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001434691 001__ 1434691
001434691 003__ OCoLC
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001434691 019__ $$a1237410616$$a1240648273
001434691 020__ $$a9783030562595
001434691 020__ $$a303056259X
001434691 020__ $$z9783030562588
001434691 0247_ $$a10.1007/978-3-030-56259-5$$2doi
001434691 035__ $$aSP(OCoLC)1241066677
001434691 040__ $$aDKU$$beng$$epn$$cDKU$$dOCLCO$$dOCLCQ$$dEBLCP$$dSFB$$dGW5XE$$dOCLCF$$dOCLCO$$dOCLCQ
001434691 049__ $$aISEA
001434691 050_4 $$aTK1-9971
001434691 08204 $$a621.382$$223
001434691 1001_ $$aShankar, P. Mohana.,$$eauthor$$4aut$$4http://id.loc.gov/vocabulary/relators/aut
001434691 24510 $$aProbability, Random Variables, and Data Analytics with Engineering Applications /$$cby P. Mohana Shankar.
001434691 250__ $$a1st ed. 2021.
001434691 264_1 $$aCham :$$bSpringer International Publishing :$$bImprint :$$bSpringer,$$c2021.
001434691 300__ $$a1 online resource (XII, 473 pages 206 illustrations, 202 illustrations in color)
001434691 336__ $$atext$$btxt$$2rdacontent
001434691 337__ $$acomputer$$bc$$2rdamedia
001434691 338__ $$aonline resource$$bcr$$2rdacarrier
001434691 347__ $$atext file
001434691 347__ $$bPDF
001434691 504__ $$aIncludes bibliographical references and index.
001434691 5050_ $$aChapter 1. Introduction -- Chapter 2. Sets, Venn diagrams, Probability and Bayes' Rule -- Chapter 3. Concept of a random variable -- Chapter 4. Multiple random variables and their Characteristics -- Chapter 5. Applications to Data Analytics and Modeling.
001434691 506__ $$aAccess limited to authorized users.
001434691 520__ $$aThis book bridges the gap between theory and applications that currently exist in undergraduate engineering probability textbooks. It offers examples and exercises using data (sets) in addition to traditional analytical and conceptual ones. Conceptual topics such as one and two random variables, transformations, etc. are presented with a focus on applications. Data analytics related portions of the book offer detailed coverage of receiver operating characteristics curves, parametric and nonparametric hypothesis testing, bootstrapping, performance analysis of machine vision and clinical diagnostic systems, and so on. With Excel spreadsheets of data provided, the book offers a balanced mix of traditional topics and data analytics expanding the scope, diversity, and applications of engineering probability. This makes the contents of the book relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies. A full suite of classroom material is included. A solutions manual is available for instructors. Bridges the gap between conceptual topics and data analytics through appropriate examples and exercises; Features 100's of exercises comprising of traditional analytical ones and others based on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with computational ones, providing two-level verifications of a majority of examples and exercises.
001434691 650_0 $$aElectrical engineering.
001434691 650_0 $$aApplied mathematics.
001434691 650_0 $$aEngineering mathematics.
001434691 650_0 $$aProbabilities.
001434691 650_0 $$aStatistics.
001434691 650_6 $$aGénie électrique.
001434691 650_6 $$aMathématiques de l'ingénieur.
001434691 650_6 $$aProbabilités.
001434691 655_0 $$aElectronic books.
001434691 77608 $$iPrint version: $$z9783030562588
001434691 77608 $$iPrint version: $$z9783030562601
001434691 77608 $$iPrint version: $$z9783030562618
001434691 852__ $$bebk
001434691 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-56259-5$$zOnline Access$$91397441.1
001434691 909CO $$ooai:library.usi.edu:1434691$$pGLOBAL_SET
001434691 980__ $$aBIB
001434691 980__ $$aEBOOK
001434691 982__ $$aEbook
001434691 983__ $$aOnline
001434691 994__ $$a92$$bISE