Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1608875
M. Bárcena, M. Garín, Ana Martín, F. Tusell, A. Unzueta
Abstract Teaching some concepts in statistics greatly benefits from individual practice with immediate feedback. In order to provide such practice to a large number of students we have written a simulator based on an historical event: the loss in May 22, 1968, and subsequent search for the nuclear submarine USS Scorpion. Students work on a simplified version of the search and can see probabilities change in response to new evidence. The simulator is designed to assist in the teaching of Bayesian concepts, in particular Bayesian updating. It has been deployed in our courses and our experience and results are described, as well as the reactions of our students to its use. The simulator is open source, freely available and easy to implement and run, as it only requires a machine to serve web pages. We explain in detail our experience with its deployment and use.
{"title":"A Web Simulator to Assist in the Teaching of Bayes’ Theorem","authors":"M. Bárcena, M. Garín, Ana Martín, F. Tusell, A. Unzueta","doi":"10.1080/10691898.2019.1608875","DOIUrl":"https://doi.org/10.1080/10691898.2019.1608875","url":null,"abstract":"Abstract Teaching some concepts in statistics greatly benefits from individual practice with immediate feedback. In order to provide such practice to a large number of students we have written a simulator based on an historical event: the loss in May 22, 1968, and subsequent search for the nuclear submarine USS Scorpion. Students work on a simplified version of the search and can see probabilities change in response to new evidence. The simulator is designed to assist in the teaching of Bayesian concepts, in particular Bayesian updating. It has been deployed in our courses and our experience and results are described, as well as the reactions of our students to its use. The simulator is open source, freely available and easy to implement and run, as it only requires a machine to serve web pages. We explain in detail our experience with its deployment and use.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"68 - 78"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1608875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48058490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-02DOI: 10.1080/10691898.2019.1565935
Randall E. Groth
Abstract The Common Core State Standards for Mathematics have a widespread impact on children’s statistical learning opportunities. The Grade 6 standards are particularly ambitious in the goals they set. In this critique, experiences helping children work toward the Grade 6 Common Core statistics expectations are used in conjunction with previous research to identify ways in which the Grades 4–6 standards might be supplemented or revised to help maximize learning. It is suggested that opportunities for children to perceive datasets as aggregates and to draw reasonable conclusions about statistical data by attending to context should be purposefully introduced in Grades 4–5. Currently, the Common Core does not have explicit learning standards for these activities in fourth and fifth grade. It is also suggested that teachers help students question their natural tendencies to focus extensively on the mode when summarizing data. The current standards do not specifically mention the mode. Revising or supplementing the Common Core in the suggested ways holds potential to make the Grade 6 statistical learning standards more attainable for children and to help teachers better anticipate the statistical thinking tendencies that are likely to emerge during classroom discourse.
{"title":"Applying Design-Based Research Findings to Improve the Common Core State Standards for Data and Statistics in Grades 4–6","authors":"Randall E. Groth","doi":"10.1080/10691898.2019.1565935","DOIUrl":"https://doi.org/10.1080/10691898.2019.1565935","url":null,"abstract":"Abstract The Common Core State Standards for Mathematics have a widespread impact on children’s statistical learning opportunities. The Grade 6 standards are particularly ambitious in the goals they set. In this critique, experiences helping children work toward the Grade 6 Common Core statistics expectations are used in conjunction with previous research to identify ways in which the Grades 4–6 standards might be supplemented or revised to help maximize learning. It is suggested that opportunities for children to perceive datasets as aggregates and to draw reasonable conclusions about statistical data by attending to context should be purposefully introduced in Grades 4–5. Currently, the Common Core does not have explicit learning standards for these activities in fourth and fifth grade. It is also suggested that teachers help students question their natural tendencies to focus extensively on the mode when summarizing data. The current standards do not specifically mention the mode. Revising or supplementing the Common Core in the suggested ways holds potential to make the Grade 6 statistical learning standards more attainable for children and to help teachers better anticipate the statistical thinking tendencies that are likely to emerge during classroom discourse.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"29 - 36"},"PeriodicalIF":2.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1565935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42982832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-02DOI: 10.1080/10691898.2019.1565934
Philip A. Yates
ABSTRACT When exposed to principal components analysis for the first time, students can sometimes miss the primary purpose of the analysis. Often the focus is solely on data reduction and what to do after the dimensions of the data have been reduced is ignored. The datasets discussed here can be used as an in-class example, a homework assignment, or a written project, with a focus in this article as an in-class example. The data give the students an opportunity to perform principal components analysis and follow-up analyses on a real dataset that is not necessarily the easiest to handle.
{"title":"Punching a Ticket to Cooperstown","authors":"Philip A. Yates","doi":"10.1080/10691898.2019.1565934","DOIUrl":"https://doi.org/10.1080/10691898.2019.1565934","url":null,"abstract":"ABSTRACT When exposed to principal components analysis for the first time, students can sometimes miss the primary purpose of the analysis. Often the focus is solely on data reduction and what to do after the dimensions of the data have been reduced is ignored. The datasets discussed here can be used as an in-class example, a homework assignment, or a written project, with a focus in this article as an in-class example. The data give the students an opportunity to perform principal components analysis and follow-up analyses on a real dataset that is not necessarily the easiest to handle.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"37 - 47"},"PeriodicalIF":2.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1565934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44357864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-02DOI: 10.1080/10691898.2019.1603506
Allan Rossman, J. Witmer
{"title":"Interview With Jeff Witmer","authors":"Allan Rossman, J. Witmer","doi":"10.1080/10691898.2019.1603506","DOIUrl":"https://doi.org/10.1080/10691898.2019.1603506","url":null,"abstract":"","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"48 - 57"},"PeriodicalIF":2.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1603506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41946274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-02DOI: 10.1080/10691898.2018.1564638
A. Loy, Shonda Kuiper, Laura M. Chihara
Abstract This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science—such as visualization, data manipulation, and database usage—that instructors at a wide-range of institutions can incorporate into existing statistics courses. The resulting materials are flexible enough to serve both introductory and advanced students, and aim to provide students with the skills to experiment with data, find their own patterns, and ask their own questions. In this article, we discuss a tutorial on data visualization and a case study synthesizing data wrangling and visualization skills in detail, and provide references to additional class-tested materials. R and R Markdown are used for all of the activities.
{"title":"Supporting Data Science in the Statistics Curriculum","authors":"A. Loy, Shonda Kuiper, Laura M. Chihara","doi":"10.1080/10691898.2018.1564638","DOIUrl":"https://doi.org/10.1080/10691898.2018.1564638","url":null,"abstract":"Abstract This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science—such as visualization, data manipulation, and database usage—that instructors at a wide-range of institutions can incorporate into existing statistics courses. The resulting materials are flexible enough to serve both introductory and advanced students, and aim to provide students with the skills to experiment with data, find their own patterns, and ask their own questions. In this article, we discuss a tutorial on data visualization and a case study synthesizing data wrangling and visualization skills in detail, and provide references to additional class-tested materials. R and R Markdown are used for all of the activities.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"11 - 2"},"PeriodicalIF":2.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2018.1564638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45868803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-02DOI: 10.1080/10691898.2019.1600387
Kevin Ross, Dennis L. Sun
Abstract Simulation is an effective tool for analyzing probability models as well as for facilitating understanding of concepts in probability and statistics. Unfortunately, implementing a simulation from scratch often requires users to think about programming issues that are not relevant to the simulation itself. We have developed a Python package called Symbulate (https://github.com/dlsun/symbulate) which provides a user friendly framework for conducting simulations involving probability models. The syntax of Symbulate reflects the “language of probability” and makes it intuitive to specify, run, analyze, and visualize the results of a simulation. Moreover, Symbulate’s consistency with the mathematics of probability reinforces understanding of probabilistic concepts. Symbulate can be used in introductory through graduate courses, with a wide variety of probability concepts and problems, including: probability spaces; events; discrete and continuous random variables; joint, conditional, and marginal distributions; stochastic processes; discrete- and continuous-time Markov chains; Poisson processes; and Gaussian processes, including Brownian motion. In this work, we demonstrate Symbulate, discuss its main pedagogical features, present examples of Symbulate graphics, and share some of our experiences using Symbulate in courses.
{"title":"Symbulate: Simulation in the Language of Probability","authors":"Kevin Ross, Dennis L. Sun","doi":"10.1080/10691898.2019.1600387","DOIUrl":"https://doi.org/10.1080/10691898.2019.1600387","url":null,"abstract":"Abstract Simulation is an effective tool for analyzing probability models as well as for facilitating understanding of concepts in probability and statistics. Unfortunately, implementing a simulation from scratch often requires users to think about programming issues that are not relevant to the simulation itself. We have developed a Python package called Symbulate (https://github.com/dlsun/symbulate) which provides a user friendly framework for conducting simulations involving probability models. The syntax of Symbulate reflects the “language of probability” and makes it intuitive to specify, run, analyze, and visualize the results of a simulation. Moreover, Symbulate’s consistency with the mathematics of probability reinforces understanding of probabilistic concepts. Symbulate can be used in introductory through graduate courses, with a wide variety of probability concepts and problems, including: probability spaces; events; discrete and continuous random variables; joint, conditional, and marginal distributions; stochastic processes; discrete- and continuous-time Markov chains; Poisson processes; and Gaussian processes, including Brownian motion. In this work, we demonstrate Symbulate, discuss its main pedagogical features, present examples of Symbulate graphics, and share some of our experiences using Symbulate in courses.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"12 - 28"},"PeriodicalIF":2.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1600387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46484121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-08DOI: 10.1080/10691898.2019.1687370
Eric A. Vance, Heather S. Smith
Abstract Statistics and data science are especially collaborative disciplines that typically require practitioners to interact with many different people or groups. Consequently, interdisciplinary collaboration skills are part of the personal and professional skills essential for success as an applied statistician or data scientist. These skills are learnable and teachable, and learning and improving collaboration skills provides a way to enhance one’s practice of statistics and data science. To help individuals learn these skills and organizations to teach them, we have developed a framework covering five essential components of statistical collaboration: Attitude, Structure, Content, Communication, and Relationship. We call this the ASCCR Frame. This framework can be incorporated into formal training programs in the classroom or on the job and can also be used by individuals through self-study. We show how this framework can be applied specifically to statisticians and data scientists to improve their collaboration skills and their interdisciplinary impact. We believe that the ASCCR Frame can help organize and stimulate research and teaching in interdisciplinary collaboration and call on individuals and organizations to begin generating evidence regarding its effectiveness.
{"title":"The ASCCR Frame for Learning Essential Collaboration Skills","authors":"Eric A. Vance, Heather S. Smith","doi":"10.1080/10691898.2019.1687370","DOIUrl":"https://doi.org/10.1080/10691898.2019.1687370","url":null,"abstract":"Abstract Statistics and data science are especially collaborative disciplines that typically require practitioners to interact with many different people or groups. Consequently, interdisciplinary collaboration skills are part of the personal and professional skills essential for success as an applied statistician or data scientist. These skills are learnable and teachable, and learning and improving collaboration skills provides a way to enhance one’s practice of statistics and data science. To help individuals learn these skills and organizations to teach them, we have developed a framework covering five essential components of statistical collaboration: Attitude, Structure, Content, Communication, and Relationship. We call this the ASCCR Frame. This framework can be incorporated into formal training programs in the classroom or on the job and can also be used by individuals through self-study. We show how this framework can be applied specifically to statisticians and data scientists to improve their collaboration skills and their interdisciplinary impact. We believe that the ASCCR Frame can help organize and stimulate research and teaching in interdisciplinary collaboration and call on individuals and organizations to begin generating evidence regarding its effectiveness.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"265 - 274"},"PeriodicalIF":2.2,"publicationDate":"2018-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1687370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59859533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.1080/10691898.2019.1617089
J. Fiksel, Leah Jager, Johanna S. Hardin, M. Taub
Abstract Git and GitHub are common tools for keeping track of multiple versions of data analytic content, which allow for more than one person to simultaneously work on a project. GitHub Classroom aims to provide a way for students to work on and submit their assignments via Git and GitHub, giving teachers an opportunity to facilitate the integration of these version control tools into their undergraduate statistics courses. In the Fall 2017 semester, we implemented GitHub Classroom in two educational settings—an introductory computational statistics lab and a more advanced computational statistics course. We found many educational benefits of implementing GitHub Classroom, such as easily providing coding feedback during assignments and making students more confident in their ability to collaborate and use version control tools for future data science work. To encourage and ease the transition into using GitHub Classroom, we provide free and publicly available resources—both for students to begin using Git/GitHub and for teachers to use GitHub Classroom for their own courses.
{"title":"Using GitHub Classroom To Teach Statistics","authors":"J. Fiksel, Leah Jager, Johanna S. Hardin, M. Taub","doi":"10.1080/10691898.2019.1617089","DOIUrl":"https://doi.org/10.1080/10691898.2019.1617089","url":null,"abstract":"Abstract Git and GitHub are common tools for keeping track of multiple versions of data analytic content, which allow for more than one person to simultaneously work on a project. GitHub Classroom aims to provide a way for students to work on and submit their assignments via Git and GitHub, giving teachers an opportunity to facilitate the integration of these version control tools into their undergraduate statistics courses. In the Fall 2017 semester, we implemented GitHub Classroom in two educational settings—an introductory computational statistics lab and a more advanced computational statistics course. We found many educational benefits of implementing GitHub Classroom, such as easily providing coding feedback during assignments and making students more confident in their ability to collaborate and use version control tools for future data science work. To encourage and ease the transition into using GitHub Classroom, we provide free and publicly available resources—both for students to begin using Git/GitHub and for teachers to use GitHub Classroom for their own courses.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"110 - 119"},"PeriodicalIF":2.2,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1617089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48418511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-02DOI: 10.1080/10691898.2018.1506953
John Gabrosek, Len O’Kelly
ABSTRACT This article describes a dataset on pop songs that charted on the Billboard Top 40 and/or at one or more of five radio stations, three in Chicago, Illinois, and two in Grand Rapids, Michigan, from the early 1960s through 1970. The dataset includes 5746 observations and 26 variables. In the body of the paper article, we describe how the cleaned version of the dataset can be used in an introductory or second-level statistics course to investigate questions of race and gender bias and the role of radio consultants in Top 40 radio airplay in the 1960s. The richness of the dataset requires students to think about relationships among multiple variables. In an appendix, we briefly describe how a raw, uncleaned version of the dataset can be used in an R programming course to illustrate data management and data entry error detection.
{"title":"R-E-S-P-E-C-T: The Role of Race, Gender, and Radio Consultants on Radio Airplay in 1960s Chicago, IL and Grand Rapids, MI","authors":"John Gabrosek, Len O’Kelly","doi":"10.1080/10691898.2018.1506953","DOIUrl":"https://doi.org/10.1080/10691898.2018.1506953","url":null,"abstract":"ABSTRACT This article describes a dataset on pop songs that charted on the Billboard Top 40 and/or at one or more of five radio stations, three in Chicago, Illinois, and two in Grand Rapids, Michigan, from the early 1960s through 1970. The dataset includes 5746 observations and 26 variables. In the body of the paper article, we describe how the cleaned version of the dataset can be used in an introductory or second-level statistics course to investigate questions of race and gender bias and the role of radio consultants in Top 40 radio airplay in the 1960s. The richness of the dataset requires students to think about relationships among multiple variables. In an appendix, we briefly describe how a raw, uncleaned version of the dataset can be used in an R programming course to illustrate data management and data entry error detection.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"26 1","pages":"223 - 233"},"PeriodicalIF":2.2,"publicationDate":"2018-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2018.1506953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44817340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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