Literature Index

Displaying 1931 - 1940 of 3326
  • Author(s):
    William M. Duckworth and W. Robert Stephenson
    Year:
    2003
    Abstract:
    Resampling methods in statistics have been around<br>for a long time. Over forty years ago Tukey coined<br>the term jackknife to describe a technique, at-<br>tributed to Quenouille (1949), that could be used to<br>estimate bias and to obtain approximate con dence<br>intervals. About 20 years later, Efron (1979) intro-<br>duced the bootstrap" as a general method for esti-<br>mating the sampling distribution of a statistic based<br>on the observed data. Today the jackknife and the<br>bootstrap, and other resampling methods, are com-<br>mon tools for the professional statistician. In spite of<br>their usefulness, these methods have not gained ac-<br>ceptance in standard statistics courses except at the<br>graduate level. Resampling methods can be made<br>accessible to students at virtually every level. This<br>paper will look at introducing resampling methods<br>into statistics courses for health care professionals.<br>We will present examples of course work that could<br>be included in such courses. These examples will<br>include motivation for resampling methods. Health<br>care data will be used to illustrate the methods. We<br>will discuss software options for those wishing to in-<br>clude resampling methods in statistics courses.
  • Author(s):
    Cristie, D.
    Editors:
    Goodall, G.
    Year:
    2004
    Abstract:
    Standard Microsoft Excel functions and the Excel Data Table facility are used in randomization applications using resampling with and without replacement.
  • Author(s):
    Boomsma, A., &amp; Molenaar, I. W.
    Year:
    1991
    Abstract:
    This article describes our positive and negative experiences with the RS program and with the Simon and Bruce views on teaching.
  • Author(s):
    Alan T. Arnholt
    Year:
    2007
    Abstract:
    This article shows how R can be used to perform resampling with and without replacement.
  • Author(s):
    Simon, J. L., &amp; Bruce, P.
    Year:
    1991
    Abstract:
    This article discusses the use of data to "simulate" sampling from a population. The authors claim that their approach, termed "resampling", offers a powerful heuristic for solving statistical problems.
  • Author(s):
    Manfred Borovcnik &amp; Ramesh Kapadia
    Year:
    2009
    Abstract:
    In the topic study group on probability at ICME 11 a variety of ideas on probability education<br>were presented. Some of the papers have been developed further by the driving ideas of interactivity and use<br>of the potential of electronic publishing. As often happens, the medium of research influences the results and<br>thus - not surprisingly - the research change its character during this process. This paper provides a summary<br>of the main threads of research in probability education across the world and the result of an experiment in<br>electronic communication. For convenience of international readers, abstracts in Spanish and German have<br>been supplied, as well as hints for navigation to linked electronic materials.
  • Author(s):
    Myers, J. L., &amp; Well, A. D.
    Year:
    2003
    Abstract:
    Intended both as a textbook for students and as a resource for researchers, this book emphasizes the statistical concepts and assumptions necessary to describe and make inferences about real data. Throughout the book the authors encourage the reader to plot and examine their data, find confidence intervals, use power analyses to determine sample size, and calculate effect sizes. The goal is to ensure the reader understands: the underlying logic and assumptions of the analysis and what it tells them; the limitations of the analysis; and the possible consequences of violating assumptions. The authors adopt a "bottom-up" approach--a simpler, less abstract discussion of analysis of variance is presented prior to developing the more general model. A concern for alternatives to standard analyses allows for the integration of non-parametric techniques into relevant design chapters, rather than in a single, isolated chapter. This organization allows for the comparison of the pros and cons of alternative procedures within the research context to which they apply. Basic concepts such as sampling distributions, expected mean squares, design efficiency and statistical models are emphasized throughout.
  • Author(s):
    Shaughnessy, J. M.
    Editors:
    Grouws, D.
    Year:
    1992
    Abstract:
    Probability and statistics (stochastics) are viewed as necessary for all students no matter their ambitions. However, there are barriers to the effective teaching of both stochastics and problem solving: 1) getting stochastics into the mainstream of the mathematical science school curriculum; 2) enhancing teachers' background and conceptions of probability and statistics; 3) confronting students' and teachers' beliefs about probability and statistics. Psychologists and mathematics educators should work collaboratively to diminish misconceptions. Doing so combines the roles of observer, describer, and intervener. Research in stochastics suggests that heuristics that are used intuitively by learners impede the conceptual understanding of concepts such as sampling. This paper reviews the research on judgemental heuristics and biases, conditional probability and independence (i.e., causal schemes), decision schema (i.e., outcome approach), and the mean. Learners have difficulties in these areas, however, evidence is contradictory as to whether training in stochastics improves performance and decreases misconceptions. The conclusion emerging from this research is that probability concepts can and should be introduced into the school at an early age. Instruction that is designed to confront misconceptions should encourage students to test whether their beliefs coincide with those of others, whether they are consistent with their own beliefs about other related things, and whether their beliefs are born out with empirical evidence. Computers can be used to provide both an exploratory and representational aspect of the discipline. The role of teachers in this type of environment and the issue of whether stu- dents should use artificial or real data sets should be considered.
    Location:
  • Author(s):
    Jones, G. A., Langrall, C. W., &amp; Mooney, E. S.
    Editors:
    Grouws, D. A.
    Year:
    2007
    Abstract:
    A comprehensive review of research.
  • Author(s):
    Pfannkuch, M., Watson, J.
    Editors:
    Perry, B., Anthony, G., Diezmann, C.
    Year:
    2004
    Abstract:
    The research reported in this chapter spans students in preparatory grades, undergraduate statistics students, preservice teachers, and practising statisticans. The chapter starts with theoretical frameworks that have been developed by Australasian researchers, then focuses on reserach involving students' reasoning and thinking within the statistics discipline, related to probability, variation, sampling, and data representation and interpretation, rounding off with a section on improving teaching and the curriculum. The conclusion addresses reserach areas for future development, as well as areas that should continue to receive attention.

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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