Literature Index

Displaying 611 - 620 of 3326

  • Combining interactive visualization and statistics to detect patterns in environmental data.

    Author(s):
    Hervé, J.-Y., Liu, Q, Nicholson, M., González, L., & Mather, T.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    One of the biggest challenges statisticians face when working with non-statisticians on applied problems is to be able to effectively communicate the statistical results. In this paper we discuss the use of interactive visualization as a tool to present the relationship between a binary response and a set of explanatory variables. The visualization system we present allows users to "manipulate" directly, dynamically, and interactively their data set. At a first level, this allows to integrate visualization with a classical statistical analysis by providing interactive 3D views of the data set. Beyond its potential use as a straightforward visualization tool, this new system opens up interesting possibilities for exploring data visually, by its better exploitation of the human visual system. The paper presents an example of exploring visual relationships between environmental variables and the presence/absence of Lyme disease in Rhode Island.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/4E4_HERV.pdf

  • Commentary on Wolford, Taylor, and Beck: The conjunction fallacy?

    Author(s):
    Bar-Hillel, M.
    Year:
    1991
    Abstract:
    Although P(A&B|X) can never exceed P(A|X) (the conjunction rule), it is possible for P(X|A&B) to exceed P(X|A). Hence, people who rank A&B as more probably than A are not necessarily violating any normative rule if the ranking is done in terms of the probability of these events to yield an event X. Wolford, Taylor, and Beck (1990) have argue that this indeed is what happens in some problems (e.g. Tversky & Kahneman's (1983) Linda Problem). The claim made here is that the Linda problem is hard to reconcile with this interpretation; that there is little if any evidence that subjects utilize this interpretation; and that in any case, representativeness can account for all Linda problem results.

  • Commentary: A Framework for Supporting Learning and Teaching About Mathematical and Scientific Models

    Author(s):
    Petrosino, A.
    Year:
    2003
    Abstract:
    This article presents a framework for thinking about the use of models and model-based curriculum in the K-12 settings. In doing so, it draws from the word of two colleagues, Leona Schauble and Richard Lehrer, as well as research we conducted together while I was a postdoctoral fellow at the University of Wisconsin and my subsequent work at The University of Texas. This article also proposes that it may be time to look at modeling as a tool that requires both scientific and mathematical reasoning to fully leverage the power of sophisticated thinking by both students and teachers.

  • Comments on the learning in introductory statistics courses.

    Author(s):
    Magalhães, M. N.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    A recurrent problem in teaching is to evaluate what the students will retain from the contents discussed in class. For the students who completed two basic courses on statistics, we applied a questionnaire with a test, 3 months after the end of the course. The test was composed of 50 true-false questions. The results revealed that students could satisfactorily answer the questions directly related with definitions. However, there was no such performance when the questions required additional procedures related.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/C310.pdf

  • Comments on the use of learning trajectories in curriculum development and research

    Author(s):
    Baroody, A. J., Cibulskis, M., Lai, M., Li, X.
    Year:
    2004
    Abstract:
    In this commentary, we first outline several frameworks for analyzing the articles in this issue. Next, we discuss Clements and Sarama's overview and the issue hypothetical learning trajectories (HLTs) in general. We then analyze each of the other contributions. We conclude our commentary by offering a vision of HLTs that includes a key role for "big ideas."

  • Common difficulties with probabilistic reasoning

    Author(s):
    Hope, J. A., & Kelly, I. W.
    Year:
    1983
    Abstract:
    In the past two decades several influential organizations, including the national Council of Supervisors of Mathematics, NACOME, UNESCO, CEEB, and the Cambridge Conference on School Mathematics, have acknowledged the role that probability and statistics play in our society. Consequently, each has recommended that probability and statistics be included as part of the modern mathematics curriculum. Probabilistic reasoning may not be an easily acquired skill for most students, however. Several recent studies have reported that even after instruction, many students have difficulties developing an intuition about the fundamental ideas of probability. Without this intuition they fail miserably when forced to reason about probable events.

  • Community Service-Learning in Statistics: Course Design and Assessment

    Author(s):
    Debra L. Hydorn
    Year:
    2007
    Abstract:
    Service-learning projects are a useful method for students to learn both the practice and value of statistical methods. Effective service learning, however, depends on several factors and can be implemented according to a variety of models. In this article, different models for incorporating service-learning in statistics courses are presented along with example statistics courses. Principles for good service-learning practice will also be presented as a means for assessing the quality of a service-learning course component.
    Location:
    http://www.amstat.org/publications/jse/v15n2/hydorn.html

  • Community-Based Learning: Motivating Encounters With Real-World Statistics

    Author(s):
    Thorme, T. & Root, R.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    We explore the effects of optional community-based projects on students and particularly on motivation and learning in an applied statistics course. We consider how the nature and structure of community-based projects enhance student learning in a constructivist classroom. We critically assess the intellectual challenges of a community-based project and the nature of the statistical problems that arise. We review students' evaluations and our own estimation of their ability to learn from experience and from the community.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/3a3_root.pdf

  • Community-oriented projects in calculus-based statistics courses

    Author(s):
    Arkady Shemyakin & Brenda Tiefenbruck
    Year:
    2009
    Abstract:
    The purpose of the paper is to share the ten years of experience of implementing small group community-oriented projects as a learning tool in three different calculus-based statistics courses at the University of St. Thomas in Minnesota. The content of the courses, specifics of group organization, authentic data issues, and the products of project work are discussed. Four examples of student projects are considered. Two of these projects were carried out for external community partners and two for community partners found inside the university. These examples serve to illustrate community involvement and community-oriented learning experienced in the statistics classroom.
    Location:
    http://iospress.metapress.com/content/b037h3gvln00lh6w/?p=7ff5a70aa377491da6c98cf00d6550d1&pi=3

  • Comparative study on teaching statistics in mathematics and non-mathematics colleges in Santa Fe (Argentina).

    Author(s):
    Tauber, L.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    We seek to carry out a comparison of the diverse characteristics that are presented in the teaching and learning of Statistics in different university careers. We will compare the Statistics curriculum that are developed in each degree program, the objectives that are pursued, the different didactic methods that are used in each case, the applications used, the work with computer and other simulation instruments and the types of problems that receive larger emphasis in each discipline. From the student's point of view, we are carrying out an investigation that is in a first exploratory phase which will help us to describe the prior knowledge that the students bring when beginning their first statistic course, in connection with the intuitive interpretation of simple statistical graphics, such as graphics of bars and sectors, and interpretation of charts of frequencies.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/9A1_TAUB.pdf

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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