Literature Index

Displaying 2681 - 2690 of 3326
  • Author(s):
    Jyoti P. Shiwalkar, M.N. Deshpande
    Year:
    2009
    Abstract:
    We find the correlation of two jointly distributed random variables connected with a coin tossing experiment. The marginal distributions are binomial and negative binomial.
  • Author(s):
    Zhu, M., & Lu, A. Y.
    Year:
    2004
    Abstract:
    In Bayesian statistics, the choice of the prior distribution is often controversial. Different rules for selecting priors have been suggested in the literature, which, sometimes, produce priors that are difficult for the students to understand intuitively. In this article, we use a simple heuristic to illustrate to the students the rather counter-intuitive fact that flat priors are not necessarily non-informative; and non-informative priors are not necessarily flat.
  • Author(s):
    Zieffler, Andrew; Isaak, Rebekah; Garfield, Joan
    Year:
    2013
    Abstract:
    In the past several decades, the statistics textbook has evolved to include a variety of ancillary materials intended to supplement students’ learning and assist the teacher (e.g., workbooks, study guides, audio program, test banks, PowerPoint slides, links to applets and websites, etc.). Given the capabilities of modern technology and the need for change in content and pedagogy in the introductory statistics course, a new vision of a textbook is offered, one that exploits new technology, provides modern content, and is a more integral part of the course. Rather than serving as a supplement to a course, the modern textbook needs to embody the course. An example of such a text in the context of a unique, new introductory statistics course is provided.
  • Author(s):
    Moore, D. S.
    Year:
    1995
    Abstract:
    Remarks on receiving the MAA's 1994 Award for Distinguished College or University Teaching of Mathematics, San Francisco, California, January 1995. MAA Focus 15 (1995) Number 2, 5-8
  • Author(s):
    Freudenthal, H.
    Year:
    1974
    Abstract:
    Not long ago probability was investigated, taught, and published in the way sheep are counted, distances, time and money are accounted for, and physics is pursued, that is tacitly supposing that everybody - investigators, teachers, students, authors and readers - knew what dice, heads and tails, stakes, chances, events, dependency and independency are. There are didactical reasons to believe that at school level probability cannot be taught in any other way, but at any account it may be taken for granted that intuitional knowledge about such tools and concepts is indispensable whenever probability ought to be related to reality.
  • Author(s):
    Charles Opolot-Okurut, Patrick Opyene-Eluk and Margaret Mwanamoiza
    Year:
    2008
    Abstract:
    This paper describes the current state of teaching statistics in Ugandan schools at different levels. Different emphasis is placed on teaching statistics at primary, secondary and tertiary levels. Official documents on curricula and examination make explicit statements on what statistical ideas and techniques are to be taught in schools and suggest useful skills and capabilities that school graduates should acquire, but little of the qualities are visible on the ground. There is little emphasis on the application of these techniques in the context of real life problems. Various challenges on the teaching of mathematics and statistics in schools and the school-university transition are identified, which include the curricula, the teaching force, and the nature of the students and the shortage of teaching resources. These challenges maybe addressed through synchronising students with varied mathematics school backgrounds in their study of statistics, policy adjustments and continuous professional development.
  • Author(s):
    Finzer, William
    Year:
    2013
    Abstract:
    The need for people fluent in working with data is growing rapidly and enormously, but U.S. K–12 education does not provide meaningful learning experiences designed to develop understanding of data science concepts or a fluency with data science skills. Data science is inherently inter-disciplinary, so it makes sense to integrate it with existing content areas, but difficulties abound. Consideration of the work involved in doing data science and the habits of mind that lie behind it leads to a way of thinking about integrating data science with mathematics and science. Examples drawn from current activity development in the Data Games project shed some light on what technology-based, data-driven might be like. The project’s ongoing research on learners’ conceptions of organizing data and the relevance to data science education is explained.
  • Author(s):
    Preece, D. A.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., & Constable, G. M.
    Year:
    1983
    Abstract:
    Basic definitions are given. Then the subject is divided into six "components", which are discussed in turn: I: Planning, design and layout. II: Management of the experiment or experiments. III: Data recording. IV: Scrutiny and editing of the data. V: Computational analysis. VI: Interpreting and reporting the results. Over-theoretical, over-mathematical teaching of the subject is criticized. The importance of practical considerations is stressed.
  • Author(s):
    Jackson, D., Edwards, B., & Berger, C.
    Year:
    1993
    Abstract:
    Discussion of graphing software for educational purposes focuses on a study in which three versions of an original computer graphing program were used by inner-city high school students to solve scientific data analysis problems. Variations in degrees of flexibility and feedback in the software are explored.
  • Author(s):
    Hagani-Mor, S., & Ben-Zvi, D.
    Editors:
    Y. Yair
    Year:
    2010

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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