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020 _a9783031323300
_9978-3-031-32330-0
024 7 _a10.1007/978-3-031-32330-0
_2doi
050 4 _aTA329-348
050 4 _aTA345-345.5
072 7 _aTBJ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aTBJ
_2thema
082 0 4 _a620
_223
100 1 _aChattamvelli, Rajan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988528
245 1 0 _aDescriptive Statistics for Scientists and Engineers
_h[electronic resource] :
_bApplications in R /
_cby Rajan Chattamvelli, Ramalingam Shanmugam.
250 _a2nd ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXI, 130 p. 8 illus., 3 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Mathematics & Statistics,
_x1938-1751
505 0 _aDescriptive Statistics -- Measures of Location -- Measures of Spread -- Measures of Skewness and Kurtosis.
520 _aThis book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects. Some applications in bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow. In addition, this book: Provides exercises throughout that are illustrated via the R programming language Assists readers to do various numeric data transformations, normality testing, etc. Aids readers to build, analyze, and interpret various descriptive statistical models Presents numerous examples from various engineering fields.
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering
_xData processing.
_99340
650 0 _aQuantitative research.
_94633
650 0 _aStatisticsĀ .
_931616
650 0 _aProbabilities.
_94604
650 1 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aData Analysis and Big Data.
_988530
650 2 4 _aApplied Statistics.
_945885
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
650 2 4 _aStatistics.
_914134
650 2 4 _aProbability Theory.
_917950
700 1 _aShanmugam, Ramalingam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988532
710 2 _aSpringerLink (Online service)
_988534
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031323294
776 0 8 _iPrinted edition:
_z9783031323317
776 0 8 _iPrinted edition:
_z9783031323324
830 0 _aSynthesis Lectures on Mathematics & Statistics,
_x1938-1751
_988536
856 4 0 _uhttps://doi.org/10.1007/978-3-031-32330-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c86263
_d86263