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001 9910720061803321
005 20251008145144.0
010   $a9783031277412$b(electronic bk.)
010   $z9783031277405
024 7 $a10.1007/978-3-031-27741-2
035   $a(MiAaPQ)EBC7246173
035   $a(Au-PeEL)EBL7246173
035   $a(OCoLC)1379480765
035   $a(PPN)270619151
035   $a(CKB)26599881000041
035   $a(DE-He213)978-3-031-27741-2
035   $a(EXLCZ)9926599881000041
100   $a20230505d2023      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIntroductory Applied Statistics $eWith Resampling Methods & R /$fby Bruce Blaine
205   $a1st ed. 2023.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023.
215   $a1 online resource (197 pages)
225 1 $aMathematics and Statistics Series
311 08$aPrint version: Blaine, Bruce Introductory Applied Statistics Cham : Springer International Publishing AG,c2023 9783031277405 
327   $a1. Foundations I: Introductory Data Analysis with R -- 2. Data Analysis in Bivariate Data: Foundations -- 3. Statistics and Data Analysis in an ANOVA Model -- 4. Statistics and Data Analysis in a Proportions Model -- 5. Statistics and Data Analysis in a Regression Model -- 6. Statistics and Data Analysis in a Logistic Model -- 7. Statistical Inference I: Randomization Methods for Hypothesis Testing -- 8. Statistical Inference II: Bootstrapping Methods for Parameter Estimation -- 9. Using Resampling Methods for Statistical Inference: Four Examples -- 10. Statistics and Data Analysis in a Pre-Post Design. .
330   $aThis book offers an introduction to applied statistics through data analysis, integrating statistical computing methods. It covers robust and non-robust descriptive statistics used in each of four bivariate statistical models that are commonly used in research: ANOVA, proportions, regression, and logistic. The text teaches statistical inference principles using resampling methods (such as randomization and bootstrapping), covering methods for hypothesis testing and parameter estimation. These methods are applied to each statistical model introduced in preceding chapters. Data analytic examples are used to teach statistical concepts throughout, and students are introduced to the R packages and functions required for basic data analysis in each of the four models. The text also includes introductory guidance to the fundamentals of data wrangling, as well as examples of write-ups so that students can learn how to communicate findings. Each chapter includes problems forpractice or assessment. Supplemental instructional videos are also available as an additional aid to instructors, or as a general resource to students. This book is intended for an introductory or basic statistics course with an applied focus, or an introductory analytics course, at the undergraduate level in a two-year or four-year institution. This can be used for students with a variety of disciplinary backgrounds, from business, to the social sciences, to medicine. No sophisticated mathematical background is required.
410  0$aMathematics and Statistics Series
606   $aQuantitative research
606   $aStatistics
606   $aMathematical statistics$xData processing
606   $aData Analysis and Big Data
606   $aApplied Statistics
606   $aStatistics and Computing
615  0$aQuantitative research.
615  0$aStatistics.
615  0$aMathematical statistics$xData processing.
615 14$aData Analysis and Big Data.
615 24$aApplied Statistics.
615 24$aStatistics and Computing.
676   $a610
700   $aBlaine$b Bruce$01355335
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910720061803321
996   $aIntroductory Applied Statistics$93359451
997   $aUNINA