Wyniki wyszukiwana dla hasla figure 3
00323 ?85e6f46f7f32f30fdf82adebd3e7fe 326 McCarviIle & Montgomery Figurę 10. The Defect Level D
00345 ?76f1a9d5e5520caac089116143c855 A Monitoring Plan for Detecting Product Degradation 349 Figur
00347 ?67ec69a566c66cdfea22c201c0db23 351 A Monitoring Plan for Detecting Product Degradation Figur
00354 ?a6267a2cb3b1478611ee18929094d3 358 Prairie & Zimmer 8ample Ratę Figurę 3. Contours of Co
00355 b1ea3274003d2e7e3c2835ee2fe625 359 A Monitoring Plan for Detecting Product Degratation Sampl
00356 ?268bb4fa217b68c5b6c8bf725af104 360 Prairie & Zimmer Sample Ratę Figurę 7. Contours of Co
00357 h1e459d7700279df3d04ac94736408f A Monitoring Plan for Detecting Product Degradation 361 Perce
00358 zf32813afb540cc5d33f56bd2d2e5e9 362 Prairie & Zimmer Perccnt Figurę 10. Percent Produced
00363 ?1269f5a82d63a83345fed901868e3a Regret Indices and Capability Quantification 367 1 ---O Tj &n
00364 ?1028436a8132748621c0d2ba2ba130 368 Obenchain 1 Figurę 4. Logistic Regret where the K s are a
00369 a2c33237555c8d05d8989d6c335c61b Regret Indices and Capability Quantification 373 Fnequency Fi
00378 65ab0b7d9e95f446f0a726dda2f032 382 Obenchain Figurę 8. CC Curves for Four Fill-Volume Regret
00380 6b4fc5cd24cdfcb1d8dc078dc50655 384Obencbain C    B Figurę 10. Which Process I
00381 ?c79f45a4235fae8108ab837f609112 385 Regret Indices and Capability Quantification Figurę 11. C
00395 /bff9ce23d8e0e8c60ebe0c43cea6c7 Regret Indices and Capability Quantification 399 Figurę 15. C
00414 ?8e21de209c04940824ad6ecd537067 418 Pignatiello & Ramberg -2.0 -1.0 0.0 1.0 2.0 Nor mai S
00418 ?56a13f3cdc41e5c794440043a03715 422 Pignatiello & Ramberg2 N =    1000 Mid
00441 ?ccbd1f830e33e40cc56e45b41d58a3 446 Russell Autoregressive Time Senes AR(1), Coeflicients Neg
00443 e607e0242a894c7128f70fe85aeb91 448 Russell then retums to the "bow-tie" pattem as
00445 ?b35346b0c0a31ef14965c537f7c03e 450 Russell Autoregressive Time Senes AR(1), Coefficients Pos

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