Literature Index

Displaying 2461 - 2470 of 3326
  • Author(s):
    Bangdiwala, S. I., Amarillo, M. L., Ughade, S., & Rodríguez, M. N., Komoltri, C. & Cumsille-Garib, J. F.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Teaching future applied statisticians requires the teaching of consultation skills since the student must learn to interact with research workers, learn to abstract the statistical aspects of substantive problems, to provide appropriate technical assistance, and to effectively communicate statistical results. The approach of the Department of Biostatistics at the University of North Carolina at Chapel Hill is to provide a dual training that includes classroom work, but also involves a 'real' practicum. The objective of this paper is to present various modalities of the experience in training future consultants. These are evaluated by former students of the Department of Biostatistics that are currently involved in consultation and in training in their respective countries. The success of the training is seen through subsequent consultations in worldwide settings.
  • Author(s):
    O'Muircheartaigh, I. G.
    Editors:
    Davidson, R., & Swift, J.
    Year:
    1986
    Abstract:
    The present paper is essentially a preliminary report on the author's experience of teaching a course in Data Analysis to students at the Naval Postgraduate School, Monterey, California. The main emphasis in this paper is on the use of graphics packages as a teaching tool, and, in particular, on how these packages can assist the student (and the teacher) to achieve greater insight into both the data analytic and methodological aspects of our discipline.
  • Author(s):
    Greer, B., & Ritson, R.
    Year:
    1993
    Abstract:
    This publication provides a historical background to the development of statistics and probability and an analysis of current research into the pedagogical issues associated with this area of mathematics. The final section contains information about resources of all types.
  • Author(s):
    McNeil, D.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    Our approach to teaching this topic is described, and some implications for the future undergraduate curriculum in statistics are discussed.
  • Author(s):
    Martignon, L. & Wassner, C.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Our comparative studies investigate the influence of different representations (i.e. formulas or graphical models and numeric formats) on the understanding of "big ideas" in stochastics (e.g. characteristics of probability, conditional probability, distribution, significance). We know from previous work (e.g. Sedlmeier & Gigerenzer, 2001) that special tree-representations combined with frequency-formats increase the performance in understanding dramatically. Another aspect of the experiments is the utility of different presenting-modes (e.g. static vs. dynamic, imitation vs. learning by doing). The pupils of age 15-19 receive a computer-based training with different representations resp. modes on basic probability tasks. The effects of the training are measured by subsequent tests. Thus we obtain insight, if they succeed easily in using the learned representations and if they benefit from it. The first results support the assumption that groups of pupils trained with frequency-representations have a better understanding of key-problems in stochastics.
  • Author(s):
    Bordley, R. F.
    Year:
    2001
    Abstract:
    There has been much concern about making the curriculum for engineering statistics more relevant to the needs of industry. One proposed solution is to include decision risk analysis in the curriculum. However, the current coverage of decision risk analysis in statistics textbooks is either nonexistent or very introductory. In part, this reflects the fact that decision risk analysis, as currently taught, relies on the complex notion of a utility function.<br>Recent research in decision theory suggests a way of comprehensively and rigorously discussing decision theory without using utility functions. In this new approach, the decision risk analysis course focuses on making decisions so as to maximize the probability of meeting a target. This allows decision theory to be integrated with reliability theory. This course would be more comprehensive than the conventional introductory treatment of decision theory and no more difficult to teach. In addition, integrating decision theory with reliability theory facilitates its incorporation in curricula that currently exclude decision theory.
  • Author(s):
    Collis, B.
    Year:
    1983
    Abstract:
    There is no doubt that statistics should be an important part of the secondary mathematics curriculum. A single classroom microcomputer can be valuable for work in both descriptive and inferential statistics. This article presents a framework for integrating a microcomputer into a statistics unit and includes descriptions of some programs suitable for the Apple microcomputer and ideas for lessons. Three functions that a microcomputer can perform within a statistics unit are illustrated: the easy generation of attractive graphs; the illustration of concepts; and the performing of tedious calculations. These three functions will frequently overlap within a specific program. Simulations are important but will not be discussed here.
  • Author(s):
    Bhagwandas, V.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., &amp; Constable, G. M.
    Year:
    1983
    Abstract:
    This paper presents the status of Indian universities with respect to teaching and research in Design of Experiments. In addition, some suggestions to improve the existing courses and research facilities are included.
  • Author(s):
    Stirling, W. D.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    In this paper we shall deal with the teaching of CDA in courses.
  • Author(s):
    Sahai, H., &amp; Reesal, M. R.
    Year:
    1992
    Abstract:
    This article illustrates some common applications of probability and statistics in the field of epidemiology as they may presented to an undergraduate class in probability and statistics.

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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