‘Print Output as worksheet Worksheets(”Sheet1”).Cells (i , 1).Value = a1 Worksheets (”Sheet1”).Cells (i, 2).Value = cost i = i + 1 Next a1 End Sub ‘Computation of the value of the gamma Poisson for a given set of parameters using the Excel worksheet function Function gammapoisson (x, npbar, s) theta = s / (s + npbar) Gammanum = Exp(-1) / Application.WorksheetFunction.GammaDist(1, s + x, 1, False) onedeno = Exp(-1) / Application.WorksheetFunction.GammaDist(1, s, 1, False) Twodeno = Exp(-1) / Application.WorksheetFunction.GammaDist(1, x + 1, 1, False) gammapoisson = (Gammanum / (onedeno * Twodeno)) * (theta ŝ) * ((1 - theta) ˆx) End Function 139
References Ailor, R. B., Schmidt, J. W. and Bennett, G. K. (1975). The design of economic <strong>acceptance</strong> <strong>sampling</strong> Plans for a mixture of attributes and variables. AIIE Transactions, 7. Anscombe, F. J. (1951). The cost of inspection. “Statistical Methods in Industrial Production”, Royal Statistical Society, London. Barnard, G. A. (1946). Sequential tests in industrial statistics. J. Roy. Statist. Soc., B, 8, 1- 26. Barnard, G. A. (1954). Sampling inspection and statistical decisions. J. Roy. Statist. Soc. B, 16, 151-165. Bowker, A. H. and Lieberman, G. L.(1955). “Hand Book of Industrial Statistics”, Englewood Cliffs, N. J, Prentince Hill Inc. Bray, D. F., Lyon, D.A and Burr, I. W. (1973). Three class attributes <strong>plans</strong> in <strong>acceptance</strong> <strong>sampling</strong>. Technometrics, 15, 575-585. Case, K. E., Schmidt, J. W. and Bennett, G. K. (1972). Cost-based <strong>acceptance</strong> <strong>sampling</strong>. Industrial Engineering, 4, (11), 26-31. Case, K. E., Schmidt, J.W. and Bennett, G. K. (1975). A discrete economic multi- attribute <strong>acceptance</strong> <strong>sampling</strong>. AIIE Trans., 363-369. Champernowne, D. G. (1953). The economics of sequential <strong>sampling</strong> procedures for defectives. Applied Statist., 2, 118-130. Chiu, W. K.(1974). A new prior distribution for attributes <strong>sampling</strong>. Technometrics, 16, 93-102. Deming, W. E. (1950) . “Some Theory of Sampling”, John Willey and Sons, New York. Dodge, H. F. (1948). Administration of <strong>sampling</strong> inspection plan. Industr. Qual. Contr., 12-19; reprinted in J. Qual. Tech., 9, 131-138 (1977). Dodge, H. F. (1950). Inspect for quality assurance, Industr. Qual. Contr., 7 (1 ). Dodge, H. F. and Romig, H. G. (1929). A method of <strong>sampling</strong> inspection. Bell Syst. Tech. 140
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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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