; reprinted in Dodge, H. F. and Romig, H. G. (1959). “Sampling Inspection Tables”, 2nd edn, Wiley, New York. Dodge, H. F. and Romig, H. G. (1959). “Sampling Inspection Tables”, 2nd edn, Wiley, New York (1st edn, 1944). Gitlow, H. S., Gitlow, S. J. and Oppenheim, J. and Oppenheim, R. (1991). “Tools and Methods for the Improvement of Quality”, Irwin Publishers, Homewood, I L:, Chapter 16. Golub, A. (1953). Designing single-sampling inspection plans when the sample size is fixed. J. Amer. Statist. Assoc., 48, 278-288. Grubbs, F. E. (1949). On designing single sampling inspection plans. Ann. Math. Statist., 20, 242-256. Guthrie, D. and Johns, M. V.(1959). Bayes acceptance sampling procedures for large lots. Ann. Math.Statist., 30, 896-925. Hald, A. (1960). The compound hypergeometric distribution and a system of single sampling inspection plans based on prior distributions and costs. Technometrics, 2, 275-340. Hald, A. (1964). Efficiency of sampling inspection plans for attributes. Bull. Inst. Intern. Statist., 40, 681-697. Hald, A. (1965). Bayesian single sampling attribute plans for discrete prior distributions. Mat. Fys. Skr. Dan. Vid. Selsk., 3, no. 2. Munksgaard, Copenhagen. Hald, A. (1967). Asymptotic properties of Bayesian single sampling plans. J. Roy. Statist. Soc. B, 29, 162-173 and Corrigenda, 586. Hald, A. (1968). Bayesian single sampling attribute plans for continuous prior distributions. Technometrics, 10, 667-683. Hald, A. (1977). A note on the determination of attribute sampling plans of given strength. Technometrics, 19, 211-212. Hald, A. (1981). “Statistical theory of Sampling Inspection by Attributes”, ACADEMIC PRESS INC., New York. Hald, A. and Keiding, N. (1969 and 1972). Asymptotic properties of Bayesian decision rules for two terminal decisions and multiple sampling. I and II. J.Roy. Statist. Soc. B. 31, 445-471 and 34, 55-74. Hald, A. and Kousgaard, E. (1966). A table for solving the equation B(c, n, p) = P for c = 0(1)100 and 15 values of P. Mat. Fys. Skr. Dan. Vid. Selsk. , 3, no. 4., Munksgaard, 141
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Multiattribute acceptance sampling
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e) the cost functions are linear an
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Contents 0.1 Purpose of sampling in
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Part 0 : Introduction 0.1 Purpose o
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ectification is defined by setting
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procurement of materials of the US
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average defective from such a proce
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0.4 Scope of the present inquiry 0.
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0.4.6 A generalized acceptance samp
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sembled units the general practice
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with the above purpose in mind. The
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equals to p i in the long run. We a
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m j , r∏ −P C j ′ = g(x j , m
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show that if we increase c 2 keepin
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independence and mutually exclusive
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different sampling schemes in terms
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curves i.e. the OC curve as a funct
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0.5.3 Part3 Bayesian multiattribute
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K(N, n)/(A 1 − R 1 ) = nk ′ s +
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example, a sample of finished garme
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...(1.1.2) If now X i is assumed to
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(i) Poisson as approximation to bin
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1.2 Multiattribute sampling schemes
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We take this case and the case of t
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highest with respect to critical de
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...(1.2.4) Note that, for single at
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...(1.2.7) To compare the relative
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Corollary For ρ 2 /ρ 1 > c 2 /c 1
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increasing in each a i . Note that
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Figure 1.2.1 Absolute value of Slop
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Table 1.2.5: Three attribute A kind
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Table 1.2.5 (contd.): Three attribu
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Table 1.2.5 (contd.) : Three attrib
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Table 1.2.5 (contd.) : Three attrib
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1.3 Multiattribute sampling plans o
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Let M 2 < M 1 . Then G(c 1 , M 1 ρ
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α, β, and ρ. For α = 0.05, β =
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ma β (a 1 , a 2 , ρ) = m ′ ...(
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Table:1.3.2: Construction parameter
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1.3.5 D kind plans of given strengt
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Figure 1.3.1: The sketch of the fun
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2.1 General cost models 2.1.1 Scope
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(j) Interaction of scrappable attri
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use of defective item is additive o
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2.1.5 Approximation under Poisson c
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2.2 The expected cost model for dis
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P (p (j) ) denotes the probability
Page 95 and 96: 250 200 Figure 2.2.1 : Lot Quality
Page 97 and 98: Table 2. 2. 1 Testing goodness of f
Page 99 and 100: 2. 3 Cost of MASSP's of A kind 2.3.
Page 101 and 102: From (2.3.2) we observe that γ 2 1
Page 103 and 104: ( γ / ) 2 γ − ( m′− m e ) /
Page 105 and 106: We now consider the following funct
Page 107 and 108: ….(2.3.12) Here S A denotes the s
Page 109 and 110: ∴There exists an optimal A plan f
Page 111 and 112: Hence, Q(m) = 1 − P (m) has the s
Page 113 and 114: where a 0 and n 0 are the parameter
Page 115 and 116: where, p = (p 1 , p 2 , ..., p r )
Page 117 and 118: situations as above. For the third
Page 119 and 120: 2.5.3 Optimal Bayesian plans in sit
Page 121 and 122: Table 2.5.2 : Optimal multi attribu
Page 123 and 124: Annexure Microsoft Visual Basic pro
Page 125 and 126: If Regret,OMREGRET(c3-1) Tjem Prevo
Page 127 and 128: 3.1 Bayesian single sampling multia
Page 129 and 130: f(p i , ¯p i , s i )dp i = e −v
Page 131 and 132: easonable to assume that the p i
Page 133 and 134: the minimum cost is obtained at a 1
Page 135 and 136: Table 3.1.1 (Contd.) : Results of 1
Page 137 and 138: Table 3.1.2 : Testing goodness of f
Page 139 and 140: Figure 3.1.1 : Scatter plots of num
Page 141 and 142: Figure 3.1.2: Sketch of the cost fu
Page 143 and 144: Annexure Microsoft Visual Basic pro
Page 145: References Ailor, R. B., Schmidt, J
Page 149: Taylor, E. F. (1957). Discovery sam
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