LEADER 01011nam0-2200313 --450 001 9910345356803321 005 20191112152824.0 010 $a978-88-7588-158-0 100 $a20191112d2016----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $a<>meccanico del marxismo$eintroduzione critica al pensiero di Gianfranco La Grassa$fPior Zygulski$gPostfazione di Augusto Illuminati 210 $aPistoia$cpetite plaisance$d2016 215 $a97 p.$d21 cm 225 1 $aDivergenze$v51 320 $aContiene bibl. (pp.83-91) 610 0 $aLa Grassa, Gianfranco$aPensiero politico e sociale 676 $a335.40092$v21$zita 700 1$aZygulski,$bPiotr$f<1993->$0768974 702 1$aLa Grassa,$bGianfranco 702 1$aIlluminati,$bAugusto 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910345356803321 952 $aCOLLEZ. 2583 (51)$b1943/2019$fFSPBC 959 $aFSPBC 996 $aMeccanico del marxismo$91567250 997 $aUNINA LEADER 05491nam 2200697Ia 450 001 9911019582903321 005 20200520144314.0 010 $a9786612307270 010 $a9781282307278 010 $a1282307274 010 $a9780470316467 010 $a0470316462 010 $a9780470317174 010 $a0470317175 035 $a(CKB)1000000000816750 035 $a(EBL)469317 035 $a(OCoLC)714798763 035 $a(SSID)ssj0000334781 035 $a(PQKBManifestationID)11272588 035 $a(PQKBTitleCode)TC0000334781 035 $a(PQKBWorkID)10279494 035 $a(PQKB)10274573 035 $a(MiAaPQ)EBC469317 035 $a(Perlego)2752135 035 $a(EXLCZ)991000000000816750 100 $a19880223d1976 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplications of statistics to industrial experimentation /$fCuthbert Daniel 210 $aNew York $cWiley$dc1976 215 $a1 online resource (321 p.) 225 1 $aWiley Series in Probability and Statistics ;$vv.27 300 $aDescription based upon print version of record. 311 08$a9780471194699 311 08$a0471194697 320 $aIncludes bibliography and indexes. 327 $aAPPLICATIONS OF STATISTICS TO INDUSTRIAL EXPERIMENTATION; Preface; Acknowledgments; Contents; Chapter 1 Introduction; 1.1 The range of industrial research; 1.2 Scientific methods; 1.3 Making each piece of data work twice; 1.4 First stages in planning industrial experiments; 1.5 Statistical background required; 1.6 Doing the arithmetic; 1.7 Sequences of experiments; 1.8 The future of "industrial" designs; Chapter 2 Simple Comparison Experiments; 2.1 An example; 2.2 The effect of a Factor?; Chapter 3 Two Factors, Each at Two Levels; 3.1. Introduction; 3.2 Factorial representations 327 $a3.3 Yates's algorithm for effects in the 223.4 Interpretation of a factorial experiment when interactions are present; 3.5 Intermediate summary; 3.6 The replicated22; 3.6.1 General remarks on replication; 3.6.2 Limitations of randomization; 3.6.3 When is randomization useful?; 3.6.4 An example; 3.7 Summary; Appendix 3.A The analysis of variance identities; Chapter 4 Two Factors, Each at Three Levels; 4.1 Introduction; 4.2 Both factors have numerically scaled levels,; 4.3 Standard computations in a 32; 4.4 One-cell interaction; 4.5 Simpler interpretation of ALBQ, AQBL and AQBQ 327 $a4.6 Tukey's test for multiplicative nonadditivity4.7 An eyeball test for interaction; 4.8 What is the answer? (What is the question?); 4.9 An unreplicated 32 on air-pollution data; 4.10 The 32 with both factors discontinuous; 4.11 The 32 with one factor continuous, one discrete-leveled; 4.12 Summary; Appendix 4.A Critical values of the maximum normed residual (MNR); Chapter 5 Unrepticated Three-Factor, Two-Level Experiments; 5.1 When to use the 23; 5.2 A real 23; 5.3 Yates's table of signs; 5.4 Yates's algorithm for the 23; 5.5 First interpretation of the 23; 5.6 Reverse Yatcs's algorithm 327 $a5.7 Interpretation with one factor discontinuous5.8 Representation when two factors are continuous; 5.9 Contours of standard error of fitted Y; 5.10 A numerical check for Yates's 2P-aIgorithm; 5.11 Interpretation of the 23; 5.12 One bad value in a 23+o; 5.13 Blocking the 23; 5.14 Summary; Appendix 5.A The variance of linear functions of uncorrelated random variables; Chapter 6 Unreplicated Four-Factor, Two-Level Experiments; 6.1 Introduction; 6.2 The first computations; 6.3 Interpretation of the first computations; 6.3.1 The empirical cumulative distribution of the residuals 327 $a6.3.2 The dy versus Y plot6.4 Looking for simple models; 6.5 A note on rounding in Yates's algorithm; 6.6 Snares (and delusions); Appendix 6.A Forty empirical cumulation distributions, independent standard normal deviates; Chapter 7 Three Five-Factor, Two-Level Unreplicated Experiments; 7.1 Introduction; 7.2 Yates's 25 on beans; 7.2.1 Description; 7.2.2 Standard computations; 7.2.3 Residuals in place; 7.2.4 Dropping the factorial representation; 7.2.5 A common result: IAl = IBI = IABl; 7.3 Davies' 25 on penicillin; 7.3.1 Description; 7.3.2 When to log; 7.3.3 A bad value 327 $a7.3.4 Effects of factors on residuals 330 $aOther volumes in the Wiley Series in Probability and Mathematical Statistics, Ralph A. Bradley, J. Stuart Hunter, David G. Kendall, & Geoffrey S. Watson, Advisory Editors Statistical Models in Applied Science Karl V. Bury Of direct interest to engineers and applied scientists, this book presents general principles of statistics and specific distribution methods and models. Prominent distribution properties and methods that are useful over a wide range of applications are covered in detail. The strengths and weaknesses of the distributional models are fully described, giving the reader a firm, 410 0$aWiley Series in Probability and Statistics 606 $aExperimental design 606 $aResearch, Industrial$xStatistical methods 615 0$aExperimental design. 615 0$aResearch, Industrial$xStatistical methods. 676 $a607 676 $a607.2 700 $aDaniel$b Cuthbert$013796 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019582903321 996 $aApplications of Statistics to Industrial Experimentation$9119554 997 $aUNINA