LEADER 00978nam a2200277 i 4500 001 991001231449707536 005 20020502185443.0 008 990201s1992 it ||| | ita 020 $a8802046204 035 $ab11482710-39ule_inst 035 $aPRUMB57214$9ExL 040 $aDUSS - Diploma Universitario in Servizio Sociale$bita 082 0 $a340 100 1 $aSacco, Rodolfo$0229107 245 10$aIntroduzione al diritto comparato /$cRodolfo Sacco 250 $a5. ed 260 $aTorino :$bUTET,$cc1992 (stampa 1997) 300 $aXIX, 271 p. ;$c25 cm. 490 0 $aTrattato di diritto comparato 650 4$aDiritto comparato 907 $a.b11482710$b01-03-17$c01-07-02 912 $a991001231449707536 945 $aLE024 DIR PB C IX 15$g1$i2024000013974$lle021$nex DUSS$o-$pE0.00$q-$rl$s- $t0$u4$v5$w4$x0$y.i11673692$z01-07-02 996 $aIntroduzione al diritto comparato$949869 997 $aUNISALENTO 998 $ale021$b01-01-99$cm$da $e-$fita$git $h0$i1 LEADER 05532nam 22006615 450 001 9910254065103321 005 20250411135114.0 010 $a3-319-32768-2 024 7 $a10.1007/978-3-319-32768-6 035 $a(CKB)3710000000765129 035 $a(DE-He213)978-3-319-32768-6 035 $a(MiAaPQ)EBC5588051 035 $a(PPN)194516806 035 $a(EXLCZ)993710000000765129 100 $a20160719d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmpirical Modeling and Data Analysis for Engineers and Applied Scientists /$fby Scott A. Pardo 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XV, 247 p. 101 illus., 61 illus. in color.) 311 08$a3-319-32767-4 327 $aPreface -- Acknowledgments -- 1. Some Probability Concepts -- 2. Some Statistical Concepts -- 3. Measurement Systems Analysis -- 4. Modeling with Data -- 5. Factorial Experiments -- 6. Fractional Factorial Designs -- 7. Higher Order Approximations -- 8. Mixture Experiments -- 9. Some Examples and Applications -- 10. Binary Logistic Regression -- 11. Reliability, Life Testing, and Shelf Life -- 12. Some Bayesian Concepts -- 13. Validation and Verification -- 14. Simulation and Random Variable Generation -- 15. Taguchi Methods® and Robust Design -- References. 330 $aThis textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it.  In contrast, engineers and applied scientists design products, processes, and solutions to problems.   That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm.  Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes.  Many seem to think that statistics is only useful for demonstrating that a device or processactually does what it was designed to do.  Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process.  This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages:  SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods. 606 $aStatistics 606 $aStatistics 606 $aBiotechnology 606 $aChemistry, Technical 606 $aEcology 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aStatistical Theory and Methods 606 $aBiotechnology 606 $aChemical Bioengineering 606 $aIndustrial Chemistry 606 $aEnvironmental Sciences 615 0$aStatistics. 615 0$aStatistics. 615 0$aBiotechnology. 615 0$aChemistry, Technical. 615 0$aEcology. 615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aStatistical Theory and Methods. 615 24$aBiotechnology. 615 24$aChemical Bioengineering. 615 24$aIndustrial Chemistry. 615 24$aEnvironmental Sciences. 676 $a620.0042 700 $aPardo$b Scott A$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755901 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254065103321 996 $aEmpirical modeling and data analysis for engineers and applied scientists$91523293 997 $aUNINA