A series of papers at this conference have built on the African Data Initiative (ADI) which was started to improve statistical literacy and understanding. One immediate deliverables of this initiative has been new statistical software which is free, easy-to-use, open source and which encourages good statistical practice. Here we show how this software together with other open educational resources can be used to improve statistics teaching. This is demonstrated using an undergraduate course which was offered to about 300 students at Maseno University, Kenya. The resources include those from an e-learning course, called e-SMS (Statistics Made Simple) and an electronic statistics book called Computer Assisted Statistics Textbook (CAST). The course also made extensive use of Moodle to enable a “blended” approach to be undertaken.
{"title":"Open educational resources for statistics teaching","authors":"J. Musyoka, R. Stern, D. Stern","doi":"10.52041/srap.17605","DOIUrl":"https://doi.org/10.52041/srap.17605","url":null,"abstract":"A series of papers at this conference have built on the African Data Initiative (ADI) which was started to improve statistical literacy and understanding. One immediate deliverables of this initiative has been new statistical software which is free, easy-to-use, open source and which encourages good statistical practice. Here we show how this software together with other open educational resources can be used to improve statistics teaching. This is demonstrated using an undergraduate course which was offered to about 300 students at Maseno University, Kenya. The resources include those from an e-learning course, called e-SMS (Statistics Made Simple) and an electronic statistics book called Computer Assisted Statistics Textbook (CAST). The course also made extensive use of Moodle to enable a “blended” approach to be undertaken.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125975974","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}
In the first session of the school year, the researcher faced this question by a student: 'why can't we learn mathematics and statistics while we can succeed in other subject matters with little amount of effort?' Of course, facing such a question was not beyond expectation; many students in Iran and other countries may ask such a question. Skamp (1976), Serpinka (1998), Freudental (1982) support this point. Based on his experience, the researcher considered Skamp's theoretical framework on schemata, and learning in structuralist viewpoint as suitable to account for this question. The student's role is regarded as vital. The research findings were put into practice in the class by the researcher, which yielded satisfactory results. The present study first gives a discussion on the students' meaningful theoretical infrastructure of the mathematical knowledge, then presents the research results.
{"title":"We learn statistics and mathematics hardly","authors":"M. Nekoufar","doi":"10.52041/srap.17305","DOIUrl":"https://doi.org/10.52041/srap.17305","url":null,"abstract":"In the first session of the school year, the researcher faced this question by a student: 'why can't we learn mathematics and statistics while we can succeed in other subject matters with little amount of effort?' Of course, facing such a question was not beyond expectation; many students in Iran and other countries may ask such a question. Skamp (1976), Serpinka (1998), Freudental (1982) support this point. Based on his experience, the researcher considered Skamp's theoretical framework on schemata, and learning in structuralist viewpoint as suitable to account for this question. The student's role is regarded as vital. The research findings were put into practice in the class by the researcher, which yielded satisfactory results. The present study first gives a discussion on the students' meaningful theoretical infrastructure of the mathematical knowledge, then presents the research results.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124974851","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}
In the centrally planned economy the main function of official statistics was monitoring of plans’ execution. Hence, official statisticians had to be experts in economics and bookkeeping, like tax inspectors. Russian statistical education was oriented mostly to official needs and statistics was included in the same educational group as economics. Currently, professional requirements for statisticians have changed. Official statistics lost its control function, and the old reporting system is being replaced by sample surveys that are less onerous for respondents and also less expensive. The statistical agency needs more professionals in survey methodology, as well as statistical managers and mathematicians. In 2015, the new professional standard "statistician" was accepted in Russia in line with ISCO 2008. It consists of set of competences for professionals in data collection, processing, analysis and methodology in any field of activity, including business, finance, science, medicine. In 2016, the educational classification of statistics was changed to the same group as mathematics. A new educational standard for statistics was also accepted. This presentation describes part of the reconstructive process.
{"title":"Reformatting statistical education in Russia: changes in classifications, standards, and programs","authors":"A. Ponomarenko","doi":"10.52041/srap.17314","DOIUrl":"https://doi.org/10.52041/srap.17314","url":null,"abstract":"In the centrally planned economy the main function of official statistics was monitoring of plans’ execution. Hence, official statisticians had to be experts in economics and bookkeeping, like tax inspectors. Russian statistical education was oriented mostly to official needs and statistics was included in the same educational group as economics. Currently, professional requirements for statisticians have changed. Official statistics lost its control function, and the old reporting system is being replaced by sample surveys that are less onerous for respondents and also less expensive. The statistical agency needs more professionals in survey methodology, as well as statistical managers and mathematicians. In 2015, the new professional standard \"statistician\" was accepted in Russia in line with ISCO 2008. It consists of set of competences for professionals in data collection, processing, analysis and methodology in any field of activity, including business, finance, science, medicine. In 2016, the educational classification of statistics was changed to the same group as mathematics. A new educational standard for statistics was also accepted. This presentation describes part of the reconstructive process.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132060419","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}
Susanne Podworny, Daniel Frischemeier, Rolf Biehler
We designed and developed a statistics course “Data & chance for primary school” that aims at developing content knowledge, pedagogical content knowledge, and technological knowledge of preservice primary school teachers. The course consists of weekly lectures where the content and the technological knowledge components are developed and of a weekly accompanying small- group seminar. The course is designed with statistical reasoning learning environment principles; interface tasks that bridge content knowledge and pedagogical content knowledge play a fundamental role in the course. Three topics are taught: data analysis, combinatorics, and introduction into probability via stochastic simulations. The first results—from online surveys before and after the course (n=189), evaluation of participants’ written homework assignments, and a written test administered after the course—show that statistical thinking of our preservice teachers improves over time and that they show more positive attitudes towards statistics after having attended the course.
我们设计并开发了一门以培养小学职前教师的内容知识、教学内容知识和技术知识为目标的统计学课程“Data & chance for primary school”。该课程包括每周的讲座，其中内容和技术知识组成部分的发展和每周伴随的小组研讨会。课程设计遵循统计推理学习环境原则;连接内容知识和教学内容知识的界面任务在课程中起着重要作用。教授三个主题:数据分析、组合学和通过随机模拟介绍概率。第一个结果——来自课程前后的在线调查(n=189)，对参与者书面作业的评估，以及课程后进行的笔试——表明，我们的职前教师的统计思维随着时间的推移而提高，他们在参加课程后对统计表现出更积极的态度。
{"title":"Design, realization and evaluation of a statistics course for preservice teachers for primary school in Germany","authors":"Susanne Podworny, Daniel Frischemeier, Rolf Biehler","doi":"10.52041/srap.17309","DOIUrl":"https://doi.org/10.52041/srap.17309","url":null,"abstract":"We designed and developed a statistics course “Data & chance for primary school” that aims at developing content knowledge, pedagogical content knowledge, and technological knowledge of preservice primary school teachers. The course consists of weekly lectures where the content and the technological knowledge components are developed and of a weekly accompanying small- group seminar. The course is designed with statistical reasoning learning environment principles; interface tasks that bridge content knowledge and pedagogical content knowledge play a fundamental role in the course. Three topics are taught: data analysis, combinatorics, and introduction into probability via stochastic simulations. The first results—from online surveys before and after the course (n=189), evaluation of participants’ written homework assignments, and a written test administered after the course—show that statistical thinking of our preservice teachers improves over time and that they show more positive attitudes towards statistics after having attended the course.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133402769","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}
Statistics Capacity Building has traditionally been associated with building capacity in Official Statistics, i.e. the capability to collect, analyze and disseminate high quality data in a timely manner and analysing the data for effective functioning of government, the economy and society. Statistical Capacity Building in the 21st century encompasses the capability to deliver relevant statistics training for the needs in ALL areas of official statistics, as well as public and private sectors, academia, and research centres. This calls for education systems to deliver effective and updated statistics training across the spectrum, from basic data literacy to high level straining in the statistical sciences. Challenges faced when building statistics capacity across the spectrum are well documented, however in developing countries, these challenges are similar, but often on a larger scale and more critical. The author will give an overview of lessons learnt and experiences in sta- tistics capacity building initiatives in a developing country, at all levels in South Africa (school to PhD), over a period of more than 25 years.
{"title":"Plenary lecture: statistics capacity building in a developing country – experiences, opportunities and challenges","authors":"D. North","doi":"10.52041/srap.17102","DOIUrl":"https://doi.org/10.52041/srap.17102","url":null,"abstract":"Statistics Capacity Building has traditionally been associated with building capacity in Official Statistics, i.e. the capability to collect, analyze and disseminate high quality data in a timely manner and analysing the data for effective functioning of government, the economy and society. Statistical Capacity Building in the 21st century encompasses the capability to deliver relevant statistics training for the needs in ALL areas of official statistics, as well as public and private sectors, academia, and research centres. This calls for education systems to deliver effective and updated statistics training across the spectrum, from basic data literacy to high level straining in the statistical sciences. Challenges faced when building statistics capacity across the spectrum are well documented, however in developing countries, these challenges are similar, but often on a larger scale and more critical.\u2028 The author will give an overview of lessons learnt and experiences in sta- tistics capacity building initiatives in a developing country, at all levels in South Africa (school to PhD), over a period of more than 25 years.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114317861","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}
The present study analyzed the satisfaction level of first-year biology students exposed to two sorts of learning activities while taking an introductory course on quantitative methods. Using a handbook of practices in Quantitative Methods in Biology, first-year Biology students carried out different activities involving data collection, statistical analysis and construction of graphical models. At the end of the term, students were asked to contrast their learning activities with and without the use of virtual learning tools. Most students declared both approaches useful in their learning process, but preferred the production of their own data without the intervention of technology or virtual/computerized tools. The pattern of responses found in the survey, along with the wide array of situations a first-year biology student may well encounter in the future, suggest that learning experiences of biology majors should balance virtualization and empirical work “in the material world”.
{"title":"The power of balancing in a data-rich material world: teaching introductory mathematics and statistics to biology students","authors":"J. Navarro-Alberto, R. C. Barrientos-Medina","doi":"10.52041/srap.17403","DOIUrl":"https://doi.org/10.52041/srap.17403","url":null,"abstract":"The present study analyzed the satisfaction level of first-year biology students exposed to two sorts of learning activities while taking an introductory course on quantitative methods. Using a handbook of practices in Quantitative Methods in Biology, first-year Biology students carried out different activities involving data collection, statistical analysis and construction of graphical models. At the end of the term, students were asked to contrast their learning activities with and without the use of virtual learning tools. Most students declared both approaches useful in their learning process, but preferred the production of their own data without the intervention of technology or virtual/computerized tools. The pattern of responses found in the survey, along with the wide array of situations a first-year biology student may well encounter in the future, suggest that learning experiences of biology majors should balance virtualization and empirical work “in the material world”.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"31 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659053","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}
Some non-academic factors, particularly perceived usefulness, are salient determinants of student success, and engagement in a discipline. This study explored the association between college students’ ratings of the usefulness of an introductory statistics course, their beliefs about where statistics will be most useful, and their intentions to take another statistics course. A cross-sectional study of 106 students was conducted. The mean rating for usefulness was 4.7 (out of 7), with no significant difference by gender and age. Sixty-four percent reported that they would consider taking another statistics course, and that subgroup rated the course as more useful (p = .01). Thirty-five percent reported that statistics would be most useful for graduate school, 32% research, 14% their job, and 19% were undecided. The “undecided” students rated statistics as less useful (p = .001). Instructors should emphasize practical examples of the use of data in real-world problem-solving and decision-making. Qualitative research methods could help to elucidate these findings.
{"title":"Perceived usefulness of the introductory statistics course as a correlate of student engagement in statistics","authors":"R. Hassad","doi":"10.52041/srap.17312","DOIUrl":"https://doi.org/10.52041/srap.17312","url":null,"abstract":"Some non-academic factors, particularly perceived usefulness, are salient determinants of student success, and engagement in a discipline. This study explored the association between college students’ ratings of the usefulness of an introductory statistics course, their beliefs about where statistics will be most useful, and their intentions to take another statistics course. A cross-sectional study of 106 students was conducted. The mean rating for usefulness was 4.7 (out of 7), with no significant difference by gender and age. Sixty-four percent reported that they would consider taking another statistics course, and that subgroup rated the course as more useful (p = .01). Thirty-five percent reported that statistics would be most useful for graduate school, 32% research, 14% their job, and 19% were undecided. The “undecided” students rated statistics as less useful (p = .001). Instructors should emphasize practical examples of the use of data in real-world problem-solving and decision-making. Qualitative research methods could help to elucidate these findings.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117166785","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}
Each year increasing amounts of data are being produced and there are growing trends towards data becoming more accessible, particularly online. Here we present a range of examples where data are conveniently arranged in multiple linked rectangles or data frames. They are often omitted from all but advanced statistics courses. However, they are common in practice, hence their omission leaves graduates poorly prepared for real world problems. The obvious example is a survey that is at multiple levels. Other examples include multiple time series with spatial data, where the spatial information is in a separate data frame; and data sets in a single rectangle (data frame) but where the analyses are on summary data. The statistical software, R-Instat, resulting from the African Data Initiative is designed to make it easy to handle such data.
每年产生的数据量都在增加，而且越来越多的趋势是数据变得更容易获取，特别是在线数据。在这里，我们提供了一系列示例，其中数据方便地安排在多个链接的矩形或数据帧中。除了高级统计课程，它们经常被忽略。然而，它们在实践中很常见，因此它们的遗漏使毕业生对现实世界的问题准备不足。一个明显的例子是一个多层次的调查。其他示例包括具有空间数据的多个时间序列，其中空间信息位于单独的数据框架中;和数据集在一个矩形(数据框架)，但其中的分析是在汇总数据。统计软件R-Instat是由非洲数据倡议组织(African Data Initiative)开发的，旨在简化处理此类数据的工作。
{"title":"Making multilevel data ideas more accessible","authors":"D. Parsons, D. Stern, R. Stern","doi":"10.52041/srap.17202","DOIUrl":"https://doi.org/10.52041/srap.17202","url":null,"abstract":"Each year increasing amounts of data are being produced and there are growing trends towards data becoming more accessible, particularly online. Here we present a range of examples where data are conveniently arranged in multiple linked rectangles or data frames. They are often omitted from all but advanced statistics courses. However, they are common in practice, hence their omission leaves graduates poorly prepared for real world problems. The obvious example is a survey that is at multiple levels. Other examples include multiple time series with spatial data, where the spatial information is in a separate data frame; and data sets in a single rectangle (data frame) but where the analyses are on summary data. The statistical software, R-Instat, resulting from the African Data Initiative is designed to make it easy to handle such data.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123239547","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}
In the developing world, there is a need to create awareness in civil servants regarding the importance of carrying out in-depth analyses of official data, in order to ensure policy-formulation aimed at improving the lives of the people. This paper reports on an attempt to achieve this at the first level of civil service, through an innovative approach in rendering a course entitled “Quantitative Tools for Decision-Making” to the probationary officers of the 43rd Common Training Program at the Civil Services Academy of Pakistan. The course was wholly based on data pertaining to the 2011 Punjab Multiple Indicator Cluster Survey (MICS 2011). Feedback from participants in the four-month-long course provides motivation for recommending the ongoing adoption of this approach at all levels of civil service. Similar strategies may prove to be worthwhile in many other developing countries of the world striving to create statistical awareness in civil servants.
{"title":"Teaching statistics at the civil service academy of Pakistan: an innovative endeavour","authors":"S. Habibullah","doi":"10.52041/srap.17315","DOIUrl":"https://doi.org/10.52041/srap.17315","url":null,"abstract":"In the developing world, there is a need to create awareness in civil servants regarding the importance of carrying out in-depth analyses of official data, in order to ensure policy-formulation aimed at improving the lives of the people. This paper reports on an attempt to achieve this at the first level of civil service, through an innovative approach in rendering a course entitled “Quantitative Tools for Decision-Making” to the probationary officers of the 43rd Common Training Program at the Civil Services Academy of Pakistan. The course was wholly based on data pertaining to the 2011 Punjab Multiple Indicator Cluster Survey (MICS 2011). Feedback from participants in the four-month-long course provides motivation for recommending the ongoing adoption of this approach at all levels of civil service. Similar strategies may prove to be worthwhile in many other developing countries of the world striving to create statistical awareness in civil servants.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128992921","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}
Post-truth refers to a climate where emotional reactions and personal beliefs are used more in shaping opinion and forming the basis for political action than is empirical evidence. Contempt for evidence is socially corrosive. It violates the core values of the statistical community, and poses an existential threat to the idea of evidence-informed decision making. The task of developing resistance to post-truth should be shared amongst everyone involved in statistics education. Here, we explore some possible responses as a community; we need to promote a non- partisan approach to promoting respect for high-quality evidence, and reasoning from evidence. We also need to look hard at our implicit acceptance of an ‘evidence-informed’ world view – when does the statistical and scientific community claim too much? After some scene setting (a brief introduction to the problem, and ideas on solutions from groups such as fact-checkers, social media platform providers, and journalists), we explore ways in which introductory statistics courses could be adapted to incorporate ‘anti-post-truth’ activities, then conclude with some ideas about how statistics educators can contribute to efforts from the broader community that depends on statistical literacy, and that is threatened by post-truth.
{"title":"Statistics education in a post-truth era","authors":"J. Ridgway, J. Nicholson, David Stern","doi":"10.52041/srap.17304","DOIUrl":"https://doi.org/10.52041/srap.17304","url":null,"abstract":"Post-truth refers to a climate where emotional reactions and personal beliefs are used more in shaping opinion and forming the basis for political action than is empirical evidence. Contempt for evidence is socially corrosive. It violates the core values of the statistical community, and poses an existential threat to the idea of evidence-informed decision making. The task of developing resistance to post-truth should be shared amongst everyone involved in statistics education. Here, we explore some possible responses as a community; we need to promote a non- partisan approach to promoting respect for high-quality evidence, and reasoning from evidence. We also need to look hard at our implicit acceptance of an ‘evidence-informed’ world view – when does the statistical and scientific community claim too much? After some scene setting (a brief introduction to the problem, and ideas on solutions from groups such as fact-checkers, social media platform providers, and journalists), we explore ways in which introductory statistics courses could be adapted to incorporate ‘anti-post-truth’ activities, then conclude with some ideas about how statistics educators can contribute to efforts from the broader community that depends on statistical literacy, and that is threatened by post-truth.","PeriodicalId":421900,"journal":{"name":"Teaching Statistics in a Data Rich World IASE Satellite Conference","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121004036","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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