West Allis Industries x-chart Control Chart Factor Example 6.1 Factor Size of for Sample Charts (n) Factor for UCL Factor for and LCL for LCL for x-Charts R-Charts (A2) (D3) 2 1.880 0 3.267 3 1.023 0 R = 0.0021 A2 = 0.729 =x = 0.5027 2.575 0.729+ 0.729 (0.0021) 0= 0.5042 in. UCLx4= x= + A2R = 0.5027 2.282 = LCLx 5= x - A2R = 0.5027 0.577– 0.729 (0.0021) =0 0.5012 in. © 2007 Pearson Education 2.115 UCL R(D4)
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West Allis Industries R-chart Control Chart Factors Example 6.1 Factor Size of for Sample Charts (n) Factor for UCL Factor for and LCL for LCL for x-Charts R-Charts (A2) (D3) 2 1.880 0 3.267 3 1.023 0 R = 0.0021 2.575 4 0.729 0 D4 = 2.282 2.282 5 0.577 0 UCLR = D4R = 2.282 (0.0021) = 0.00479 in. D3 = 0 2.115 LCLR = D3R 0.483 0 (0.0021) = 0 in. 0 © 2007 Pearson Education 6 2.004 UCL R(D4)
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Control Charts for Variables Control Chart Factors Factor for UCL Factor for Factor Control - Special Metal Screw UCL for Size of Charts and LCL for LCL for Sample R-Charts R-Charts R = 0.0020 x - Charts x-Charts (n) (A2) x = 0.5025 (D3) (D4) 2 1.880 UCL = x + A x 2R 3 1.023 LCL 4 x = x - A0.729 2R 5 6 7 0.577 0.483 0.419 0 0 0 0 0 0.076 3.267 2.575 2.282 2.115 2.004 1.924
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Control Charts for Variables Control Chart Factors Factor for UCL Factor for Factor Control - Special Metal Screw UCL for Size of Charts and LCL for LCL for Sample R-Charts R-Charts R = 0.0020 D4 = 2.2080 R - Charts x-Charts (n) (A2) (D3) (D4) 2 3 4 5 6 7 1.880 1.023 0.729 0.577 0.483 0.419 0 0 0 0 0 0.076 3.267 2.575 2.282 2.115 2.004 1.924
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Control Charts for Variables Control Charts - Special Metal Screw x - Charts R = 0.0020 x = 0.5025 A2 = 0.729 UCLx = x + A2R LCLx = x - A2R UCLx = 0.5025 + 0.729 (0.0020) = 0.5040 in. LCLx = 0.5025 - 0.729 (0.0020) = 0.5010 in.
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West Allis Industries Completed Control Chart Data Example 6.1 Special Metal Screw Sample Number 1 2 3 4 5 1 0.5014 0.5021 0.5018 0.5008 0.5041 © 2007 Pearson Education Sample 2 3 0.5022 0.5009 0.5041 0.5024 0.5026 0.5035 0.5034 0.5024 0.5056 0.5034 4 0.5027 0.5020 0.5023 0.5015 0.5047 R= R 0.0018 0.0021 0.0017 0.0026 0.0022 0.0021 x= = _ x 0.5018 0.5027 0.5026 0.5020 0.5045 0.5027
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30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 A Required Return 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100 0.110 0.120 0.130 0.140 0.150 0.160 0.170 0.180 0.190 0.200 0.210 0.220 0.230 0.240 B Risk 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.116 0.119 0.123 0.128 0.133 0.139 Decision Models -- Prof. Juran C Return 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.180 0.190 0.200 0.210 0.220 0.230 0.240 D Ford 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.285 0.249 0.224 0.198 0.173 0.148 0.122 E Lilly 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.413 0.430 0.429 0.428 0.426 0.425 0.424 F Kellogg 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.029 0.038 0.046 0.054 0.063 0.071 G Merck 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.039 0.072 0.105 0.138 0.171 H HP 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.302 0.286 0.271 0.256 0.241 0.226 0.211 17
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30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 A Required Return 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100 0.110 0.120 0.130 0.140 0.150 0.160 0.170 0.180 0.190 0.200 0.210 0.220 0.230 0.240 B Risk 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.116 0.119 0.123 0.128 0.133 0.139 C Return 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.179 0.180 0.190 0.200 0.210 0.220 0.230 0.240 Operations Management -- Prof. Juran D Ford 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.285 0.249 0.224 0.198 0.173 0.148 0.122 E Lilly 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.407 0.413 0.430 0.429 0.428 0.426 0.425 0.424 F Kellogg 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.029 0.038 0.046 0.054 0.063 0.071 G Merck 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.039 0.072 0.105 0.138 0.171 H HP 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.302 0.286 0.271 0.256 0.241 0.226 0.211 65 © The McGraw-Hill Companies, Inc.,
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Communication In a parallel implementation of simple search, tasks can execute independently and need communicate only to report solutions. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 174 174 CS 340 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 200 200 The parallel algorithm for this problem will also need to keep track of the bounding value (i.e., the smallest chip area found so far), which must be accessed by every task. One possibility would be to encapsulate the bounding value maintenance in a single centralized task with which the other tasks will communicate. This approach is inherently unscalable, since the processor handling the centralized task can only service requests from the other tasks at a particular rate, thus bounding the number of tasks that can execute concurrently. Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 Page 6
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Output of Multiconfiguration calculations in Molpro ************************************************************************ ********************************************************** 1PROGRAM * MULTI (Direct Multiconfiguration SCF) J. Werner (1984) S.T. Elbert (1988) Number of closed-shell orbitals: 2 ( 2 0 ) Number of active orbitals: 12 ( 9 3 ) Number of external orbitals: 36 ( 24 12 ) Authors: P.J. Knowles, H.- CSFs’ space State symmetry 1 Number of electrons: 14 Spin symmetry=Singlet Space symmetry=1 Number of states: 1 Number of CSFs: 85590 (314280 determinants, 627264 intermediate states) Molecular orbitals read from record Wavefunction dump at record 2100.2 Type=RHF/CANONICAL (state 1.1) 2140.2 Convergence thresholds 0.10E-01 (gradient) 0.10E-05 (energy) 0.10E-02 (step length) Number of orbital rotations: 318 ( 18 Core/Active 48 Core/Virtual 0 Active/Active 252 Active/Virtual) Total number of variables: 314598 ************************************************************************ ********************************************************** MULTI HF-SCF -115.17393693 -115.04914114 ************************************************************************ ********************************************************** Var iable memory released ITER. MIC NCI NEG ENERGY(VAR) ENERGY(PROJ) ENERGY CHANGE GRAD(0) GRAD(ORB) GRAD(CI) STEP TIME 1 80 26 0 -115.06974944 -115.17359664 0.00027746 0.00968625 0.16D+01 77.47 2 48 32 0 -115.17285292 -115.17392304 0.00000005 0.01474826 0.21D+00 184.75 3 47 32 0 -115.17393623 -115.17385996 0.00000003 0.01216470 0.38D-01 292.88 4 52 33 0 -115.17387470 -115.17397740 0.00000003 0.01369836 0.73D-01 402.97 5 44 31 0 -115.17399741 -115.17391540 0.00000004 0.01151657 0.38D-01 508.48 6 45 32 0 -115.17393129 -115.17386437 0.00000003 0.01211887 0.32D-01 616.27 7 47 33 0 -115.17387905 -115.17395726 0.00000007 0.01322541 0.58D-01 725.22 -0.10384720 0.03599697 -0.00107012 0.01955790 0.00007627 0.00148619 -0.00010270 0.00131110 0.00008201 0.00152852 0.00006693 0.00134753 -0.00007821 0.00130802 End of file Where the input file is Meth.int and the output is Meth.out
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Partitioning There is no obvious data structure that could be used to perform a decomposition of this problem’s domain into components that could be mapped to separate processors. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 200 200 Chip Chip Size: Size: 174 174 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 A fine-grained functional decomposition is therefore needed, where the exploration of each search tree node is handled by a separate task. CS 340 This means that new tasks will be created in a wavefront as the search progresses down the search tree, which will be explored in a breadthfirst fashion. Notice that only tasks on the wavefront will be able to execute concurrently. Page 5
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Normalisierung – 1.NF SS 2017– IBB4C Datenmanagement Fr 15:15 – 16:45 R 0.009 Filialen_ID 001 023 072 023 072 001 072 072 Filiale Stadt Region Artikel Marke Farben Filialen_ID Artikel Farben Filialen_ID Stadt Region Artikel Marke Farben in OEZ München Filiale Süd Trikot Deutschland Adidas weiß, rot, schwarz in PEP München Süd Trikot Italien Puma blau, weiß 001 Trikot Deutschland weiß, rot, schwarz Fußgängerzone München inSüd Trikot Frankreich München Adidas blau 001 OEZ Süd Trikot Deutschland Adidas weiß, rot, schwarz in PEP München Süd Trikot Deutschland Adidas weiß, rot, schwarz 023 Trikot Italien blau, Fußgängerzone München Süd Trikot Italien Puma blau, weiß 023in OEZ München inSüdPEP München Süd weiß Trikot Italien Puma blau, weiß Trikot Frankreich Adidas blau, weiß 072 Fußgängerzone München Trikot Süd TrikotFrankreich Deutschland Adidas weiß, rot, schwarz blau 072 Fußgängerzone München Süd Trikot Frankreich Filialen_IDAdidasArtikel blau Farbe Fußgängerzone München Süd Italien Puma blau 023 Trikot Trikot Deutschland weiß, rot, schwarz 023 in PEP München Süd Trikot Deutschland Adidas weiß, rot, schwarz 001 Trikot Deutschland weiß 072 Trikot Italien blau, weiß 072 Fußgängerzone München blau, Süd weiß Trikot Italien 001 Puma Trikot Deutschland blau, weiß rot 001 Trikot Frankreich 001 Trikot Deutschland 001 in OEZDeutschland München Trikot Frankreich Adidas blau, weiß schwarz 072 Trikot weiß,Süd rot, schwarz 023 Trikot Italien 072 Fußgängerzone München Süd 072 Trikot Italien blau Trikot Deutschland Adidas weiß, rot, schwarzblau 023 Trikot Italienblau weiß 072 Fußgängerzone München Süd Trikot Italien Puma 072 Trikot Frankreich blau 023 Trikot Deutschland weiß 023 Trikot Deutschland rot 023 Trikot Deutschland schwarz 072 Trikot Italien weiß 072 Trikot Italien blau 001 Trikot Frankreich weiß 001 Trikot Frankreich blau 072 Trikot Deutschland weiß 072 Trikot Deutschland rot 072 Trikot Deutschland schwarz 072 Trikot Italien blau © Bojan Milijaš, 28.04.2017 Normalisierung - Denormalisierung 3
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Normalisierung – 1.NF SS 2010 – IBB4C Datenmanagement Fr 15:15 – 16:45 R 1.007 Filialen_ID 001 023 072 023 072 001 072 072 Filiale Stadt Region Artikel Marke Farben Filialen_ID Artikel Farben Filialen_ID Stadt Region Artikel Marke Farben in OEZ München Filiale Süd Trikot Deutschland Adidas weiß, rot, schwarz in PEP München Süd Trikot Italien Puma blau, weiß 001 Trikot Deutschland weiß, rot, schwarz Fußgängerzone München inSüd Trikot Frankreich München Adidas blau 001 OEZ Süd Trikot Deutschland Adidas weiß, rot, schwarz in PEP München Süd Trikot Deutschland Adidas weiß, rot, schwarz 023 Trikot Italien blau, Fußgängerzone München Süd Trikot Italien Puma blau, weiß 023in OEZ München inSüdPEP München Süd weiß Trikot Italien Puma blau, weiß Trikot Frankreich Adidas blau, weiß 072 Fußgängerzone München Trikot Süd TrikotFrankreich Deutschland Adidas weiß, rot, schwarz blau 072 Fußgängerzone München Süd Trikot Frankreich Filialen_IDAdidasArtikel blau Farbe Fußgängerzone München Süd Italien Puma blau 023 Trikot Trikot Deutschland weiß, rot, schwarz 023 in PEP München Süd Trikot Deutschland Adidas weiß, rot, schwarz 001 Trikot Deutschland weiß 072 Trikot Italien blau, weiß 072 Fußgängerzone München blau, Süd weiß Trikot Italien 001 Puma Trikot Deutschland blau, weiß rot 001 Trikot Frankreich 001 Trikot Deutschland 001 in OEZDeutschland München Trikot Frankreich Adidas blau, weiß schwarz 072 Trikot weiß,Süd rot, schwarz 023 Trikot Italien 072 Fußgängerzone München Süd 072 Trikot Italien blau Trikot Deutschland Adidas weiß, rot, schwarzblau 023 Trikot Italienblau weiß 072 Fußgängerzone München Süd Trikot Italien Puma 072 Trikot Frankreich blau 023 Trikot Deutschland weiß 023 Trikot Deutschland rot 023 Trikot Deutschland schwarz 072 Trikot Italien weiß 072 Trikot Italien blau 001 Trikot Frankreich weiß 001 Trikot Frankreich blau 072 Trikot Deutschland weiß 072 Trikot Deutschland rot 072 Trikot Deutschland schwarz 072 Trikot Italien blau © Bojan Milijaš, 03/17/19 Normalisierung - Denormalisierung 3
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West Allis Industries Control Chart Development Example 6.1 0.5027 – 0.5009 = 0.0018 Special Metal Screw Sample Number 1 2 3 4 5 1 0.5014 0.5021 0.5018 0.5008 0.5041 © 2007 Pearson Education Sample 2 3 0.5022 0.5009 0.5041 0.5024 0.5026 0.5035 0.5034 0.5024 0.5056 0.5034 4 0.5027 0.5020 0.5023 0.5015 0.5039 R 0.0018 _ x 0.5018 (0.5014 + 0.5022 + 0.5009 + 0.5027)/4 = 0.5018
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Control Charts for Variables Control Charts - Special Metal Screw x - Charts R = 0.0020 x = 0.5025 A2 = 0.729 UCLx = x + A2R LCLx = x - A2R UCLx = 0.5025 + 0.729 (0.0020) = 0.5040 in.
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Why do we need 2 charts? Consistent, but the average is in the wrong place UCL UCL LCL LCL X-Bar Chart R Chart The average works out ok, but way too much variability between points UCL UCL LCL LCL X-Bar Chart R Chart
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501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 114 114 114 114 114 114 114 114 115 115 115 115 115 115 115 115 115 116 116 116 116 116 116 116 116 116 116 117 117 117 117 117 117 117 117 117 117 117 117 117 118 118 118 118 118 118 118 118 118 118 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 120 120 120 120 120 120 120 120 121 121 121 121 121 121 121 121 121 121 121 122 122 122 122 122 122 122 122 123 123 123 123 123 123 123 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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OTHER CHARTS AVAILABLE FROM GOOGLE CHARTS Traditional Graphs Diagrams Area Charts (Traditional and Stepped) Bubble Charts Bar Charts Box and Whisker Plots (Candlestick Charts) Column Charts Calendar Charts Combo Charts Gauge Charts Histograms Geographic Charts Intervals Organizational Charts Line Charts Tables Pie Charts Timelines Scatter Charts Tree Map Charts Time Series (Annotated) Word Trees Trend lines **User created community charts are also available**
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Sunny Dale Bank Example 6.2 Sunny Dale Bank management determined the mean time to process a customer is 5 minutes, with a standard deviation of 1.5 minutes. Management wants to monitor mean time to process a customer by periodically using a sample size of six customers. Design an x-chart that has a type I error of 5 percent. That is, set the control limits so that there is a 2.5 percent chance a sample result will fall below the LCL and a 2.5 percent chance that a sample result will fall above the UCL. Sunny Dale Bank x= = 5.0 minutes  = 1.5 minutes n = 6 customers z = 1.96 Control Limits = + z UCLx = x x UCLx = 5.0 + 1.96(1.5)/ 6 = 6.20 min = – z LCL = x x x x = /n LCLx = 5.0 – 1.96(1.5)/ 6 = 3.80 min After several weeks of sampling, two successive samples came in at 3.70 and 3.68 minutes, respectively. Is the customer service process in statistical control? © 2007 Pearson Education
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Control Limits The control limits for the x-chart are: UCL–x = =x + A2R and LCLx–= x=- A2R Where = X = central line of the chart, which can be either the average of past sample means or a target value set for the process. A2 = constant to provide three-sigma limits for the sample mean. The control limits for the R-chart are UCLR = D4R and LCLR = D3R where R = average of several past R values and the central line of the chart. D3,D4 = constants that provide 3 standard deviations (three-sigma) limits for a given sample size. © 2007 Pearson Education
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Samples for the Proteomics Experiment Tissue 1 2 3 4 Treatment Group 1. Before Senescence A 2. After Senescence B 1. Before Senescence C 2. After Senescence D 1. Before Senescence E 2. After Senescence F 1. Before Senescence G 2. After Senescence H Switchgrass Clone # 5 (Early Senescence) Switchgrass Clone # 4 (Late Senescence) Prairie Cordgrass-ND (Early Senescence) Prairie Cordgrass-SD (Late Senescence) Sample# Sample# 1 Sample# 2 Sample# 3 Sample# 4 Sample# 5 Sample# 6 Sample# 7 Sample# 8 Sample# 9 Sample# 10 Sample# 11 Sample# 12 Sample# 13 Sample# 14 Sample# 15 Sample# 16 Sample# 17 Sample# 18 Sample# 19 Sample# 20 Sample# 21 Sample# 22 Sample# 23 Sample# 24
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NEED-TO-KNOW 10-4 (A) Beginning Balance Period End 12/31/2017 12/31/2018 12/31/2019 12/31/2020 $1,000 768 524 268 (B) Debit Interest Expense [5% x (A)] $50 38 26 14* (C) Debit Notes Payable [(D) - (B)] $232 244 256 268 General Journal 01/01/2017 12/31/2017 12/31/2018 12/31/2019 12/31/2020 Cash Notes payable (D) Credit Cash (E) Ending Balance [(A) - (C)] $282 282 282 282 $768 524 268 0 Debit 1,000 Credit 1,000 Interest expense Notes payable Cash ($1,000 x .05) Interest expense Notes payable Cash ($768 x .05) Interest expense Notes payable Cash ($524 x .05) Interest expense Notes payable Cash (* Rounded) 50 232 282 38 244 282 26 256 282 Learning Objective P5: Record the retirement of bonds. Learning Objective C1: Explain the types of notes and prepare entries to account for notes. 14 268 282 © McGraw-Hill Education 50
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