Literature Index

Displaying 2731 - 2740 of 3326
  • Author(s):
    Vardeman, S. B. & Wendelberger, J. R.
    Editors:
    Stephenson, W. R.
    Year:
    2005
    Abstract:
    There is a little-known but very simple generalization of the standard result that for uncorrelated random variables with common mean (mu) and variance (sigma) , the expected value of the sample variance is sigma squared . The generalization justifies the use of the usual standard error of the sample mean in possibly heteroscedastic situations, and motivates elementary estimators in even unbalanced linear random effects models. The latter both provides nontrivial examples and exercises concerning method-of-moments estimation, and also helps "demystify" the whole matter of variance component estimation. This is illustrated in general for the simple one-way context and for a specific unbalanced two-factor hierarchical data structure.
  • Author(s):
    Gallagher, J.
    Editors:
    Goodall, G.
    Year:
    2006
    Abstract:
    This article illustrates that not all statistical software packages are correctly calculating a p-value for the classical F test comparison of two independent Normal variances. This is illustrated with a simple example, and the reasons why are discussed. Eight different software packages are considered.
  • Author(s):
    Finzer, W.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Research on learning and commercial software development competes strongly for a project's scarce resources, and yet they have widely overlapping goals. If they could be made to coexist, their synergy could improve both processes. On the research side, to use software to help understand how students perceive and learn statistical concepts requires a software platform that is stable, easy for students to use, and flexible enough to allow different models to be tried; that is, the research benefits from a smoothly functioning development process. On the development side, there is great need for insight into the learning process to inform the software design, and need for research methods to test whether any given design works with students and improves their statistical understanding.
  • Author(s):
    Harvey Goldstein
    Year:
    2007
    Abstract:
    This article sets out a vision for the general nature of the statistics curriculum in the medium to long term.
  • Author(s):
    Franklin, C. A., & Garfield, J. B.
    Editors:
    Burrill, G. F.
    Year:
    2006
    Abstract:
    This article presents brief summaries (introductions) for the two reports that came from the Guidelines for Instruction and Assessment in Statistical Education (GAISE) initiative of American Statistical Association (ASA). The reports focus on Pre-K-12 and college statistics education respectively.
  • Author(s):
    Denes-Raj, V., Epstein, S., & Cole, J.
    Year:
    1995
    Abstract:
    A well-substantiated, surprising finding is that people judge the occurence of an event of low probability as less likely when its probability is represented by a ratio of small numbers (e.g., 1 in 20) than of larger (e.g., 10 in 200) numbers. The results of three experiments demonstrated that this phenomenon is broadly general and occurs as readily in pre- as in postoutcome judgments. These results support an interpretation in terms of subjective probability, as suggested by the principles of cognitive-experiential self theory, but not as an interpretation in terms of imagining couter-factual alternatives, as proposed by norm theory.
  • Author(s):
    Sheldon, N.
    Editors:
    Teaching Statistics
    Year:
    2004
    Abstract:
    This article defines the generalized mean and shows how it relates to such statistics as the arithmetic, geometric and harmonic means.
  • Author(s):
    Sowey, E. R.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    One often hears that "data are not information, information is not knowledge, knowledge is not wisdom". But what will turn data into information, information into knowledge, and knowledge into wisdom? The first two facets of this question are at the core of every university course in statistics. They provide a motivation for understanding statistical description and statistical inference, respectively. It is the third facet, the getting of wisdom, which adds depth, resilience and realism to that understanding, yet its importance is often underrated in professional statistics programs. Crucial to the getting of wisdom in this context is a competence to argue back to a statistic and to criticise a statistical argument. Imparting this competence should be a vital concern in designing the program syllabus. In this paper I argue that, by adding a little to the syllabus, such a program can also aid the statistician in opening up for his/her client the client's own path to statistical knowledge and wisdom. Such a move constructively addresses an abiding social issue: the need to enhance the level of numeracy in our alarmingly innumerate society.
  • Author(s):
    Edirisooriya, G.
    Editors:
    Goodall, G.
    Year:
    2003
    Abstract:
    This article draws analogies between the activities of statisticians and of chefs. It suggests how these analogies can be used in teaching, both to help understanding of what statistics is about and to increase motivation to learn the subject.
  • Author(s):
    Taffe, J.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    The laws to be introduced share some of the characteristics of two other laws discussed in the paper. They will help to throw light on a very widely-used approach to teaching statistics in tertiary institutions.

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