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

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Table 3.1.1 (Contd.) : Results of 100% QC checks on 86 lots each of size 25500 pieces ( approximate ) of eye drop vials . The table presents the number of different types of defects observed. Day Glass Fibre Impurity Breakage Defective Sealing Leakage Total 61 60 230 61 8 11 88 458 62 67 204 56 9 49 577 962 63 58 302 37 5 68 103 573 64 74 665 104 10 12 85 950 65 54 400 70 10 64 162 760 66 45 193 56 5 69 150 518 67 54 131 62 10 58 164 479 68 93 406 132 11 86 661 1389 69 54 243 82 5 64 82 530 70 54 144 85 11 46 60 400 71 53 176 60 10 61 63 423 72 54 316 75 11 46 69 571 73 72 245 109 11 49 20 506 74 54 189 60 32 30 58 423 75 64 256 109 12 31 59 531 76 79 480 209 16 45 424 1253 77 78 170 70 15 44 219 596 78 87 617 210 16 42 116 1088 79 162 594 122 18 64 452 1412 80 65 312 83 29 77 254 820 81 69 163 79 18 46 205 580 82 59 300 77 12 56 76 580 83 52 361 100 16 79 244 852 84 9 107 78 11 42 73 320 85 66 182 33 9 43 61 394 86 53 125 56 4 36 139 413 131
Table 3.1.2 : Testing goodness of fit of gamma distribution for the observed critical defects in lots containing about 25500 eye drop vials each Number of defects /unit ( p 1 ) Observed Frequency ( O i ) Estimated Frequency ( E i ) ( O i -E i ) 2 / E i 0.034 4 4.5827 0.0704 Totals 86 86 13.811 p 1 = Prob [ 0. 01708 χ , 2 (10) Var ( p ) 1 > 13.811 ] = 0.182 = 8.44821E - 05, s 1 = p . 2 1 / /Var(p ) 1 = 3.4532 132
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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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Page 87 and 88: use of defective item is additive o
Page 89 and 90: 2.1.5 Approximation under Poisson c
Page 91 and 92: 2.2 The expected cost model for dis
Page 93 and 94: 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: Table 3.1.1 (Contd.) : Results of 1
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 and 146: References Ailor, R. B., Schmidt, J
Page 147 and 148: Copenhagen. Hamaker, H. C. (1950).
Page 149: Taylor, E. F. (1957). Discovery sam
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