{"id":355164,"date":"2024-10-20T01:06:40","date_gmt":"2024-10-20T01:06:40","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-5703-42003\/"},"modified":"2024-10-26T01:22:35","modified_gmt":"2024-10-26T01:22:35","slug":"bs-5703-42003","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-5703-42003\/","title":{"rendered":"BS 5703-4:2003"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
1<\/td>\n | BRITISH STANDARD <\/td>\n<\/tr>\n | ||||||
2<\/td>\n | Committees responsible for this British Standard <\/td>\n<\/tr>\n | ||||||
3<\/td>\n | Contents <\/td>\n<\/tr>\n | ||||||
7<\/td>\n | Introduction 1 Scope 2 Normative references 3 Terms, definitions, abbreviations and symbols 3.1 General 3.2 Additional terms, definitions, abbreviations and symbols <\/td>\n<\/tr>\n | ||||||
8<\/td>\n | 3.3 Symbols <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | 4 Discrete data: cusum basics 4.1 Data classification 4.2 Classified data <\/td>\n<\/tr>\n | ||||||
11<\/td>\n | Table 1 – Nonconforming spot weld production figures and cusum table Figure 1 – Basic cusum plot of spot weld data <\/td>\n<\/tr>\n | ||||||
12<\/td>\n | Figure 2 – Standard shewhart 4.3 Countable data <\/td>\n<\/tr>\n | ||||||
14<\/td>\n | Table 2 – Printing errors and basic cusum tabulation for magazine issues Figure 3 – Cusum plot of magazine printing errors by issue number <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | Figure 4 – Standard Shewhart 5 Cusum decision schemes for discrete data 5.1 General <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 5.2 Truncated V-mask scheme Figure 5 – Outline features of one-sided V-mask for discrete data <\/td>\n<\/tr>\n | ||||||
19<\/td>\n | 5.3 Decision interval (DI) scheme 5.4 Fast initial response (FIR) cusum 6 Cusum decision schemes for count data 6.1 Step-by-step design approach using the British Standard table method <\/td>\n<\/tr>\n | ||||||
20<\/td>\n | 6.2 Case study of cusum design using the British Standard\ufffdtable method <\/td>\n<\/tr>\n | ||||||
21<\/td>\n | Table 3 – Table for cusum stock-out plot with V-mask <\/td>\n<\/tr>\n | ||||||
22<\/td>\n | Figure 6 – Cusum chart with V-mask for stock-outs ( <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | Table 4 – Cusum DI (decision interval) tabulation: ( <\/td>\n<\/tr>\n | ||||||
24<\/td>\n | 6.3 Step-by-step design approach using a software routine <\/td>\n<\/tr>\n | ||||||
25<\/td>\n | 6.4 Case study of cusum design using the software approach <\/td>\n<\/tr>\n | ||||||
27<\/td>\n | Table 5 – Warp knitting plant data: cusum tabulation <\/td>\n<\/tr>\n | ||||||
28<\/td>\n | Figure 7 – Cusum plot of fabric data with \u201cup\u201d and \u201cdown\u201d truncated V-masks Figure 8 – Decision interval plot of fabric data with upper and lower control limits <\/td>\n<\/tr>\n | ||||||
29<\/td>\n | 7 Cusum decision schemes for classified data 7.1 Design approach using the British Standard table method <\/td>\n<\/tr>\n | ||||||
30<\/td>\n | Figure 9 – Cusum plot of spot weld data with V-mask having Table 6 – Decision interval (DI) tabulation of spot weld data <\/td>\n<\/tr>\n | ||||||
31<\/td>\n | Table 7 – ARL performance for a binomial cusum scheme with a sample size = 50 and target value, 7.2 Design approach using a software routine <\/td>\n<\/tr>\n | ||||||
33<\/td>\n | Table 8 – Audited product cusum <\/td>\n<\/tr>\n | ||||||
34<\/td>\n | Figure 10 – Audited product cusum plot with asymmetrical truncated V-mask for up and down movement <\/td>\n<\/tr>\n | ||||||
35<\/td>\n | Figure 11 – Cusum plot of audited product with upper and lower control limits 8 Design of cusum schemes for low rates of occurrence of events 8.1 Introduction <\/td>\n<\/tr>\n | ||||||
36<\/td>\n | 8.2 Case studies Table 9 – ARLs for 1 % and 4 % proportions in terms of Table 10 – ARL performance of two <\/td>\n<\/tr>\n | ||||||
37<\/td>\n | Table 11 – Cusum schemes in terms of selected ARLs and in- and out-of-control probabilities Table 12 – ARL performance of two <\/td>\n<\/tr>\n | ||||||
41<\/td>\n | Annex A (normative) Tests for validity of Poisson or binomial distribution assumptions A.1 General A.2 Test by comparison of observed and theoretical variances <\/td>\n<\/tr>\n | ||||||
42<\/td>\n | Table A.1 – Critical values of variance ratio for testing binomial or Poisson distribution assumptions <\/td>\n<\/tr>\n | ||||||
43<\/td>\n | A.3 Successive difference test <\/td>\n<\/tr>\n | ||||||
44<\/td>\n | Annex B (normative) Cusum design schemes for count (Poisson) data Table B.1 – C1 and C2 Schemes for count (Poisson) data in terms of <\/td>\n<\/tr>\n | ||||||
45<\/td>\n | Table B.2 – ARL characteristics for cusum schemes in terms of <\/td>\n<\/tr>\n | ||||||
46<\/td>\n | Table B.3 – ARLs for low rate schemes with <\/td>\n<\/tr>\n | ||||||
47<\/td>\n | Table B.4 – ARLs for schemes with <\/td>\n<\/tr>\n | ||||||
48<\/td>\n | Table B.5 – ARL data for schemes with <\/td>\n<\/tr>\n | ||||||
49<\/td>\n | Annex C (informative) Tabular cusum design schemes for binomial data Table C.1 – C1 and C2 schemes for binomial data <\/td>\n<\/tr>\n | ||||||
50<\/td>\n | Table C.2 – ARL data for binomial variables design scheme Table C.2a) – C1 design schemes with <\/td>\n<\/tr>\n | ||||||
51<\/td>\n | Table C.2b) – C2 design schemes with Table C.2c) – C1 design schemes with <\/td>\n<\/tr>\n | ||||||
52<\/td>\n | Table C.2d) – C2 design schemes with Table C.2e) – C1 design schemes with <\/td>\n<\/tr>\n | ||||||
53<\/td>\n | Table C.2f) – C2 design schemes with Table C.2g) – C1 design schemes with <\/td>\n<\/tr>\n | ||||||
54<\/td>\n | Table C.2h) – C2 design schemes with Table C.2i) – C1 design schemes with <\/td>\n<\/tr>\n | ||||||
55<\/td>\n | Table C.2j) – C2 design schemes with Table C.3 – ARLs for low rate schemes with <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Guide to data analysis and quality control using cusum techniques – Cusum methods for discrete (count\/classified) data<\/b><\/p>\n |