03339nam a22005655i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001600186072002300202072001500225100008100240245007100321250001800392264006900410300003300479336002600512337002600538338003600564347002400600490002300624505033800647520094700985650002501932650005301957650004202010650003602052650002302088650002602111650003102137650009502168650003702263650005902300650003602359650004202395700008202437710003402519773002602553776003602579776003602615830003002651856004602681912001402727912001502741999001702756978-3-031-76074-7DE-He21320251030114611.0cr nn 008mamaa241210s2024    sz |    s    |||| 0|eng d  a97830317607479978-3-031-76074-77 a10.1007/978-3-031-76074-72doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema1 aAlbert, Jim.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98873610aR by Exampleh[electronic resource] /cby Jim Albert, Maria Rizzo.  a2nd ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 454 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUse R!,x2197-57440 aIntroduction -- Quantitative Data -- Categorical Data -- Exploratory Data Analysis -- Presentation Graphics -- Importing Data -- Basic Inference Models -- Regression -- Analysis of Variance I -- Analysis of Variance II -- Randomization Tests -- Multivariate Data -- Simulation Experiments -- Bayesian Modeling -- Monte Carlo Methods.  aNow in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R. The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods. 0aStatistics .965982 0aMathematical statisticsxData processing.964750 0aComputer sciencexMathematics.964041 0aMathematical statistics.967233 0aStatistics.965982 0aCoding theory.965513 0aInformation theory.95778314aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.96691124aStatistics and Computing.96644624aProbability and Statistics in Computer Science.96723424aStatistical Theory and Methods.24aCoding and Information Theory.9655151 aRizzo, Maria.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9887372 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303176073008iPrinted edition:z9783031760754 0aUse R!,x2197-574497472840uhttps://doi.org/10.1007/978-3-031-76074-7  aZDB-2-SMA  aZDB-2-SXMS  c65604d65604