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010   $a1-4842-6053-8
024 7 $a10.1007/978-1-4842-6053-1
035   $a(CKB)4100000011513197
035   $a(DE-He213)978-1-4842-6053-1
035   $a(MiAaPQ)EBC6381185
035   $a(CaSebORM)9781484260531
035   $a(PPN)26151735X
035   $a(EXLCZ)994100000011513197
100   $a20201017d2020      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBeginning R 4 $eFrom Beginner to Pro /$fby Matt Wiley, Joshua F. Wiley
205   $a1st ed. 2020.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020.
215   $a1 online resource (XX, 467 p. 110 illus., 66 illus. in color.) 
311   $a1-4842-6052-X 
320   $aIncludes bibliographical references and index.
327   $a1: Installing R -- 2: Installing Packages and Using Libraries -- 3: Data Input and Output -- 4: Working with Data -- 5: Data and Samples -- 6: Descriptive Statistics -- 7: Understanding Probability and Distribution -- 8: Correlation and Regression -- 9: Confidence Intervals -- 10: Hypothesis Testing -- 11: Multiple Regression -- 12: Moderated Regression -- 13: Analysts of Variance -- Bibliography.
330   $aLearn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling. Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. You will: Acquire and install R and RStudio Import and export data from multiple file formats Analyze data and generate graphics (including confidence intervals) Interactively conduct hypothesis testing Code multiple and moderated regression solutions.
606   $aCompilers (Computer programs)
606   $aComputer programming
606   $aComputer science?Mathematics
606   $aMathematical statistics
606   $aCompilers and Interpreters
606   $aProgramming Techniques
606   $aProbability and Statistics in Computer Science
615  0$aCompilers (Computer programs).
615  0$aComputer programming.
615  0$aComputer science?Mathematics.
615  0$aMathematical statistics.
615 14$aCompilers and Interpreters.
615 24$aProgramming Techniques.
615 24$aProbability and Statistics in Computer Science.
676   $a005.262
700   $aWiley$b Matt$0897297
702   $aWiley$b Joshua F.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910427048803321
996   $aBeginning R 4$92499649
997   $aUNINA