Reasoning with data :: an introduction to traditional and Bayesian statistics using R /
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 class...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
The Guilford Press,
[2017]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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.-- |
| Beschreibung: | 1 online resource (x, 325 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781462530298 146253029X |
Internformat
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| 245 | 1 | 0 | |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] | |
| 264 | 4 | |c ©2017 | |
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| 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. | |
| 588 | 0 | |a Print version record. | |
| 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. |0 http://id.loc.gov/authorities/subjects/sh85082137 | |
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 0 | |a Statistics. |0 http://id.loc.gov/authorities/subjects/sh85127580 | |
| 650 | 2 | |a Statistics as Topic | |
| 650 | 2 | |a Bayes Theorem | |
| 650 | 6 | |a Théorie de la décision bayésienne |v Problèmes et exercices. | |
| 650 | 6 | |a Théorie de la décision bayésienne |x Informatique. | |
| 650 | 6 | |a Statistique mathématique |x Informatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Statistiques. | |
| 650 | 6 | |a Théorème de Bayes. | |
| 650 | 6 | |a Statistique. | |
| 650 | 7 | |a statistics. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Statistics |2 fast | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
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| 650 | 7 | |a Mathematical statistics |2 fast | |
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| 650 | 7 | |a R (Computer program language) |2 fast | |
| 655 | 7 | |a Problems and exercises |2 fast | |
| 758 | |i has work: |a Reasoning with data (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGdfM7VmJ8mDRrB3hjKY4C |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| contents | 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. |
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| publisher | The Guilford Press, |
| record_format | marc |
| spelling | Stanton, Jeffrey M., 1961- author. https://id.oclc.org/worldcat/entity/E39PBJth7dhHfKt3fhmB7GQjG3 http://id.loc.gov/authorities/names/n2006013082 Reasoning with data : an introduction to traditional and Bayesian statistics using R / Jeffrey M. Stanton. New York : The Guilford Press, [2017] ©2017 1 online resource (x, 325 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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.-- Provided by Publisher. Includes bibliographical references and index. 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. Print version record. Bayesian statistical decision theory Problems, exercises, etc. Bayesian statistical decision theory Data processing. Mathematical statistics Problems, exercises, etc. Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Statistics. http://id.loc.gov/authorities/subjects/sh85127580 Statistics as Topic Bayes Theorem Théorie de la décision bayésienne Problèmes et exercices. Théorie de la décision bayésienne Informatique. Statistique mathématique Informatique. R (Langage de programmation) Statistiques. Théorème de Bayes. Statistique. statistics. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistics fast Bayesian statistical decision theory fast Bayesian statistical decision theory Data processing fast Mathematical statistics fast Mathematical statistics Data processing fast R (Computer program language) fast Problems and exercises fast has work: Reasoning with data (Text) https://id.oclc.org/worldcat/entity/E39PCGdfM7VmJ8mDRrB3hjKY4C https://id.oclc.org/worldcat/ontology/hasWork Print version: Stanton, Jeffrey M., 1961- Reasoning with data. New York : The Guilford Press, [2017] 9781462530267 (DLC) 2017004984 (OCoLC)960845674 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1512441 Volltext |
| spellingShingle | Stanton, Jeffrey M., 1961- Reasoning with data : an introduction to traditional and Bayesian statistics using R / 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. Bayesian statistical decision theory Problems, exercises, etc. Bayesian statistical decision theory Data processing. Mathematical statistics Problems, exercises, etc. Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Statistics. http://id.loc.gov/authorities/subjects/sh85127580 Statistics as Topic Bayes Theorem Théorie de la décision bayésienne Problèmes et exercices. Théorie de la décision bayésienne Informatique. Statistique mathématique Informatique. R (Langage de programmation) Statistiques. Théorème de Bayes. Statistique. statistics. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistics fast Bayesian statistical decision theory fast Bayesian statistical decision theory Data processing fast Mathematical statistics fast Mathematical statistics Data processing fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85082137 http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh85127580 |
| title | Reasoning with data : an introduction to traditional and Bayesian statistics using R / |
| title_auth | Reasoning with data : an introduction to traditional and Bayesian statistics using R / |
| title_exact_search | Reasoning with data : an introduction to traditional and Bayesian statistics using R / |
| title_full | Reasoning with data : an introduction to traditional and Bayesian statistics using R / Jeffrey M. Stanton. |
| title_fullStr | Reasoning with data : an introduction to traditional and Bayesian statistics using R / Jeffrey M. Stanton. |
| title_full_unstemmed | Reasoning with data : an introduction to traditional and Bayesian statistics using R / Jeffrey M. Stanton. |
| title_short | Reasoning with data : |
| title_sort | reasoning with data an introduction to traditional and bayesian statistics using r |
| title_sub | an introduction to traditional and Bayesian statistics using R / |
| topic | Bayesian statistical decision theory Problems, exercises, etc. Bayesian statistical decision theory Data processing. Mathematical statistics Problems, exercises, etc. Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Statistics. http://id.loc.gov/authorities/subjects/sh85127580 Statistics as Topic Bayes Theorem Théorie de la décision bayésienne Problèmes et exercices. Théorie de la décision bayésienne Informatique. Statistique mathématique Informatique. R (Langage de programmation) Statistiques. Théorème de Bayes. Statistique. statistics. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistics fast Bayesian statistical decision theory fast Bayesian statistical decision theory Data processing fast Mathematical statistics fast Mathematical statistics Data processing fast R (Computer program language) fast |
| topic_facet | Bayesian statistical decision theory Problems, exercises, etc. Bayesian statistical decision theory Data processing. Mathematical statistics Problems, exercises, etc. Mathematical statistics Data processing. R (Computer program language) Statistics. Statistics as Topic Bayes Theorem Théorie de la décision bayésienne Problèmes et exercices. Théorie de la décision bayésienne Informatique. Statistique mathématique Informatique. R (Langage de programmation) Statistiques. Théorème de Bayes. Statistique. statistics. MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Statistics Bayesian statistical decision theory Bayesian statistical decision theory Data processing Mathematical statistics Mathematical statistics Data processing Problems and exercises |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1512441 |
| work_keys_str_mv | AT stantonjeffreym reasoningwithdataanintroductiontotraditionalandbayesianstatisticsusingr |