02142nam a2200313 i 4500991000657999707536041126s2003 gw a b 000 0 eng d354040810Xb13253645-39ule_instDip.to Fisicaeng530.14322LC QC17853.1.5Quantum gravity :from theory to experimental search /D. Giulini, C. Kiefer, C. Lämmerzahl (eds.)Berlin ;New York :Springer,c2003xii, 400 p. :ill. ;24 cmLecture notes in physics,0075-8450 ;631Includes bibliographical referencesQuantum gravity - a general introduction / Claus Kiefer -- That strange procedure called quantisation / Domenico Giulini -- Lectures on loop quantum gravity / Thomas Thiemann -- A discrete history of the Lorentzian path integral / Renate Loll -- Introduction to string theory / Thomas Mohaupt -- Quantum theory of gravitational collapse : lecture notes on quantum conchology / Petr Hþajþi{153}cek -- Primordial black holes as a probe of the early universe, gravitational collapse, high energy physics and quantum gravity / Bernard J. Carr -- On the assignment of entropy to black holes / Daniel Sudarsky -- Physics with large extra dimensions and non-Newtonian gravity at sub-mm distances / Ignatios Antoniadis -- Quantum states of neutrons in the gravitational field and limits for non-Newtonian interaction in the range between 1 [mu]m and 10 [mu]m / Hartmut Abele, Stefan Baessler, Alexander Westphal -- The Einstein equivalence principle and the search for new physics / Claus L·ammerzahlQuantum gravityGiulini, Domenicoauthorhttp://id.loc.gov/vocabulary/relators/aut62073Kiefer, ClausLämmerzahl, Claus.b1325364502-04-1426-11-04991000657999707536LE006 53.1.5 GIU12006000153454le006pE69.95-l- 00000.i1394957326-11-04Quantum gravity3368872UNISALENTOle00626-11-04ma -enggw 0003987nam 2200505 450 991081771740332120200423115832.01-119-25643-71-119-25642-91-119-25644-5(CKB)4330000000009719(MiAaPQ)EBC5847433(OCoLC)1117710087(CaSebORM)9781119256410(EXLCZ)99433000000000971920190913d2020 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierSAS for R users a book for budding data scientists /Ajay Ohri1st editionHoboken, New Jersey :Wiley,[2020]©20201 online resource (211 pages)1-119-25641-0 Includes bibliographical references and index.About SAS and R -- Data input, import and print -- Data inspection and cleaning -- Handling dates, strings, numbers -- Numerical summary and group by analysis -- Frequency distributions and cross tabulations -- Using SQL with SAS and R -- Functions, loops, arrays, macros -- Data visualization -- Data output -- Statistics for data scientists.BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.SAS (Computer program language)R (Computer program language)StatisticsData processingSAS (Computer program language)R (Computer program language)StatisticsData processing.005.55Ohri A(Ajay),1060276MiAaPQMiAaPQMiAaPQBOOK9910817717403321SAS for R users3920910UNINA