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LDR03850cmm u2200661Ki 4500
001000000310831
003OCoLC
00520230525140958
006m o d
007cr cnu---unuuu
008170502s2017 nyu ob 001 0 eng d
020 ▼a 9781462530298 ▼q (electronic bk.)
020 ▼a 146253029X ▼q (electronic bk.)
020 ▼z 9781462530267
020 ▼z 1462530265
020 ▼z 9781462530274
020 ▼z 1462530273
0291 ▼a AU@ ▼b 000060698917
035 ▼a (OCoLC)985106063
040 ▼a N$T ▼b eng ▼e rda ▼e pn ▼c N$T ▼d YDX ▼d IDEBK ▼d CSAIL ▼d GWM ▼d OCLCO ▼d NRC ▼d OCLCQ ▼d OCLCO ▼d 248032
049 ▼a MAIN
050 4 ▼a QA279.5 ▼b .S745 2017eb
072 7 ▼a MAT ▼x 003000 ▼2 bisacsh
072 7 ▼a MAT ▼x 029000 ▼2 bisacsh
08204 ▼a 519.50285/53 ▼2 23
1001 ▼a Stanton, Jeffrey M., ▼d 1961-
24510 ▼a Reasoning with data : ▼b an introduction to traditional and Bayesian statistics using R / ▼c Jeffrey M. Stanton.
264 1 ▼a New York : ▼b The Guilford Press, ▼c [2017]
300 ▼a 1 online resource.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
504 ▼a Includes bibliographical references and index.
520 ▼a Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources.-- ▼c Provided by Publisher.
5880 ▼a Print version record.
590 ▼a eBooks on EBSCOhost ▼b All EBSCO eBooks
650 0 ▼a Bayesian statistical decision theory ▼v Problems, exercises, etc.
650 0 ▼a Bayesian statistical decision theory ▼x Data processing.
650 0 ▼a Mathematical statistics ▼v Problems, exercises, etc.
650 0 ▼a Mathematical statistics ▼x Data processing.
650 0 ▼a R (Computer program language)
650 7 ▼a Bayesian statistical decision theory. ▼2 fast ▼0 (OCoLC)fst00829019
650 7 ▼a Bayesian statistical decision theory ▼x Data processing. ▼2 fast ▼0 (OCoLC)fst00829020
650 7 ▼a Mathematical statistics. ▼2 fast ▼0 (OCoLC)fst01012127
650 7 ▼a Mathematical statistics ▼x Data processing. ▼2 fast ▼0 (OCoLC)fst01012133
650 7 ▼a R (Computer program language) ▼2 fast ▼0 (OCoLC)fst01086207
650 7 ▼a MATHEMATICS / Applied ▼2 bisacsh
650 7 ▼a MATHEMATICS / Probability & Statistics / General ▼2 bisacsh
650 2 ▼a Statistics as Topic. ▼0 (DNLM)D013223
650 2 ▼a Bayes Theorem. ▼0 (DNLM)D001499
655 7 ▼a Problems and exercises. ▼2 fast ▼0 (OCoLC)fst01423783
655 4 ▼a Electronic books.
77608 ▼i Print version: ▼a Stanton, Jeffrey M., 1961- ▼t Reasoning with data. ▼d New York : The Guilford Press, [2017] ▼z 9781462530267 ▼w (DLC) 2017004984 ▼w (OCoLC)960845674
85640 ▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1512441
938 ▼a ProQuest MyiLibrary Digital eBook Collection ▼b IDEB ▼n cis38095162
938 ▼a YBP Library Services ▼b YANK ▼n 14524772
938 ▼a YBP Library Services ▼b YANK ▼n 14272323
938 ▼a EBSCOhost ▼b EBSC ▼n 1512441
990 ▼a 관리자