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03745aam a2200553 i 4500 001 BEE610B868B111E9997FFC0F97128E48 003 SILO 005 20190427010012 008 170307s2017 nyua b 001 0 eng 010 $a 2017004984 020 $a 1462530273 020 $a 9781462530274 020 $a 1462530265 020 $a 9781462530267 035 $a (OCoLC)960845674 040 $a DLC $b eng $e rda $c DLC $d BTCTA $d YDX $d BDX $d OCLCO $d OCLCQ $d OCLCF $d YDX $d OCLCO $d FIE $d MRM $d VKC $d HRM $d OCLCO $d UKMGB $d OCLCA $d CHVBK $d NMW $d OCLCO $d VFL $d ROB $d OCLCO $d SILO 042 $a pcc 050 00 $a QA279.5 $b .S745 2017 060 4 $a QA 279.5 S745 2017 082 00 $a 519.50285/53 $2 23 100 1 $a Stanton, Jeffrey M., $d 1961- $e author. 245 10 $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 x, 325 pages : $b illustrations ; $c 26 cm 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. 504 $a Includes bibliographical references and index. 505 0 $a Statistical vocabulary -- Reasoning with probability -- Probabilities in the long run -- Introducing the logic of inference using confidence intervals -- Bayesian and traditional hypothesis testing -- Comparing groups and analyzing experiments -- Associations between variables -- Linear multiple regression -- Interactions in ANOVA and regression -- Logistic regression -- Analyzing change over time -- Dealing with too many variables -- All together now. 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 2 $a Bayes Theorem. 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 Bayes-Entscheidungstheorie $2 gnd 650 7 $a Datenanalyse $2 gnd 650 7 $a R $g Programm $2 gnd 650 7 $a Statistik $2 gnd 655 7 $a Problems and exercises. $2 fast $0 (OCoLC)fst01423783 655 7 $a Problems and exercises. $2 lcgft 776 08 $i ebook version : $z 9781462530298 941 $a 1 952 $l SOAX911 $d 20190427010719.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=BEE610B868B111E9997FFC0F97128E48 994 $a C0 $b IOKInitiate Another SILO Locator Search