Literature Index

Displaying 2161 - 2170 of 3326

  • Statistical methods and Maori research

    Author(s):
    Barlow, C.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    I believe that Maori research can benefit immensely through the incorporation of a fuller range of appropriate quantitative methods to enhance the general qualitative approach to research problems adopted in this field. In the first part of this paper I outlined some of the strategies used in investigating problems relating to Maori language and cultural issues and suggested ways of applying statistical methods (where possible) to augment the analyses of the available data. In the second part of the paper I discussed a particular piece of research that I am currently involved in which looks at attitudes of bilingual speakers to spoken Maori. I attempted to demonstrate how the use of factor analysis and bi-plot graphs provide an interesting way of summarising and viewing the data which would not be possible without the use of these statistical techniques.

  • Statistical methods in general insurance.

    Author(s):
    Boland, P. J.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    The Actuarial profession appeals to many with excellent quantitative skills who aspire to "make financial sense of the future." However the road to qualification as an Actuary through the Institute or Faculty of Actuaries in the UK (or the Society of Actuaries or Casualty Actuary Society in the United States) is not an easy one, and a series of very challenging exams must be passed to qualify as a Fellow Actuary. These exams test many skills, and in particular demand a good knowledge of probability and statistics. The main areas of work for actuaries are traditionally life assurance, actuarial consultancy, general insurance, and investment. Although statistical skills are required in all of these areas, they are particularly important in general insurance. In this paper we discuss the basic tools and techniques in probability and statistics that are essential for an actuary who intends to work in general insurance.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/5G1_BOLA.pdf

  • Statistical modeling to promote students’ aggregate reasoning with sample and sampling.

    Author(s):
    Aridor, K., & Ben-Zvi, D.
    Year:
    2017
    Abstract:
    Helping students develop an aggregate view of data is a key challenge in statistics education. It has been suggested that modeling pedagogy can address this challenge (Lehrer & Schauble, 2004). In this paper we present a case study – part of a UK-Israel research project – that aims to examine how students’ reasoning about modeling of a real phenomenon can support the emergence of aggregate view of data, in the context of making informal statistical inferences. We focus on the emergent reasoning of two fifth-graders (aged 10) involved in statistical data analysis and modeling activities using TinkerPlots2. We describe the students’ articulations of aggregate view of data as they: 1) explore a small sample; 2) plan and construct a model that represents the investigated phenomenon and make predictions about ‘some wider universe’; and 3) generate random samples from this model to examine its representativeness. This paper aims to contribute to the study of models that young students can understand and use to develop their aggregate view of data. Keywords: Exploratory data analysis, informal statistical inference, aggregate view of data, statistical model, statistical modeling. 

  • Statistical modeling to promote students’ aggregate view of data in the context of informal statistical inference

    Author(s):
    Aridor, K., & Ben-Zvi, D.
    Year:
    2015

  • Statistical or quantitative thinking as a fundamental intelligence

    Author(s):
    Ullman, N. R.
    Year:
    1995
    Abstract:
    This article presents an expanded view of statistics both in the topics embodied and in the way people employ it. Statistics is proposed as one of the fundamental human intelligences and, using the verbal intelligence as a model, a quantitative parallel to speaking is introduced. This spoken form of the quantitative language is part of everyone's everyday activities and has been used throughout our lives. Informal statistical principles are described as providing a foundation for the formal statistics traditionally taught and promulgated. Recommendations for beginning a radical change in what we teach are offered.

  • Statistical Power Analysis with Microsoft Excel: Normal Tests for One or Two Means as a Prelude to Using Non-Central Distributions to Calculate Power

    Author(s):
    António Teixeira, Álvaro Rosa and Teresa Calapez
    Year:
    2009
    Abstract:
    This article presents statistical power analysis (SPA) based on the normal distribution using Excel, adopting textbook and SPA approaches. The objective is to present the latter in a comparative way within a framework that is familiar to textbook level readers, as a first step to understand SPA with other distributions. The analysis focuses on the case of the equality of the means of two populations with equal variances for independent samples with the same size.<br><br>This is the situation adopted as case 0 by Cohen (1988), a pioneer in the subject, to develop his set of tables and so, the present article can be seen as an introduction to Cohen's methodology applied to tests based on samples from normal populations. We also discuss how to extend the calculation to cases with other characteristics (cases 1 to 4), similarly to what Cohen proposes, as well as a brief discussion about the advantages and shortcomings of Excel. We teach mainly in the area of business and economics, which determines the scope of our analysis.
    Location:
    http://www.amstat.org/publications/jse/v17n1/teixeira.html

  • Statistical Project Competition for Secondary School Students - A Hong Kong experience

    Author(s):
    Shen, S. M., Li, K. Y., &amp; Lam, K.
    Editors:
    Vere-Jones, D., Carlyle, S., &amp; Dawkins, B. P.
    Year:
    1991
    Abstract:
    Describes work submitted to statistical competitions in Hong Kong.

  • Statistical reasoning in novices

    Author(s):
    Well, A. D., Pollatsek, A., Konold. C. E., &amp; Hardiman, P.
    Year:
    1984
    Abstract:
    There is a growing body of evidence indicating that people often overestimate the similarity between characteristics of random samples and those of the populations from which they are drawn. In the first section of the paper, we review some studies that have attempted to determine whether the basic heuristic employed in thinking about random samples is passive and descriptive or whether it is deducible from a belief in active balancing. In the second section, we discuss the importance of sample size on judgments about the characteristics of random samples.

  • Statistical reasoning learning environment

    Author(s):
    Ben-Zvi, D.
    Year:
    2011
    Abstract:
    This article describes a model for an interactive inquiry-based statistics learning environment that is designed to develop students’ statistical reasoning. This model is called a “Statistical Reasoning Learning Environment” (SRLE) and is built on the socio-constructivist theory of learning and teaching. This model is based on six principles of instructional design: fundamental statistical ideas, motivating real data sets, inquiry- and data-based classroom activities, innovative technological tools, classroom norms, and alternative assessment. Two examples of SRLEs are briefly discussed.

  • Statistical reasoning of middle school children engaged in survey inquiry

    Author(s):
    Nancy C. Lavigne &amp; Susanne P. Lajoie
    Year:
    2007
    Abstract:
    The case study examined two groups of grade 7 students as they engaged in four inquiry phases: posing a question and collecting, analyzing, and representing data. Previous studies reported analyses of statistical reasoning on a single inquiry phase. Our goal was to identify the modes of statistical reasoning displayed during group discussions in all phases as children designed and conducted their own inquiry. A content analysis of audio and video recorded discussions yielded 10 statistical reasoning modes: six relate to Garfield and Gal's [Garfield, J., Gal, I. (1999). Teaching and assessing statistical reasoning. In L. V. Stiff, &amp; F. R. Curcio (Eds.), Developing mathematical reasoning in grades K-12. 1999 Yearbook (pp. 207-219). Reston, VA: National Council of Teachers of Mathematics] statistical reasoning types involved in the collection, analysis, and representation of data and four modes deal with an aspect of inquiry not exclusively focused upon in the literature on statistical reasoning - i.e., the problem-posing phase. Although students' reasoning reflected an incomplete understanding of statistics they serve as building blocks for instruction.
    Location:
    http://www.sciencedirect.com/science?_ob=PublicationURL&amp;_tockey=%23TOC%236753%232007%23999679995%23669870%23FLA%23&amp;_cdi=6753&amp;_pubType=J&amp;_auth=y&amp;_acct=C000050221&amp;_version=1&amp;_urlVersion=0&amp;_userid=10&amp;md5=74edd1964251732d70

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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

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