Literature Index

Displaying 3211 - 3220 of 3326

  • Visualizing Multiple Regression

    Author(s):
    Ip, E. H. S.
    Year:
    2001
    Abstract:
    Several examples are presented to demonstrate how Venn diagramming can be used to help students visualize multiple regression concepts such as the coefficient of determination, the multiple partial correlation, and the Type I and Type II sums of squares. In addition, it is suggested that Venn diagramming can aid in the interpretation of a measure of variable importance obtained by average stepwise selection. Finally, we report findings of an experiment that compared outcomes of two instructional methods for multiple regression, one using Venn diagrams and one not.
    Location:
    http://www.amstat.org/publications/jse/v9n1/ip.html

  • Visualizing statistical relationships

    Author(s):
    Rubin, A.
    Year:
    2002
    Abstract:
    Two recent developments in statistical education provide the opportunity for significant advances in helping non-statisician judge statistical claims.<br>(1) Research in statistical thinking has begun to yield models of people's conceptions that are detailed enough to have practical, pedagogical implications.<br>(2) Powerful new software tools designed explicitly for statistical education provide new visualizations with enormous potential for making statistical thinking accessible, for the first time, to the wide range of people who need to use it.<br>While these two developments are exciting in and of themselves, a collaboration between researchers and software designers would accelerate the development of both research and software in important ways. Currently both researchers and developers benefit in limited ways from each others' expertise and ongoing work, but taking full advantage of their respective contributions requires an explicit and organized effort with careful planning to achieve the benefits to each. We propose to create a paradignmatic illustration of such a collaboration, one which will have an impact on both software developers and researchers and, ultimately, on the productivity of the statistical education field.

  • Water Quality Statistics.

    Author(s):
    Hodgson, T., Andersen, L., Robison-Cox, J., &amp; Jones, C.
    Editors:
    Goodall, G.
    Year:
    2004
    Abstract:
    Water quality experiments, especially the use of macroinvertebrates as indicators of water quality, offer an ideal context for connecting statistics and science. In the STAR program for secondary students and teachers, water quality experiments were also used as a context for teaching statistics. In this article, we trace one activity that uses virtual streams and repeated sampling to develop the notion of a hypothesis test for one proportion.

  • Ways to incorporate innovative practices in an applied regression analysis course

    Author(s):
    Halvorsen, K. T., McKenzie Jr., J. D., &amp; Sullivan, M. M.
    Year:
    2001
    Abstract:
    The authors prepared a paper that described an example of a second course in applied regression analysis as part of the ASA Undergraduate Statistics Education Initiative (USEI) Symposium. They recommended that such a course include many practices that are not commonly integrated in a typical applied statistics course. Here the authors will give examples of such practices that they have used successfully (and can be effectively used in any introductory applied statistics course). Because data analysis must be the central theme of the course, examples of how the instructor and students can obtain interesting, real-world data will be given. These include novel activities to collect data in class, as well as web and text resources for data. The USEI paper strongly recommended that students should experience the entire data collection and analysis process. The USEI paper emphasized that active learning must be included in any such course. Activities that promote active learning such as the use of short talks to introduce concepts followed by class discussion and student presentations of examples will be given. Appropriate technology is an indispensable part of such a course. At a minimum this means that the course has suitable computational and conceptual software.

  • We can still learn about probability by rolling dice and tossing coins

    Author(s):
    Dunn, P. K.
    Editors:
    Goodall, G.
    Year:
    2005
    Abstract:
    Rolling dice and tossing coins can still be used to teach probability even if students know (or think they know) what happens in these experiments. This article considers many simple variations of these experiments which are interesting, potentially enjoyable and challenging. Using these variations can cause students (and teachers) to think again about the statistical issues involved - and learn in the process.

  • We must preach what is practiced

    Author(s):
    Ehrenberg, A. S. C.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., &amp; Constable, G. M.
    Year:
    1983
    Abstract:
    In teaching non-specialists, we need to cover the procedures they will come across in their other studies and subsequent work. Since many courses and introductory texts do not do so, students are often disillusioned with them and regard such courses as irrelevant. I now describe some topics which we often teach unnecessarily (or with the wrong emphasis) and some which we mostly do not teach at all even though they are often needed. They range from deep matters like statistical inference or causation to more hum-drum ( but important) ones like means, medians and modes.

  • We must preach what is practised: A radical review of statistical teaching

    Author(s):
    Ehrenberg, A. S. C.
    Year:
    1976
    Abstract:
    This paper discusses the complaints of statistical teaching.

  • We need both exploratory and confirmatory

    Author(s):
    Tukey, J. W.
    Year:
    1980
    Abstract:
    We often forget how science and engineering function. Ideas come from previous exploration more often than from lightning strokes. Important questions can demand the most careful planning for confirmatory analysis. Broad general inquiries are also important. Finding the question is often more important than finding the answer. Exploratory data analysis is an attitude, a flexibility, and a reliance on display, NOT a bundle of techniques, and should be so taught. Confirmatory data analysis, by contrast, is easier to teach and easier to computerize. We need to teach both; to think about science and engineering more broadly; to be prepared to randomize and avoid multiplicity.

  • WEAVING ASSESSMENT FOR STUDENT LEARNING IN PROBABILISTIC REASONING AT THE INTRODUCTORY TERTIARY LEVEL

    Author(s):
    MACGILLIVRAY, Helen
    Year:
    2007
    Abstract:
    For any course in a student's degree program, the assessment should be part of an integrated assessment and learning package, with the components of the package combining to meet the learning objectives in a steady development of skills and operational knowledge that take account of the students' various prior and future learnings. This paper considers such a package for an introductory course in probability and distributional modelling, including its objectives with reference to the nature of statistical thinking in probabilistic and distributional modelling, and general assessment principles. A new component of assessment to strengthen the problem-solving environment and to better address some of the objectives is described, together with student and tutor feedback and student data.
    Location:
    http://www.swinburne.edu.au/lss/statistics/

  • Web Application Teaching Tools for Statistics Using R and Shiny

    Author(s):
    DOI, JIMMY; POTTER, GAIL; WONG, JIMMY; ALCARAZ, IRVIN; CHI, PETER
    Year:
    2016
    Abstract:
    Technology plays a critical role in supporting statistics education, and student comprehension is improved when simulations accompanied by dynamic visualizations are employed. Many web-based teaching tool applets programmed in Java/Javascript are publicly available (e.g., www.rossmanchance.com, www.socr.ucla.edu). These provide a user-friendly interface which is accessible and appealing to students in introductory statistics courses. However, not all statistics educators are fluent in Java/Javascript and may not be able to tailor these apps or develop their own. Shiny, a web application framework for R created by RStudio, facilitates applet development for educators who are familiar with R. We illustrate the utility, convenience, and versatility of Shiny through our collection of 17 freely available apps covering a range of topics and levels (found at www.statistics.calpoly.edu/shiny). Our Shiny source code is publicly available so that anyone may tailor our apps as desired. We provide feedback on how our apps have been used in statistics classes including some challenges that were encountered. We also discuss feasibility on building, launching, and deploying Shiny apps. A brief tutorial on installing and using Shiny is provided in the appendix. Some teaching materials based on our Shiny apps are also included in the appendix.
    Location:
    http://escholarship.org/uc/item/00d4q8cp#

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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

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