Literature Index

Displaying 761 - 770 of 3326
  • Author(s):
    Shaughnessy, J. M., Garfield, J. B., & Greer, B.
    Editors:
    Bishop, A. J., Clements, K., Keitel, C. Kilpatrick, J. & Laborde, C.
    Year:
    1996
    Abstract:
    This paper discusses many different aspects of the concept of data handling, which is described as an holistic approach to dealing with data, involving a frame of mind as well as an environment within which one can explore data, rather than just covering a body of specific statistical content.
  • Author(s):
    Davies, G.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    The National Curriculum for Mathematics recently introduced to all state schools in any UK school curriculum. Help and support is constantly sought by both primary and secondary teachers now attempting to implement this new initiative. In my role as Project Officer: Data handling in the National Curriculum I am attempting to meet these needs. This initiative has given us the opportunity to review how statistics is taught in our schools and to develop new strategies to improve upon existing good practice. The heading given to the statistical content in the National Curriculum is Data Handling, an unfortunate title which implies a passive act. It is my belief that statistics is best taught through active statistical investigation, in real life, practical situations offering meaningful and enjoyable learning experiences. Examples of the materials I have produced were discussed and further references to other recent international curriculum developments made. Some of the implications of the"National Curriculum" on primary teaching, secondary teaching and teacher training (retraining) were also discussed.
  • Author(s):
    Robert Gould
    Year:
    2017
    Abstract:
    Past definitions of statistical literacy should be updated in order to account for the greatly amplified role that data now play in our lives. Experience working with high-school students in an innovative data science curriculum has shown that teaching statistical literacy, augmented by data literacy, can begin early  
  • Author(s):
    Cochran, J. J.
    Year:
    2002
    Abstract:
    The 1969-2000 Major League Baseball Attendance dataset contains Runs Scored, Runs Allowed, Wins, Losses, Number of Games Behind the Division Leader, and Home Game Attendance of each major league franchise for the 1969 through 2000 seasons. Also included for each franchise are its location, league affiliation (National or American), and division affiliation (East, Central, or West). These data have been used in a project-based modeling course to instruct students in basic data management, the use of exploratory data analysis to "clean" data, and construction of regression models. The dataset, which is both cross-sectional and time-series, is of a manageable size and easily understood. Furthermore, it provides a useful, interesting, and realistic classroom example for discussing many important statistical concepts.
  • Author(s):
    Ganesh, S.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Teaching of statistics involves developing and adapting robust procedures for understanding statistical concepts, and for the management and analysis of statistical data. The field of statistics is constantly challenged problems that arise from science, industry and business. Traditionally, the statistics curriculum deals with data often collected to answer specific questions. However, in the modern 'information' age, vast amounts of data are collected, often automatically, with the advent of powerful computers. Data Mining is the process of extracting knowledge from large volumes of data. Since 'computation' plays a major role in this process, computer scientists have a significant claim over the ownership of data mining. Nevertheless, data mining techniques, in general, have a statistical base; and statisticians are beginning to show a significant interest in the area, including offering tertiary courses in 'statistical' data mining.
  • Author(s):
    Konold, C., Higgins, T., Russell, S. J., Khalil, K.
    Year:
    2004
    Abstract:
    Statistical thinking focuses on properties that belong not to individual data values but to the entire aggregate. We analyse students' statements from 3 different sources to explore possible building blocks of the idea of data as aggregate and speculate on how young students go about putting these ideas together. We identify 4 general perspectives that students use in working with data, which in addition to an aggregate perspective include regarding data as pointers, as case values, and as classifiers. Some students seem inclined to view data from one of these 3 alternative perspectives, which then influences the types of questions they ask, the representations they generate or prefer, the interpretations they give to notions such as the average, and the conclusions they draw from the data.
  • Author(s):
    Lock, R. H., & Arnold, J. T.
    Year:
    1993
    Abstract:
    We describe the purpose of the "Datasets and Stories" section of this journal. Guidelines for submitting datasets and articles to this section are discussed. Instructions are provided for retrieving data from the JSE data archives.
  • Author(s):
    Amy S. Nowacki
    Year:
    2013
    Abstract:
    Examples are highly sought by both students and teachers. This is particularly true as many statistical instructors aim to engage their students and increase active participation. While simulated datasets are functional, they lack real perspective and the intricacies of actual data. In order to obtain real datasets, the principal investigator of a study must be willing to share the data. Understanding investigators’ opinions regarding data sharing would thus help elucidate the general lack of data sharing currently exhibited. Presented are the results of a survey designed to gather information regarding the proportion of researchers willing to share their data, conditions, formats, primary motivation, concerns and current availability of data for sharing. With 76% (56/74) responding favorably to the idea of sharing their published data, the creation of a new statistical educational resource was prompted. Thus, additionally described is a web-based dataset repository that can be used as a resource by both educators and students of statistics. This growing repository presents raw data from real medical studies and offers (a) a vignette summarizing the study, research question and study design; (b) a data dictionary with clear documentation of variables and codes; (c) a complete citation for the associated study publication; and (d) a variety of data formats compatible with the majority of statistical packages. The repository went online on 12/18/12 at the URL http://www.lerner.ccf.org/qhs/datasets/.
  • Author(s):
    Forbes, Sharleen Denise
    Year:
    2012
    Abstract:
    This paper reports on the use, in 2011, of some recent data visualizations to both motivate students and assist them to understanding underlying official statistics concepts. Examples of visualisations used in a Masters course in public policy and an applied statistics honours course are presented. These visualizations are free, either on-line or open-source and easy to access. Although they are of aggregates of very large official data sets and so may mask some of the underlying variation they provide students with fun tools to explore the patterns and relationships between variables in the data set, discuss its implications and sometimes lead to new questions and analyses. Geo-visualisations help demonstrate the inter-disciplinary nature of official statistics in the real world. Initial feedback from students in these courses was enthusiastic. The on-going challenge for the teacher is to keep up-to-date in a world of rapidly evolving technology and to see the learning opportunities that it may provide. This paper suggests data visualisation is a valuable teaching resource now and, in the longer term, may have implications not only on how we teach but also on what we teach in statistics.
  • Author(s):
    Wang, Xiaofei; Rush, Cynthia; Horton, Nicholas Jon
    Year:
    2017
    Abstract:
    In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common in recent years. Existing literature has documented multiple success stories of teaching statistics with R, bolstered by the capabilities of R Markdown. In this article, we present an in-class data visualization activity intended to expose students to R and R Markdown during the first week of an introductory statistics class. The activity begins with a brief lecture on exploratory data analysis in R. Students are then placed in small groups tasked with exploring a new dataset to produce three visualizations that describe particular insights that are not immediately obvious from the data. Upon completion, students will have produced a series of univariate and multivariate visualizations on a real dataset and practiced describing them.  

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education